Information processing method and device based on sentiment analysis, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses an information processing method based on sentiment analysis, and the method comprises the steps: collecting data and information which comprise text data, user behavior data and event information; performing natural language processing and sentiment analysis on the text data, and identifying sentiment features of the text data; analyzing the user behavior data to determine a behavior pattern; analyzing the event information to determine an event change trend; generating an emotional state index based on the emotional characteristics, the behavior pattern and the event change trend; monitoring the online platform to obtain user emotion feedback; comprehensively analyzing the emotion state index and the user emotion feedback to generate an emotion analysis result; and generating target output content based on the emotion analysis result. Through combination of emotion analysis, behavior data and event change trends, the emotion state of the user can be captured in real time, decision suggestions are dynamically adjusted, and market or environment changes are effectively coped with.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence technology and financial technology, and in particular to an information processing method, device, equipment and storage medium based on sentiment analysis. Background Art

[0002] With the rapid development of the financial market, smart investment advisory systems have become an important tool in investment management. Traditional smart investment advisory systems rely on historical data, algorithmic models and big data analysis to provide investors with asset allocation, risk assessment and other services through analysis of market fundamentals and technical data. However, such systems still have certain limitations in terms of the accuracy and personalization of investment advice.

[0003] First, the core of traditional smart investment advisory systems relies on historical data and fixed algorithmic models, ignoring the dynamic changes in market sentiment and investor sentiment. In the actual investment decision-making process, market sentiment often has a significant impact on the volatility of asset prices. Especially when major events occur in the financial market or the market fluctuates violently, investors' emotional reactions may lead to irrational decisions, and traditional smart investment advisory systems fail to fully consider this factor, resulting in a lack of timeliness and flexibility in responding to rapid market changes.

[0004] Secondly, traditional smart investment advisory systems pay little attention to investor emotion management. Investor emotions are an important factor affecting their decision-making behavior, but traditional systems usually only rely on historical data and provide standardized investment advice based on fixed models, lacking dynamic analysis of individual investor emotion fluctuations. This standardized advice model ignores the personalized needs of investors and is difficult to effectively help investors make reasonable investment decisions when their emotions fluctuate.

[0005] In addition, traditional smart investment advisory systems are inadequate in responding to market changes in real time. Since these systems are mainly based on historical data and static algorithmic models, it is usually difficult to capture the immediate impact of market hot spots or emergencies on market sentiment in a timely manner. Real-time information such as market sentiment, news reports, and social media comments have an important impact on market sentiment, but traditional investment advisory systems have limited capabilities in analyzing such unstructured data, resulting in a delayed response to market changes and an inability to provide sufficiently flexible and dynamic investment advice. Summary of the invention

[0006] The main purpose of the present invention is to provide an information processing method, device, equipment and storage medium based on sentiment analysis, aiming to solve the technical problems that the prior art fails to combine market sentiment and user sentiment for analysis, reacts slowly to environmental changes, and cannot accurately provide users with satisfactory decisions.

[0007] To achieve the above object, the present invention provides an information processing method based on sentiment analysis, comprising:

[0008] Collect data and information, including text data, user behavior data and event information;

[0009] Performing natural language processing and sentiment analysis on the text data to identify sentiment features of the text data;

[0010] Analyze the user behavior data to determine the user's behavior pattern;

[0011] Analyze the event information to determine the event change trend;

[0012] Determining emotional state indicators based on the emotional characteristics, behavioral patterns, and event change trends;

[0013] Monitor information on online platforms and obtain user sentiment feedback;

[0014] Comprehensively analyzing the emotional state indicators and user emotional feedback to generate emotional analysis results;

[0015] A target output content is generated based on the emotion analysis result to respond to the user's emotional state.

[0016] Furthermore, to achieve the above-mentioned purpose, the present invention provides an information processing device based on sentiment analysis, comprising:

[0017] A data collection module, used to collect data and information, including text data, user behavior data and event information;

[0018] A sentiment analysis module, used to perform natural language processing and sentiment analysis on the text data to identify sentiment features of the text data;

[0019] A user behavior analysis module, used to analyze the user behavior data and determine the user's behavior pattern;

[0020] An event analysis and trend prediction module is used to analyze the event information and determine the event change trend;

[0021] An emotional state analysis module, used to determine emotional state indicators based on the emotional characteristics, behavior patterns and event change trends;

[0022] User emotional feedback collection module, used to monitor the information of online platforms and obtain user emotional feedback;

[0023] The comprehensive emotion analysis module is used to comprehensively analyze the emotion state indicators and user emotion feedback to generate emotion analysis results;

[0024] The output generation module is used to generate target output content for responding to the user's emotional state based on the emotion analysis result.

[0025] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer device, which includes a memory, a processor, and an information processing program based on sentiment analysis stored in the memory and executable on the processor, wherein the information processing program based on sentiment analysis, when executed by the processor, implements the steps of the information processing method based on sentiment analysis as described above.

[0026] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which is stored an information processing program based on sentiment analysis, and when the information processing program based on sentiment analysis is executed by a processor, the steps of the information processing method based on sentiment analysis as described above are implemented.

[0027] Beneficial effects: The present invention relates to the fields of artificial intelligence technology and financial technology, and discloses an information processing method based on sentiment analysis, which collects data and information, including text data, user behavior data and event information; performs natural language processing and sentiment analysis on text data to identify sentiment characteristics of text data; analyzes user behavior data to determine behavior patterns; analyzes event information to determine event change trends; generates sentiment state indicators based on sentiment characteristics, behavior patterns and event change trends; monitors online platforms to obtain user sentiment feedback; comprehensively analyzes sentiment state indicators and user sentiment feedback to generate sentiment analysis results; and generates target output content based on sentiment analysis results. The present invention can capture the user's emotional state in real time, dynamically adjust decision-making suggestions, and effectively respond to market or environmental changes by combining sentiment analysis with behavior data and event change trends. In addition, the accuracy of personalized recommendations is further improved by combining analysis of user sentiment feedback. This not only enhances the system's ability to respond to external changes, but also better meets the user's personalized needs and optimizes the overall decision-making process. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0029] Figure 1 A schematic diagram of an application environment of an information processing method based on sentiment analysis in one embodiment of the present invention;

[0030] Figure 2 This is a flow chart of an embodiment of an information processing method based on sentiment analysis of the present invention;

[0031] Figure 3 A schematic diagram of functional modules of a preferred embodiment of an information processing device based on sentiment analysis of the present invention;

[0032] Figure 4 A schematic diagram of the structure of a computer device in one embodiment of the present invention;

[0033] Figure 5 FIG. 4 is another schematic diagram of the structure of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0034] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0035] The information processing method based on sentiment analysis provided by the embodiment of the present invention can be applied in Figure 1 In an application environment, the user end communicates with the server end through a network. The server end can collect data and information through the user end, and the data and information include text data, user behavior data and event information; perform natural language processing and sentiment analysis on the text data to identify the sentiment characteristics of the text data; analyze user behavior data to determine behavior patterns; analyze event information to determine event change trends; generate emotional state indicators based on sentiment characteristics, behavior patterns and event change trends; monitor online platforms to obtain user emotional feedback; comprehensively analyze emotional state indicators and user emotional feedback to generate emotional analysis results; and generate target output content based on emotional analysis results. The present invention can capture the user's emotional state in real time, dynamically adjust decision suggestions, and effectively respond to market or environmental changes by combining sentiment analysis with behavioral data and event change trends. In addition, the analysis of user emotional feedback further improves the accuracy of personalized recommendations. This not only enhances the system's ability to respond to external changes, but also better meets the user's personalized needs and optimizes the overall decision-making process. Among them, the user end can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The server end can be implemented with an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0036] See also Figure 2 , Figure 2 This is a flow chart of an embodiment of an information processing method based on sentiment analysis provided by the present invention. It should be noted that although a logical sequence is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0037] like Figure 2 As shown, the information processing method based on sentiment analysis proposed by the present invention comprises the following steps:

[0038] S10, collecting data and information, wherein the data and information include text data, user behavior data and event information;

[0039] In this embodiment, different types of data are obtained from multiple sources, including text data, user behavior data, and event information, which together form the basis for analyzing user sentiment and market sentiment.

[0040] Text data refers to information obtained from unstructured text content. These texts can be comments on social media, news articles, user messages, or other publicly published content. Text data is often used for sentiment analysis, which analyzes the sentimental tendencies to understand the emotional state of the market or users. Data scraping tools (such as web crawlers) can be used to obtain text data from social media, news websites, and other channels. Through the API interface, relevant platforms are accessed in real time to collect user comments, forum posts, news articles, and other content. Text data is usually stored in a database in an unstructured form to prepare for subsequent natural language processing (NLP).

[0041] User behavior data covers the user's operation records on the platform or application, including click behavior, page dwell time, browsing path, search history, etc. These data help to depict the user's behavior patterns and reflect their potential emotional changes. Embed tracking scripts in applications or websites to record user behavior operations. Collect data through log analysis or behavior tracking systems (such as Google Analytics or custom tracking tools). Real-time behavior data can be transmitted and processed through stream data processing frameworks (such as Kafka, Flink) to respond to user behavior immediately.

[0042] Event information refers to the impact of external events on the market or user sentiment. It includes major news, policy changes, market emergencies, etc. This type of information is usually triggered by the external environment and can significantly affect market fluctuations and user sentiment. Capture market or industry-related event information through news platforms or policy release platforms. You can access data sources regularly or obtain this information in real time through APIs. Use keyword monitoring systems (such as news monitoring tools) to track specific events or news topics. Once a specific keyword is triggered, the system automatically captures and stores relevant data.

[0043] Develop or integrate web crawlers to capture user comments, news articles, forum posts, etc. on social media platforms. Establish a database to store structured and unstructured text to ensure a clean data source for subsequent analysis steps. Use point-of-sale technology to monitor user operations on the platform in real time, collect user click data, page browsing time and path, etc., generate user behavior logs, and store them in real time in the data warehouse. Establish a regular or real-time data collection mechanism to capture news, policy releases, or industry announcements. Use monitoring systems to respond quickly to market or environmental emergencies to ensure the timeliness of data.

[0044] Example: In the financial field, you can capture the market investors' emotional feedback on a certain stock through social media comments. User behavior data such as buying and selling behavior on the trading platform can reflect the changes in investors' emotions. Event information may involve external factors such as the introduction of new policies and the release of corporate financial reports, which can significantly affect market sentiment. Combining these three types of data, financial platforms can more accurately predict market trends and provide investors with more targeted decision-making suggestions.

[0045] By collecting multiple types of data, we can fully capture the dynamics of users and the market. Text data provides the basis for sentiment analysis, user behavior data helps us understand the actual operation tendencies of users, and event information reflects the impact of the external environment on user emotions. By integrating this data, we can generate a more comprehensive sentiment analysis and provide data support for personalized recommendations and decision-making.

[0046] S20, performing natural language processing and sentiment analysis on the text data to identify sentiment features of the text data;

[0047] In this embodiment, natural language processing technology is used to analyze and process natural language content extracted from text data, mainly including text preprocessing, word segmentation, part-of-speech tagging, entity recognition and other steps, aiming to extract useful information from unstructured text data and perform semantic understanding.

[0048] First, it is necessary to remove noise data in the text (such as HTML tags, special symbols, repeated irrelevant words, etc.) to ensure that subsequent text analysis can be more accurate. Segmenting a continuous text stream into independent words or phrases is an important step before conducting sentiment analysis. In the Chinese environment, word segmentation technology is particularly important, and commonly used tools include Jieba, THULAC, etc. Perform part-of-speech tagging on the segmented words to determine whether each word is a noun, verb, adjective, etc., to facilitate the subsequent recognition of sentiment words. Identify specific entities in the text (such as names, companies, events, places, etc.), which may be crucial to the contextual understanding of sentiment analysis.

[0049] Sentiment analysis is to determine the emotional tendency (such as positive, negative or neutral) of words, sentences or paragraphs in text content. This helps to understand the emotional attitude or emotional intensity expressed in the text.

[0050] Through the sentiment dictionary, we can determine whether the sentiment words in the text are positive, negative or neutral, and calculate the overall sentiment tendency based on the weights of these words. For example, the words "growth" and "profit" can be regarded as positive sentiment, while "loss" and "bankruptcy" are negative sentiment. We can use pre-trained sentiment classification models (such as SVM, Naive Bayes, BERT, etc.) for sentiment analysis. The training data set usually contains text data with sentiment labels, and the model is trained through supervised learning to enable it to have the ability to classify sentiment.

[0051] Based on the distribution and strength of sentiment words in the text, the sentiment analysis system can calculate the sentiment polarity (positive, negative or neutral) and sentiment intensity (the intensity of the sentiment expression). Sentiment polarity is usually +1 (positive), -1 (negative) or 0 (neutral), while sentiment intensity is the strength of the sentiment expression (such as a value between 0 and 1).

[0052] Sentiment features refer to the sentiment information extracted from text, such as sentiment polarity, sentiment intensity, etc. These sentiment features are used to quantify the sentiment expression of the text, thereby helping subsequent decision-making or recommendation systems understand the emotional state expressed in the text.

[0053] Extract sentiment polarity from the vocabulary and context of the text to determine whether it is positive, negative or neutral. Evaluate the intensity of sentiment expression by analyzing the number and distribution of sentiment words. The sentiment intensity can be calculated based on the frequency and weight of sentiment words. Finally, the sentiment polarity and intensity are quantified into numerical values ​​to facilitate subsequent sentiment state analysis and evaluation.

[0054] Text cleaning uses regular expressions and natural language processing libraries (such as NLTK, spaCy, etc.) to remove noise. In Chinese processing, Jieba word segmentation can be used; in English processing, spaCy or NLTK can be used for word segmentation and part-of-speech tagging. Named entities in the text are identified by using pre-trained models (such as NER models) and associated with sentiment analysis results.

[0055] Use existing sentiment lexicon libraries (such as SentiWordNet or Chinese sentiment lexicon) to analyze sentiment words in the text. Build or call a pre-trained sentiment classification model (such as BERT, SVM, etc.), input the text into the model and obtain the sentiment classification results. By analyzing the sentiment words in the text, calculate the sentiment polarity and intensity of the text and quantify them into numerical values.

[0056] Example: In the financial field, sentiment analysis of news comments or user discussions on social media can be used to assess the sentiment attitudes of market investors towards specific stocks or events. For example, when analyzing a company's financial report, natural language processing technology can be used to extract positive words such as "growth, profit" or negative words such as "loss, bankruptcy" from user comments, and combined with sentiment intensity to assess investors' overall sentiment towards the event. This helps financial platforms predict changes in market sentiment and provide more targeted investment advice.

[0057] By performing natural language processing and sentiment analysis on text data, we can effectively extract emotional features from user-generated content and help understand the emotional state of users or the market. Combining sentiment polarity and sentiment intensity, the system can quantify the emotional expression in the text, thereby providing support for personalized recommendations or sentiment management. At the same time, this analysis method can help the system more accurately predict the user's emotional trends and improve the flexibility and real-time nature of decision-making.

[0058] S30, analyzing the user behavior data to determine the user's behavior pattern;

[0059] In this embodiment, user behavior data is obtained through the operation and interaction records of users on applications, websites or other platforms. This data includes user clicks, browsing, purchase behaviors, visit frequency, stay time, etc. These behavior data can reveal the user's operating habits, preferences and behavior patterns through analysis.

[0060] Through embedded tracking codes and behavioral analysis tools (such as Google Analytics, etc.), various user behavior records on the platform can be collected. Including visited pages, dwell time, clicked content, etc. Before analysis, the behavioral data needs to be cleaned, including removing invalid data (such as invalid clicks, robot traffic, etc.), and normalizing the data (such as unifying the time format, normalizing user behavior, etc.). Use time series models to analyze user behavior data, and by analyzing the time characteristics of user behavior, determine the changing trend of user behavior patterns over time.

[0061] Behavior patterns refer to the rules of user behavior extracted by analyzing user behavior data. The identification of behavior patterns is usually based on frequency analysis, behavior sequence mining, and cluster analysis.

[0062] By statistically analyzing the frequency of user behavior, we can identify high-frequency user operations, such as frequent clicks on a certain type of product or high-frequency use of a specific function. Frequency analysis can be used to discover user preferences and predict future behaviors. By analyzing the sequence of user behavior, we can find the logical associations of certain behaviors. For example, users may frequently compare products after searching for a specific product and then make a purchase decision. Common algorithms include sequential pattern mining algorithms (such as the GSP algorithm) or association rule mining (such as the Apriori algorithm). Cluster the behavior data of a large number of users to find the common behavior patterns of similar user groups. Common clustering algorithms include K-means, DBSCAN, etc. By dividing users into different groups, we can identify the behavior habits of different types of users.

[0063] User behavior is dynamic, and the system needs to adjust its judgment of user behavior patterns in a timely manner through the accumulation and analysis of long-term data. Dynamic analysis can help identify changes in user behavior patterns over time, or changes under specific events or environmental factors.

[0064] Use time series analysis or moving average algorithms to track the changing trends of user behavior patterns. For example, by analyzing the changes in the frequency of user behavior over a period of time, you can determine whether the user's activity has increased or whether their interests have changed.

[0065] Build a prediction model based on the user's past behavior data to predict the user's possible future behavior. Behavior prediction can be performed using regression analysis and machine learning models (such as LSTM, RNN, etc.).

[0066] User behavior data is obtained by tracking the user's clicks, page views, purchases, etc. on the platform. These data are stored in log files or databases. Next, invalid clicks, abnormal behaviors, and other data are removed through data cleaning tools. The frequency of the cleaned user behavior data is counted to extract the user's high-frequency behaviors. Next, the user's behavior sequence is analyzed using the sequence pattern mining method to identify behavioral patterns with certain regularities.

[0067] Perform cluster analysis on the data of multiple users to identify user groups with similar behavioral characteristics. Use the K-means clustering algorithm to group users according to behavioral similarity.

[0068] Use time series models to monitor changes in user behavior over time and identify changes in behavioral trends. Use LSTM models to predict users’ likely future behavior patterns and adjust personalized recommendations or provide corresponding prompts based on these predictions.

[0069] Example: In the financial field, user behavior data can reflect investors' operating habits and preferences. For example, if a user frequently views a certain type of financial product or frequently clicks on analysis reports of certain stocks, the system can identify the user's preferred investment areas by analyzing these behaviors. Through behavioral pattern mining, it is possible to discover the user's operating rules after stock analysis, such as users often trade after reading multiple financial news articles. Based on these behavioral patterns, financial platforms can push relevant investment advice or market analysis reports in a timely manner to help users make more informed investment decisions.

[0070] By analyzing user behavior data, we can gain an in-depth understanding of users' operating habits and preferences, thereby identifying their behavior patterns. The analysis of behavior patterns can not only help the system predict users' future operations, but also provide basic data support for personalized recommendations and decision-making. The system can dynamically adjust recommendation strategies or provide more targeted services based on the identified behavior patterns, thereby improving user experience and user engagement.

[0071] S40, analyzing the event information to determine the event change trend;

[0072] In this embodiment, event information refers to external factors that may affect the market, user sentiment, or system decision-making. Events can be economic policies, market emergencies, corporate financial report releases, news and public opinion, etc. By analyzing these event information, useful features can be extracted and their potential impact on the market or user behavior can be identified.

[0073] Event information is obtained from news platforms, policy release platforms, social media, announcements and other channels through automated data crawling tools and API interfaces. This event information can be stored in structured or unstructured forms such as text, timestamps, and classification labels. Event data is preprocessed, including text cleaning, deduplication, and noise removal. The preprocessing step ensures the accuracy of subsequent analysis, such as filtering out events related to the analysis by extracting keywords, identifying named entities, and removing irrelevant information.

[0074] Event classification refers to the classification of event information according to the nature and impact of the event. Events can be divided into different types such as policy changes, corporate behaviors (such as financial report releases, mergers and acquisitions, etc.), natural disasters, social events, etc. Feature extraction is to extract key information from event data to facilitate subsequent trend analysis.

[0075] Event information can be classified based on machine learning text classification techniques (such as Naive Bayes, SVM, BERT, etc.). The system automatically identifies and classifies events based on the training data set. For example, for news text, the pre-trained BERT model can be used to identify event types such as economic policies, market sentiment, or corporate announcements.

[0076] Extracting keywords or entities (such as company names, person names, dates, etc.) from events through natural language processing technology can help to more accurately understand the specific impact of events. Using named entity recognition (NER) tools, you can automatically extract entities with high influence in text.

[0077] Event information analysis requires not only classification and feature extraction, but also assessment of the scope and intensity of the event. The system needs to comprehensively assess the potential impact of the event on user behavior or the market based on multiple dimensions such as the nature, scale, and frequency of the event.

[0078] Based on the scale of the event, the time window in which the event occurred, and the category of the event, the system can calculate the impact of the event on a specific field or user. For example, for major policy changes in the financial field, the impact strength can be measured by the activity of public opinion discussions, related stock price fluctuations, etc.

[0079] By analyzing the sentiment trends in news reports and social media, the negative or positive impact of an event can be assessed. For example, the release of a corporate earnings report may trigger fluctuations in market sentiment, and the system can assess the potential impact of an event on the market through sentiment analysis.

[0080] Event trend analysis is to identify the trajectory and future direction of event development by comparing historical events with current events. It can also predict the trend of event changes by analyzing event frequency, intensity, time interval, etc.

[0081] Use time series analysis models (such as ARIMA model, LSTM) to analyze the temporal changes of event information. For example, by analyzing the time nodes of multiple related events, we can identify the changing trends of events and determine the fluctuation direction of market or user sentiment.

[0082] Based on the frequency of events, the intensity and type of event impact, statistical models or machine learning prediction models are built to infer possible event change trends in the future. Historical event data can be used for training to predict the impact of similar events on the future.

[0083] Regularly collect major news, policy releases, corporate announcements and other event information through web crawlers, API interfaces or third-party data providers. Store event data by timestamp to facilitate subsequent time series analysis.

[0084] Use a machine learning-based text classification model to classify event information into different types (such as policy changes, market sentiment, natural disasters, etc.) and extract keywords and important entities in events (such as company names, dates, names of people, etc.) through natural language processing technology.

[0085] Calculate the scope and intensity of the event and assess its potential impact on the market or user sentiment. Use sentiment analysis technology to analyze relevant reports or comments to determine the emotional tendency of the event (positive, negative, neutral).

[0086] Apply time series analysis models to analyze historical event data, identify changes in the frequency and intensity of events, and predict the changing trends of future events. Build statistical models to infer the future impact of events and provide a basis for system decision-making.

[0087] Example description: In the financial field, event information analysis can help financial platforms quickly identify market emergencies and policy changes. For example, when a company releases its financial report, the system can automatically analyze the market's emotional response and predict the company's stock trend based on the content of the financial report and market feedback. At the same time, policy changes (such as interest rate adjustments, fiscal policy changes, etc.) can be analyzed through event change trends to predict the future direction of market sentiment fluctuations and help investors make reasonable decision preparations in advance. This real-time event analysis can significantly improve the decision-making flexibility and user service level of the financial platform.

[0088] Through the analysis and trend prediction of event information, the system can timely capture the changing trends of the market or user emotions. The classification and impact analysis of events can identify which events have a significant impact on user emotions or market fluctuations, helping the system to respond in real time. Event trend analysis can predict the emotional fluctuations that may be caused by future events, allowing the system to warn in advance and adjust the response strategy.

[0089] S50, determining an emotional state indicator based on the emotional characteristics, behavior patterns, and event change trends;

[0090] In this embodiment, the sentiment feature is the sentiment information extracted from the text data, including sentiment polarity (positive, negative or neutral) and sentiment intensity. Quantifying the sentiment feature means converting the sentiment information into numerical values ​​for subsequent calculation.

[0091] The sentiment polarity can be expressed as a numerical value, such as +1 for positive sentiment, -1 for negative sentiment, and 0 for neutral sentiment. According to the strength of the sentiment word, a floating value between 0 and 1 is set to represent the strength of the sentiment expression. For example, the sentiment word "very good" may correspond to a higher sentiment intensity (such as 0.8), while "good" may correspond to a lower sentiment intensity (such as 0.4).

[0092] Behavior patterns are the behavioral patterns that users display through a series of operations. These patterns can be quantified through behavior frequency, behavior change trends, etc., reflecting the user's operating habits and behavioral tendencies. By counting the user's behavior frequency in a specific time period, such as the number of clicks, visit frequency, number of purchases per day, etc., the behavior frequency can be expressed in integers or decimals. By comparing the changes in behavior frequency in different time periods, the degree of fluctuation in behavior can be calculated. For example, the standard deviation or rate of change (such as the ratio of change within a day) is used to express the magnitude of the change in behavior.

[0093] Event change trends refer to the changes in the impact of external events on user emotions or the market over time. These events can be news reports, policy changes, market emergencies, etc. Quantifying event trends means expressing the intensity and frequency changes of the impact of events through numerical values. By analyzing the emotional tendency (positive or negative) and the scope of influence (such as market fluctuations, social media discussion, etc.) of the event, the impact of the event is converted into a numerical value. For example, a value from 0 to 1 can be used to represent the intensity of the impact of the event, with 0 indicating no impact and 1 indicating the greatest impact. Through time series analysis, calculate the changes in the frequency and intensity of events. A common practice is to calculate the number of occurrences of related events per unit time and the magnitude of changes in their influence.

[0094] The emotional state index is generated by comprehensively considering the quantitative values ​​of emotional characteristics, behavioral patterns, and event change trends, reflecting the current overall emotional state of users or the market. Through weighted calculation, the influence of various factors is combined to obtain a numerical emotional state index. Assign a weight to each factor (emotional characteristics, behavioral patterns, event change trends), and adjust the weight according to the needs of different scenarios. For example, the impact of user behavior on emotional state may be set to 50%, emotional characteristics to 30%, and event changes to 20%. Using the weighted average method, the quantitative values ​​of emotional characteristics, behavioral patterns, and event change trends are added together to calculate the final emotional state index.

[0095] The sentiment analysis model is used to process text data and extract sentiment polarity and sentiment intensity. The positive, negative, and neutral sentiment polarities are converted into numerical values, and the sentiment intensity is determined by analyzing the vocabulary weight and sentence structure. Finally, the sentiment feature is converted into a numerical value.

[0096] Conduct frequency statistics on user behavior data and analyze the changing trends of user operations. Quantify user behavior fluctuations through indicators such as standard deviation and frequency ratio.

[0097] Analyze event information through time series models to calculate the frequency and impact of events. Use sentiment analysis tools to further evaluate the emotional tendency of events and quantify the impact of events on emotions.

[0098] Different weights are set, and the quantitative values ​​of emotional characteristics, behavioral patterns, and event change trends are combined to perform weighted average calculations to generate the final emotional state index, which reflects the overall emotional state of the current user or market.

[0099] Example: In the financial field, through comprehensive analysis of investors' emotional characteristics, trading behavior patterns and external market events (such as policy changes or corporate financial report releases), an indicator that reflects the overall market sentiment can be calculated. When investor sentiment fluctuates violently, market events occur frequently, and behavior patterns show abnormal trading frequencies, the system can assess the possible risks in the market through the emotional state indicator and issue early warning information to investors to help them make more rational and stable investment decisions.

[0100] By combining emotional features, user behavior, and event change trends, the system can accurately assess the emotional state of users or the market. Emotional state indicators provide a basis for the system's decision-making, reflecting the magnitude and stability of user emotional fluctuations, and helping the system make more personalized recommendations or decisions. In addition, the addition of event information makes emotional analysis more comprehensive, taking into account the impact of the external environment on user emotions, thereby improving the system's prediction accuracy and resilience.

[0101] S60, monitors the information of online platforms and obtains user emotional feedback;

[0102] In this embodiment, the online platform refers to a social media platform, forum, news comment area, e-commerce platform, etc. where users are active, from which the user's emotional response to a specific event, product or market can be obtained. The purpose of monitoring the information of the online platform is to collect user-generated content (UGC) in real time, such as comments, likes, shares, articles, discussions, etc. These data can reflect the user's emotional attitude towards a specific event or product. By using a web crawler tool, user comments, posts, articles and other information of a specified platform are automatically captured. For example, user comments on social media, discussion posts in forums, etc.

[0103] Some platforms provide public API interfaces, which can be used to obtain users' comments, feedback, likes and other information in real time. For example, the API interfaces of platforms such as Twitter and Reddit can be used to capture comments and discussion data.

[0104] Establish a regular data crawling mechanism to monitor and store user content from different platforms to ensure the timeliness and continuity of information collection.

[0105] User emotional feedback refers to the emotional reactions expressed by users through online platforms. These reactions can be emotional tendencies in text content (such as positive, negative, neutral), or emotional attitudes reflected in non-text behaviors (such as likes, shares, comments, etc.). User emotional feedback is a direct reflection of the user's emotional state and is used for subsequent sentiment analysis and emotional state evaluation.

[0106] Perform natural language processing (NLP) and sentiment analysis on the captured text data (such as comments, articles, etc.) to identify the polarity and intensity of user emotions. For example, sentiment classification models (such as BERT, SVM, etc.) can be used to analyze the emotional tendencies of user comments and extract the user's emotional response.

[0107] By analyzing the user's interactive behaviors on the platform, such as likes, shares, and reposts, we can infer the user's emotional feedback. The number of likes and reposts of a certain content by users can often reflect their emotional attitude towards the content, especially positive or supportive emotions.

[0108] The sentiment polarity (positive, negative, neutral) and sentiment intensity in the analysis results are quantified and extracted into structured sentiment data for subsequent use.

[0109] There may be differences in users' emotional feedback on multiple online platforms, so it is necessary to integrate the emotional data on different platforms to generate a unified emotional view.

[0110] Clean the data from different platforms, including deduplication, denoising, and format unification. The data structure and quality of different platforms may be different, so standardization is needed to ensure the uniformity of the data. Summarize user sentiment feedback from multiple platforms and store them in a database. You can use big data storage frameworks (such as Hadoop and Elasticsearch) to integrate and manage this data.

[0111] Use web crawlers or API interfaces to crawl user-generated content from online platforms such as social media, news websites, and forums in real time. Web crawlers are run regularly to ensure that the latest user sentiment feedback data is continuously collected. API interfaces are used for efficient extraction of real-time data. The acquired text data is input into the natural language processing system, and the sentiment analysis model is used to identify the sentiment polarity and sentiment intensity in the text. For non-text data, the user's sentiment attitude towards the content is inferred by analyzing the user's likes, reposts, and number of comments. After the sentiment data is processed by the sentiment classification model, it is quantified into sentiment polarity (positive, negative, neutral) and intensity values ​​and stored as structured data. Clean, denoise, and deduplicate the sentiment data from different platforms and ensure that the format is consistent. The integrated sentiment data is stored in a unified database to provide support for subsequent sentiment state analysis.

[0112] Example: In the financial field, by monitoring investor comments on social media, discussions on financial forums, and user messages on news platforms, investors' emotional reactions to market dynamics, individual stock performance, or policy changes can be obtained. For example, after a company releases its quarterly financial report, the system can identify investors' emotional tendencies by crawling social media and news comments. If investors' comments are mostly positive and have a high number of likes and reposts, the system can infer that market sentiment is optimistic and provide investors with investment advice based on emotional feedback.

[0113] By monitoring the information on online platforms, users' emotional feedback can be obtained in real time and comprehensively. Combining sentiment analysis of text and non-text behavioral data, the system can accurately grasp the user's emotional response to events, products or markets. Through the integration and standardization of multi-platform data, the system can generate a more complete view of emotional feedback, providing data support for subsequent emotional state assessment and personalized recommendations. This method can improve the coverage and accuracy of emotional feedback, and enable the system to have stronger real-time analysis capabilities.

[0114] S70, comprehensively analyzing the emotional state indicator and the user's emotional feedback to generate an emotional analysis result;

[0115] In this embodiment, the emotional state index is a value calculated based on emotional characteristics, user behavior patterns and event change trends, and is used to quantify the overall emotional state of the current market or users. It reflects the comprehensive impact of emotions, behaviors and external events on user emotions.

[0116] The emotional state index has been generated through weighted calculation in the previous steps. At this point, the system uses it as input data for comparison and comprehensive analysis with the user's real-time emotional feedback. The emotional state index can be a single value or a multi-dimensional indicator vector, depending on the analysis requirements.

[0117] User sentiment feedback is obtained by sentiment analysis of user-generated content (UGC) on online platforms, and includes users' sentiment tendencies (positive, negative, neutral) and sentiment intensity. It represents the user's immediate reaction to current events, products, or markets. User sentiment feedback is generated by a sentiment analysis system, specifically including quantitative results of sentiment polarity and intensity. Feedback data can be obtained from non-text interactions such as social media comments, news comments, and likes, and converted into a standardized sentiment data format for analysis in combination with sentiment state indicators.

[0118] Comprehensive analysis refers to the fusion of emotional state indicators and user emotional feedback, evaluating the correlation and difference between the two, and generating the final emotional analysis results. This process needs to consider the time dimension, emotional tendency and intensity of the two sets of data, and analyze whether the user emotional feedback is consistent with the current emotional state indicators. The correlation between emotional state indicators and user emotional feedback is evaluated through correlation analysis techniques (such as Pearson correlation coefficient). If the two are highly correlated, it can be inferred that the user's emotions are consistent with the overall market sentiment or event sentiment changes. Calculate the deviation between the emotional state indicator and the user's emotional feedback. If the deviation is large, it means that the user's emotions are inconsistent with the overall emotions, and further measures may be needed to adjust or understand the user's special emotional state. The deviation calculation formula can be a simple difference or a multidimensional difference analysis based on Euclidean distance.

[0119] In comprehensive analysis, weights can be set based on the importance of the emotional state indicator and the user's emotional feedback. For example, when the user's emotional feedback is more representative than the emotional state indicator, a larger weight can be given to adjust the final result. The comprehensive analysis uses the weighted average method to calculate the combined result of the two.

[0120] The result of sentiment analysis refers to the final output after comprehensive analysis, which is used to describe the current user's emotional state or the market's emotional fluctuations. It combines the correlation and difference of emotional state indicators and user emotional feedback to obtain a quantitative emotional state evaluation. Based on the correlation and deviation analysis results, the system generates a sentiment analysis result to represent the current emotional state. The result of sentiment analysis is usually a quantitative value or level, indicating the intensity of the current emotional fluctuation or the stability of the user's emotions. Based on the results of sentiment analysis, the system can further determine the degree of fluctuation of the user's emotions. For example, a large deviation may mean that the user's emotions are unstable, while a high correlation indicates that the user's emotions are relatively stable. The results of sentiment analysis can be used for subsequent personalized recommendations or market warnings.

[0121] The system obtains the emotional state indicators from the previous steps and obtains the user's emotional feedback through the API interface or the real-time data capture system. These two data are used as inputs for comprehensive analysis and stored in the database or memory, ready for subsequent analysis. First, the system performs a correlation analysis on the emotional state indicators and the user's emotional feedback, calculates the correlation coefficient between the two, and evaluates their correlation. If there is a high correlation between the two, the system will consider that the user's emotions are consistent with the overall trend. Next, the system performs a difference analysis, calculates the deviation between the emotional state indicators and the user's emotional feedback, and determines whether the user's emotions are consistent with expectations. If the deviation is large, the system will further evaluate the user's emotional fluctuations. Finally, the system performs a weighted average calculation of the correlation and deviation results to generate a comprehensive emotional evaluation value, which represents the current user's emotional state or the degree of fluctuation of market sentiment.

[0122] Based on the results of the comprehensive analysis, the system outputs the sentiment analysis results and stores or passes them to downstream modules for personalized recommendations or market analysis warnings. The sentiment analysis results can be a specific value or level to describe the current emotional state.

[0123] Example: In the financial field, the system can evaluate the emotional fluctuations of investors in real time through comprehensive analysis of emotional state indicators and user emotional feedback. For example, the emotional state indicator may show that the overall market sentiment is optimistic, but by monitoring social media comments, the system finds that user emotional feedback shows an increase in negative emotions. Through comprehensive analysis, the system can determine that there is a potential risk of fluctuations in market sentiment, and then provide investors with emotional warnings and adjust recommended investment strategies.

[0124] By comprehensively analyzing the emotional state indicators and user emotional feedback, the system can accurately evaluate the user's emotional state in real time and identify emotional fluctuations. This not only improves the accuracy of sentiment analysis, but also enhances the system's real-time response capabilities. The results of sentiment analysis can provide strong data support for personalized recommendations, market warnings, user emotion management and other functions, ensuring that the system can make corresponding adjustments to user emotional changes in a timely manner.

[0125] S80: Generate target output content for responding to the user's emotional state based on the emotion analysis result.

[0126] In this embodiment, the emotion analysis result is a quantitative result obtained by combining the emotion state index and the user emotion feedback, which represents the current emotion state of the user or the market. The emotion analysis result is used to determine how the system generates personalized output content, such as recommendation information, prompts, warnings, etc.

[0127] The result of sentiment analysis may be a numerical value or a level, representing the degree of sentiment fluctuation, emotional intensity or emotional stability. This result serves as the basic input for subsequent content generation and enters the output module of the system to formulate corresponding content generation strategies.

[0128] Based on the results of sentiment analysis, the system needs to generate target output content. The output content can be personalized recommendations, prompts, warning information, or decision-making suggestions, depending on the user's emotional state and the analysis results. If the sentiment analysis results show that the user's emotions are relatively stable and positive, the system can generate personalized recommendation content (such as recommending related products, news, etc.). At this time, the content generation module generates personalized output suitable for the user's emotional state through recommendation algorithms (such as collaborative filtering, content-based recommendations, etc.). When the sentiment analysis results show that the user's emotions fluctuate greatly or the market sentiment tends to be negative, the system can generate prompts or warning content. Through the predefined rule engine, the system can generate corresponding warning information for users to help users avoid irrational behavior due to emotional fluctuations. For example, in the financial field, if the sentiment analysis results show that investors' emotions fluctuate greatly, the system can generate a prompt of "Please invest with caution". Based on the sentiment analysis results, the system can also generate adjustment suggestions to guide users on how to balance their emotions or take actions. For example, the system can recommend relevant emotional management suggestions or adjust the user's recommendation content preferences to adapt to the current emotional state.

[0129] The generated output content needs to be presented in a form that users can understand and accept. Depending on the results of the sentiment analysis, the output content can be in the form of text, charts, warning boxes, notifications, etc. According to different scenarios and emotional states, the system formats the generated content to ensure that the content is concise and clear. For example, when the user's emotions are relatively stable, the system can generate detailed recommendation information and related prompts; when the emotions fluctuate greatly, the warning content can be presented in a more conspicuous way such as pop-ups and notifications. The system can output content through different channels, such as mobile phone push, email, platform notifications, etc., to ensure that users can obtain the output content that matches their emotional state in a timely manner.

[0130] The generated target output content also needs to be fed back to the system so that the system can continuously optimize the content generation strategy and effect. User feedback on the output content will be recorded and used to further adjust the content generation logic. The system can obtain user feedback data by monitoring the user's interactive behavior on the recommended content and prompt information (such as clicking, ignoring, positive or negative evaluation). Through these feedbacks, the system can adjust the generation strategy of future output content.

[0131] The system obtains the user's current emotional state data from the emotion analysis results of the previous step. This result may be a quantified emotion value, emotion polarity, emotion intensity, etc., which is stored in the memory or database as the core input data of the content generation module. If the emotion analysis results show that the user's emotions are relatively positive and stable, the system generates personalized content (such as news, product recommendations, etc.) through the recommendation engine. Based on the user's emotional state and past behavior records, the system calls the recommendation algorithm to generate recommendation information that matches the user's emotions. When the emotion analysis results show that the user's emotions fluctuate greatly or tend to be negative, the system can generate warnings or prompts according to preset rules to remind the user to be cautious when making decisions. Emergency notifications or intervention suggestions are generated by the predefined rule system.

[0132] The output content is presented in a format that is tailored to the user's emotional state. For recommended information, the platform's message notification or push system can be used to provide recommended content to users; for warning or prompt information, the system uses pop-ups, warning boxes, etc. to ensure that users can perceive it in a timely manner. The system monitors the user's interaction with the output content and collects feedback data. The user's behavioral data (such as click-through rate, dwell time, emotional response) will be used to further optimize content generation and emotional state assessment strategies.

[0133] Example description: In the financial field, through the results of sentiment analysis, the system can generate personalized investment advice for investors. When the system detects that investor sentiment tends to be stable and market sentiment is optimistic, the system can recommend suitable investment products or strategies to help investors seize market opportunities. When investor sentiment fluctuates greatly and market sentiment tends to be negative, the system can generate risk warnings or warning messages suggesting suspension of trading to help investors avoid potential risks brought about by emotional decisions. This output content generation mechanism based on sentiment analysis can significantly improve the rationality of investor decision-making and reduce losses caused by market fluctuations.

[0134] By generating target output content based on sentiment analysis results, the system can better adapt to users' mood changes and provide users with more personalized and dynamically adjusted services. For users with relatively stable emotions, the system can provide appropriate recommended content; for users with large mood swings, the system can issue warnings or prompts in a timely manner to help users make more rational and robust decisions. This personalized content generation mechanism can effectively improve user experience and enhance the decision support function of the system.

[0135] The present invention relates to the field of artificial intelligence technology and financial technology, and discloses an information processing method based on sentiment analysis, which collects data and information, including text data, user behavior data and event information; performs natural language processing and sentiment analysis on text data to identify sentiment characteristics of text data; analyzes user behavior data to determine behavior patterns; analyzes event information to determine event change trends; generates sentiment state indicators based on sentiment characteristics, behavior patterns and event change trends; monitors online platforms to obtain user sentiment feedback; comprehensively analyzes sentiment state indicators and user sentiment feedback to generate sentiment analysis results; and generates target output content based on sentiment analysis results. The present invention can capture the user's emotional state in real time, dynamically adjust decision suggestions, and effectively respond to market or environmental changes by combining sentiment analysis with behavior data and event change trends. In addition, the accuracy of personalized recommendations is further improved by combining analysis of user sentiment feedback. This not only enhances the system's ability to respond to external changes, but also better meets user personalized needs and optimizes the overall decision-making process.

[0136] In one embodiment, the above S50 includes:

[0137] S501, generating a quantitative value of the emotional feature based on the emotional polarity and emotional intensity in the emotional feature;

[0138] S502, analyzing the behavior frequency and the behavior change degree in the behavior pattern to generate a quantitative value of the behavior pattern;

[0139] S503, generating a quantitative value of the event change trend according to the event impact intensity and the event impact coefficient on the user's emotion;

[0140] S504, performing weighted analysis on the quantified values ​​of the emotional characteristics, behavioral patterns, and event change trends to generate the emotional state index.

[0141] In this embodiment, sentiment features are extracted from text data through natural language processing and sentiment analysis, including sentiment polarity (such as positive, negative or neutral) and sentiment intensity (such as the intensity of the sentiment expression). These sentiment features are quantified into numerical values ​​to facilitate subsequent weighted calculations.

[0142] Based on the analysis results, sentiment polarity can be quantified as positive (+1), negative (-1), and neutral (0), indicating the sentiment tendency of users or the market towards a specific event. Sentiment intensity is based on the frequency and intensity of sentiment words and is set in the range of 0 to 1. For example, a stronger sentiment expression (such as "very happy") may correspond to a higher sentiment intensity value (such as 0.9), while a weaker sentiment expression (such as "a little good") may correspond to a lower sentiment intensity value (such as 0.4). Sentiment polarity and sentiment intensity are combined to generate a quantitative value of the sentiment feature, and finally a comprehensive value representing the sentiment feature is obtained.

[0143] The quantification of behavioral patterns is achieved by analyzing dimensions such as the user's operation frequency and behavior change trends. This can indicate the stability or volatility of the user's behavior over time or environmental changes. By counting the user's behavior frequency, such as the number of clicks per hour, the frequency of daily logins, etc. The behavior frequency can be used as a measure of behavioral activity and is usually quantified as an integer or floating point number. Behavioral changes are determined by analyzing the fluctuation trend of behavior, such as through standard deviation, variance, or behavior change ratio (such as the comparison of the behavior in the past week with the previous week). A larger change amplitude indicates a larger fluctuation in the user's behavior pattern, and a smaller change amplitude indicates stable behavior. Combining the behavior frequency with the degree of behavior change, a comprehensive behavior pattern quantification value is generated to reflect the activity and stability of the user's behavior.

[0144] The quantification of event change trends is based on the impact of external events on the market or users. This requires considering the intensity of the event (such as the importance and scale of the event) and the impact of the event on user emotions. By analyzing the scale, duration, and influence of public opinion of the event, the intensity of the event impact can be quantified with a value from 0 to 1. For example, major economic policy changes may be assigned a higher impact intensity (such as 0.8 or 0.9), while minor market fluctuations may be assigned a lower impact intensity (such as 0.2). By analyzing the degree of impact of the event on user emotional feedback (such as the user's attention or emotional response to the event), the event is assigned an impact coefficient. The emotional impact coefficient can be assigned based on the intensity of the user's emotional feedback, such as a range of 0 to 1, reflecting the influence of the event on the user's emotions. The impact intensity of the event and the emotional impact coefficient are combined to generate a quantitative value of the event change trend. This value is used to reflect the importance of the event in the market or user emotions.

[0145] By weighting the quantitative values ​​of emotional characteristics, behavioral patterns, and event change trends, an overall emotional state index is generated. Weighted analysis is to balance the impact of different factors on emotional state. Different weight values ​​are assigned to emotional characteristics, behavioral patterns, and event change trends. The specific weights can be adjusted dynamically according to the application scenario, such as emotional characteristics can account for 50%, behavioral patterns can account for 30%, and event change trends can account for 20%. Through weighted analysis, a numerical indicator that reflects the current emotional state of users or the market is finally generated, which can be used for subsequent personalized recommendations, market forecasts, or emotional warnings.

[0146] This embodiment can generate an index that accurately reflects the current emotional state through quantification and weighted analysis of emotional characteristics, behavior patterns, and event change trends. The emotional state index can not only reflect the emotional fluctuations of users or the market in real time, but also provide important data support for subsequent personalized recommendations and market warnings. The generation of this index can help the system better understand the changes in user emotions, thereby optimizing decision-making and services.

[0147] In one embodiment, the above S60 includes:

[0148] S601, collecting text content generated by users from an online platform;

[0149] S602, extracting sentiment polarity and sentiment intensity in the text content based on a sentiment analysis module, and generating sentiment data according to the sentiment polarity and sentiment intensity;

[0150] S603, combining the emotion data with the user's behavior pattern and historical emotion data, and comprehensively analyzing to generate the user emotion feedback.

[0151] In this embodiment, user-generated content (UGC) in the online platform usually appears in the form of comments, posts, articles, etc. These text contents can directly reflect the user's emotional response to an event, product or market. Use web crawler technology to automatically collect text data such as user comments, posts or articles from online platforms (such as social media, forums, news websites, etc.). Crawlers can crawl content regularly to ensure the real-time and integrity of data. Use the API interface provided by the platform (such as TwitterAPI, RedditAPI, etc.) to obtain user-generated content in real time, and filter relevant text data according to preset topics or keywords.

[0152] The role of the sentiment analysis module is to analyze the text content generated by users through natural language processing technology, identify the sentiment tendency (such as positive, negative or neutral) in the text and the intensity of the sentiment expression. Use sentiment analysis models (such as dictionary-based sentiment analysis or machine learning models) to process the text and identify the sentiment polarity in the text. Polarity is usually expressed as positive, negative or neutral. By analyzing the sentiment vocabulary, context and sentiment expression in the text, the intensity of the sentiment is quantified (such as a numerical range of 0 to 1) to indicate the strength of the sentiment expression. For example, a strong negative sentiment may have a higher intensity value. The extracted sentiment polarity and sentiment intensity are combined to generate structured sentiment data, which is usually expressed as a combination of sentiment polarity and intensity. For example, a text may be identified as negative sentiment (polarity is -1) and strong intensity (intensity is 0.8).

[0153] Emotional data needs to be combined with the user's behavior patterns (such as the user's operation frequency, behavior trends, etc.) and historical emotion data to form a more complete judgment of the user's emotions. The user's historical emotion data includes their past emotional reactions and behavior patterns, which can reflect the user's long-term emotional tendencies. By monitoring and analyzing the user's behavior patterns on the platform (such as browsing history, click frequency, comment interaction, etc.), combined with the current emotion data, the association between the user's emotions and behaviors can be judged. For example, if the user's behavior pattern shows positive behavior, but the emotion data is negative, further analysis of the cause of the emotional fluctuation may be required. Compare the currently extracted emotion data with the user's historical emotion data to understand the user's emotional change trend. For example, if the user's historical emotion data is mostly positive, but the current emotion data shows negative emotions, the system can infer that the emotion may have fluctuated greatly.

[0154] User emotional feedback is a comprehensive emotional response obtained by integrating current emotional data, user behavior patterns, and historical emotional data. This feedback can reflect the user's emotional state changes, emotional fluctuations, or stability, and help the system make subsequent decisions. By combining the results of sentiment analysis, user behavior pattern data, and historical sentiment data, a weighted algorithm or machine learning model (such as a decision tree or neural network) is used to generate user emotional feedback. Emotional feedback can indicate whether the user's current emotional state is positive, negative, or in an unstable state. Based on the results of the comprehensive analysis, structured emotional feedback data is generated and stored in a database or used in subsequent personalized recommendations or emotional state assessments.

[0155] In this embodiment, by collecting user-generated text content from the online platform and performing sentiment analysis, the system can accurately extract the user's sentiment polarity and intensity. After combining the current sentiment data with the user's behavior pattern and historical sentiment data, the system can generate more complete and accurate user sentiment feedback. This feedback not only helps the system understand the user's emotional state in real time, but also provides strong data support for personalized recommendations, sentiment management, and market analysis.

[0156] In one embodiment, the above S70 includes:

[0157] S701, analyzing the deviation between the emotional state indicator and the user's emotional feedback;

[0158] S702: Generate a sentiment analysis result based on the deviation, where the sentiment analysis result is used to reflect the degree of sentiment fluctuation or the sentiment stability of the user.

[0159] In this embodiment, the emotional state index is quantified based on emotional characteristics, behavior patterns, and event change trends, and represents the system's prediction or inference of the user's overall emotional state. The user's emotional feedback is generated through real-time emotional analysis and represents the user's current actual emotions. The deviation between the two reflects the difference between the emotional state predicted by the system and the user's current actual emotional state. The emotional state index obtained through weighted analysis is compared with the user's current emotional feedback. The deviation can be expressed in the form of numerical difference. Common calculation methods include simple difference calculation or Euclidean distance calculation, which are used to quantify the degree of difference between the two.

[0160] The deviation can be calculated as follows: Deviation = |emotional state indicator - user emotional feedback|.

[0161] If the deviation value is large, it means that there is a large difference between the user's current emotion and the emotional state predicted by the system. If the deviation value is small, it means that the user's emotion is relatively stable or consistent with the system prediction.

[0162] The results of sentiment analysis are obtained by analyzing the deviation between the sentiment state index and the user's sentiment feedback, and are mainly used to reflect the degree of fluctuation or emotional stability of the user's current sentiment. The larger the deviation value, the more obvious the user's sentiment fluctuation is. Conversely, the smaller the deviation value, the more stable the mood is. If the deviation value is large, the system can judge that the user's mood fluctuates greatly. At this time, the sentiment analysis results will reflect the user's mood fluctuations, which may prompt the system to pay special attention to the user's emotional state or adjust the subsequent personalized recommendation strategy. If the deviation value is small, it means that the user's mood is relatively stable, and the sentiment analysis results will reflect that the user's mood tends to be consistent and stable. At this time, the system can continue to use the current recommendation or behavior mode.

[0163] Finally, the system will generate a sentiment analysis result based on the deviation between the sentiment state indicator and the user's sentiment feedback. The result can be expressed in numerical values ​​or levels, for example, the sentiment fluctuation level can be divided into three levels: "high", "medium" and "low", or expressed in specific sentiment fluctuation values.

[0164] The results of sentiment analysis can provide a basis for the system's subsequent personalized recommendations, prompt information generation, market analysis, etc. The system can adjust its service strategy based on the analysis results and provide users with content or prompts that are more suitable for their current emotional state. If the sentiment analysis results show that the user's emotions fluctuate greatly, the system can generate risk warnings or reduce the frequency of pushing recommended content; if the emotions are relatively stable, the system can continue to provide recommended content that matches the user's emotional state. For users with obvious emotional fluctuations, the system can provide some emotional management suggestions or guidance content to help users relieve or regulate their emotions.

[0165] By analyzing the deviation between the emotional state indicator and the user's emotional feedback, the system can timely judge the user's emotional fluctuation or emotional stability. This process can help the system understand the user's emotional state more accurately, thereby providing more targeted personalized services and content recommendations. Based on the results of emotional analysis, the system can adjust the content generation strategy to avoid a decline in user experience due to emotional fluctuations and improve the system's intelligent response capabilities.

[0166] In one embodiment, before the above S20, the method further includes:

[0167] S201, collecting historical data and corresponding sentiment analysis results;

[0168] S202: Based on the historical data and the corresponding sentiment analysis results, a sentiment analysis model is trained and generated, where the sentiment analysis model is used to perform natural language processing and sentiment analysis on the text data.

[0169] In this embodiment, the historical data includes the user text data collected in the past and the corresponding sentiment analysis results. These historical data can be known sentiment tendencies (positive, negative, neutral) and sentiment strengths. By collecting enough historical data, the system can use these data to train the sentiment analysis model.

[0170] Historical data can come from different online platforms, social media, news articles, user comments and other channels. Sentiment analysis results come from previous sentiment analysis operations or manually annotated sentiment tags. The collected historical data needs to be preprocessed, such as removing irrelevant content, cleaning invalid data, and unifying data formats. The cleaned data should include each text segment and its corresponding sentiment polarity (such as positive, negative, neutral) and sentiment intensity (such as a quantitative value from 0 to 1).

[0171] Training a sentiment analysis model is based on existing historical data and its corresponding sentiment analysis results. A model that can automatically identify text sentiment features is trained through a machine learning algorithm. This model will be used for subsequent text sentiment analysis operations. Common sentiment analysis models can be based on traditional machine learning algorithms (such as support vector machine SVM, naive Bayes) or deep learning-based models (such as LSTM, BERT). Choosing a suitable model is related to the size and complexity of the data set. Use historical data as a training set to train the model through supervised learning. The model receives input text data and learns the mapping relationship between text and sentiment based on the labeled sentiment polarity and sentiment intensity. After the model training is completed, the accuracy and generalization ability of the model need to be evaluated through a validation set and a test set. If the performance of the model is not ideal, it may be necessary to optimize the performance of the model through hyperparameter adjustment, data enhancement, or changing the model architecture.

[0172] The trained sentiment analysis model will be used in actual sentiment recognition tasks. It can perform natural language processing (NLP) operations on the input text data and output the sentiment features of the text (such as sentiment polarity and sentiment intensity). Before inputting the sentiment analysis model, the system first needs to perform preprocessing operations on the text data, such as word segmentation, stop word removal, part-of-speech tagging, etc. These operations can help the model better understand the text content. The model extracts the sentiment polarity and sentiment intensity of the text by analyzing the vocabulary, syntactic structure, and contextual information of the text. Sentiment polarity can be positive, negative, or neutral, and sentiment intensity quantifies the intensity of the emotional expression.

[0173] By collecting historical data and training the sentiment analysis model, the system can more accurately perform sentiment analysis on future text data. Automated sentiment recognition based on the sentiment analysis model can significantly improve the system's sentiment analysis capabilities, allowing the system to quickly and accurately determine the user's emotional tendency and intensity, and make more accurate emotional state assessments or personalized recommendations based on this.

[0174] In one embodiment, after the above S50, the method further includes:

[0175] S505, setting a warning threshold of the emotional state indicator;

[0176] S506, when the emotional state indicator reaches the warning threshold, generating warning information;

[0177] S507: Send the warning information to the business processing end.

[0178] In this embodiment, the emotional state index is obtained by comprehensive analysis of emotional characteristics, behavior patterns and event change trends. The purpose of setting the warning threshold is to identify abnormal fluctuations in user or market emotions. When the emotional state index reaches or exceeds the threshold, the system will trigger the warning mechanism.

[0179] Warning thresholds can be set based on historical data, industry standards, or system configuration. For example, the system can set a benchmark based on past mood swings, and trigger a warning when the mood swing exceeds a certain value. Specifically, upper and lower thresholds can be set, such as triggering a warning when the mood state index is higher than 0.8 or lower than 0.2. Warning thresholds can be static (fixed value) or dynamically adjusted. Dynamic thresholds can be automatically adjusted based on real-time data analysis or the results of machine learning models to meet the needs of different scenarios.

[0180] When the emotional state indicator exceeds the preset threshold, the system will generate an early warning message. The content of the early warning message usually includes the details of the emotional anomaly, indicating that the user's emotional fluctuations are too large, the market sentiment is abnormal, etc. The system monitors the emotional state indicator in real time, and when it exceeds the preset threshold, the early warning mechanism is triggered immediately. The early warning information generation module will generate targeted early warning content according to the specific exceeded range and the type of emotional fluctuation. The early warning information includes the current value of the emotional state indicator, the threshold, and the system's interpretation of the emotional fluctuation. For example, "The current emotional state indicator is 0.9, which exceeds the early warning threshold of 0.8, and the user's emotional fluctuations are significant." This information can be output in the form of text, charts, or alerts.

[0181] Early warning information needs to be delivered to the relevant business processing end in a timely manner so that the system or staff can take further actions, such as adjusting the recommendation strategy, sending warning messages or triggering automated processing processes.

[0182] The system sends the warning information to the relevant business processing end, which may be the content recommendation module, market analysis module or risk management system. The warning information can be transmitted to the corresponding business processing system through technical means such as API interface and message queue.

[0183] The system can send warning information to the business processing end or administrator via email, platform notification, SMS or other instant messaging tools. For automated business processing, warning information can be sent to the system's decision-making module through a message queue or API interface to trigger subsequent operations.

[0184] In this embodiment, by setting a warning threshold and generating warning information when the emotional state indicator reaches the threshold, the system can monitor the emotional fluctuations of users or the market in real time and respond in a timely manner. Sending warning information to the business processing end can help the system or staff to quickly take countermeasures, such as sending prompt information or adjusting recommendation strategies, to prevent the negative impact caused by excessive emotional fluctuations. This warning mechanism can significantly improve the system's emotional monitoring and response capabilities and ensure the effectiveness of emotional management.

[0185] In one embodiment, the above S80 includes:

[0186] S801, obtaining the risk preference information and expected results of the target user;

[0187] S802, generating personalized output content for the target user based on the risk preference information, expected results and sentiment analysis results;

[0188] S803: Send the personalized output content to the target user.

[0189] In this embodiment, the system needs to obtain the user's risk preference and expected results from the information provided by the user. Risk preference refers to the user's tolerance for risk when making decisions, which is usually divided into three types: conservative, neutral and aggressive. Expected results refer to the goals or results that the user hopes to achieve in a specific situation.

[0190] The system can obtain the risk preference information of the user through the user's historical behavior (such as transaction history, past choices), the risk assessment form filled out by the user, or directly through the user's account settings. For example, in the financial field, the user's risk preference can reflect his or her tendency in investment strategy. The expected results can be inferred through questionnaires, user-defined settings, or system analysis. For example, the user may want to maximize short-term profits or obtain a stable long-term return on investment. This target information can be stored in the user's account configuration.

[0191] The system generates personalized output content based on the user's risk preferences, expected results, and current sentiment analysis results. The output content may be decision suggestions, product recommendations, risk warnings, etc., all adjusted according to the user's current emotional state and personalized needs.

[0192] The system generates output content suitable for the user based on the user's risk preference and expected results, combined with the current sentiment analysis results (such as the degree of the user's current mood swings). For example, when the user's mood is relatively unstable, the system may recommend a more conservative option; when the user's mood is stable and the risk preference is high, the system may recommend an aggressive option.

[0193] The system uses recommendation algorithms or rule engines to generate personalized output content. The recommendation engine can generate personalized content based on user portraits, sentiment analysis results, and risk preferences using collaborative filtering, content-based recommendations, and other technologies.

[0194] The output content can be text (such as prompt information, analysis report), chart (such as investment strategy chart, risk assessment chart) or other forms of suggestions. The specific type depends on the system design and user needs.

[0195] The system needs to send the generated personalized output content to users to ensure that users can receive relevant information in a timely manner. The delivery of these contents can be achieved through different channels, such as platform messages, emails, mobile application push, etc.

[0196] Through the message push function in the system, the personalized output content is sent to the user's account message center or mobile device in the form of notification. The system can push this content to users regularly or in real time.

[0197] The system can send personalized output content to users through multiple channels such as email, text messages, instant messaging applications, etc., ensuring that users can obtain this information in a timely manner.

[0198] Depending on the user's device and preferences, the system can choose a suitable content display method, such as a report with pictures and text, a concise prompt message or an interactive chart display.

[0199] By combining the user's risk preferences, expected results, and sentiment analysis results, the system can generate highly personalized output content to help users make rational decisions based on their current emotional state and preferences. This approach can improve user experience and ensure that the content provided by the system is highly matched to user needs, thereby improving the system's recommendation accuracy and user satisfaction.

[0200] In one embodiment, an information processing device based on sentiment analysis is provided, and the information processing device based on sentiment analysis corresponds one-to-one to the information processing method based on sentiment analysis in the above embodiment. Figure 3 , Figure 3 This is a functional module diagram of a preferred embodiment of an information processing device based on sentiment analysis of the present invention. Data acquisition module 10, sentiment analysis module 20, user behavior analysis module 30, event analysis and trend prediction module 40, sentiment state analysis module 50, user sentiment feedback acquisition module 60, sentiment comprehensive analysis module 70 and output generation module 80. Each functional module is described in detail as follows:

[0201] A data collection module 10 is used to collect data and information, including text data, user behavior data and event information;

[0202] The sentiment analysis module 20 is used to perform natural language processing and sentiment analysis on the text data to identify the sentiment characteristics of the text data;

[0203] A user behavior analysis module 30, used to analyze the user behavior data and determine the user's behavior pattern;

[0204] An event analysis and trend prediction module 40 is used to analyze the event information and determine the event change trend;

[0205] An emotional state analysis module 50, for determining an emotional state index based on the emotional characteristics, behavior patterns and event change trends;

[0206] The user emotion feedback collection module 60 is used to monitor the information of the online platform and obtain the user emotion feedback;

[0207] The emotional comprehensive analysis module 70 is used to comprehensively analyze the emotional state indicators and user emotional feedback to generate emotional analysis results;

[0208] The output generation module 80 is used to generate target output content for responding to the user's emotional state based on the emotion analysis result.

[0209] In one embodiment, the emotional state analysis module 50 is specifically used to:

[0210] Based on the emotion polarity and emotion intensity in the emotion feature, generating a quantitative value of the emotion feature;

[0211] Analyzing the behavior frequency and the degree of behavior change in the behavior pattern to generate a quantitative value of the behavior pattern;

[0212] Generate a quantitative value of the event change trend according to the event impact intensity and the event's impact coefficient on the user's emotion;

[0213] The emotional state index is generated by performing weighted analysis on the quantitative values ​​of emotional characteristics, behavioral patterns and event change trends.

[0214] In one embodiment, the user emotion feedback collection module 60 is specifically used to:

[0215] Collect user-generated text content from online platforms;

[0216] Extracting sentiment polarity and sentiment intensity from the text content based on a sentiment analysis module, and generating sentiment data according to the sentiment polarity and sentiment intensity;

[0217] The emotional data is combined with the user's behavior pattern and historical emotional data, and a comprehensive analysis is performed to generate the user emotional feedback.

[0218] In one embodiment, the comprehensive emotion analysis module 70 is specifically used to:

[0219] Analyzing the deviation between the emotional state indicator and the user's emotional feedback;

[0220] A sentiment analysis result is generated based on the deviation, and the sentiment analysis result is used to reflect the degree of sentiment fluctuation or the sentiment stability of the user.

[0221] In one embodiment, the sentiment analysis module 20 is specifically used to:

[0222] Collect historical data and corresponding sentiment analysis results;

[0223] Based on the historical data and the corresponding sentiment analysis results, a sentiment analysis model is trained and generated, and the sentiment analysis model is used to perform natural language processing and sentiment analysis on text data.

[0224] In one embodiment, the emotional state analysis module 50 is specifically used to:

[0225] Set warning thresholds for emotional state indicators;

[0226] When the emotional state indicator reaches the warning threshold, generating warning information;

[0227] The warning information is sent to the business processing end.

[0228] In one embodiment, the output generation module 80 is specifically configured to:

[0229] Obtain risk preference information and expected results from target users;

[0230] Generate personalized output content for the target user based on the risk preference information, expected results, and sentiment analysis results;

[0231] The personalized output content is sent to the target user.

[0232] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external user terminal through a network connection. When the computer program is executed by the processor, it realizes the functions or steps of the service side of an information processing method based on sentiment analysis.

[0233] In one embodiment, a computer device is provided. The computer device may be a user terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the user side of a method for processing information based on sentiment analysis

[0234] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0235] Collect data and information, including text data, user behavior data and event information;

[0236] Performing natural language processing and sentiment analysis on the text data to identify sentiment features of the text data;

[0237] Analyze the user behavior data to determine the user's behavior pattern;

[0238] Analyze the event information to determine the event change trend;

[0239] Determining emotional state indicators based on the emotional characteristics, behavioral patterns, and event change trends;

[0240] Monitor information on online platforms and obtain user sentiment feedback;

[0241] Comprehensively analyzing the emotional state indicators and user emotional feedback to generate emotional analysis results;

[0242] A target output content is generated based on the emotion analysis result to respond to the user's emotional state.

[0243] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0244] Collect data and information, including text data, user behavior data and event information;

[0245] Performing natural language processing and sentiment analysis on the text data to identify sentiment features of the text data;

[0246] Analyze the user behavior data to determine the user's behavior pattern;

[0247] Analyze the event information to determine the event change trend;

[0248] Determining emotional state indicators based on the emotional characteristics, behavioral patterns, and event change trends;

[0249] Monitor information on online platforms and obtain user sentiment feedback;

[0250] Comprehensively analyzing the emotional state indicators and user emotional feedback to generate emotional analysis results;

[0251] A target output content is generated based on the emotion analysis result to respond to the user's emotional state.

[0252] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the user side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0253] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0254] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0255] It should be noted that if software tools or components other than those of the Company appear in the embodiments of the present application, they are only used for illustration and do not represent actual use. The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above-mentioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the above-mentioned embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An information processing method based on sentiment analysis, characterized in that: The following steps are involved: Collect data and information, including text data, user behavior data and event information; Performing natural language processing and sentiment analysis on the text data to identify sentiment features of the text data; Analyze the user behavior data to determine the user's behavior pattern; Analyze the event information to determine the event change trend; Determining emotional state indicators based on the emotional characteristics, behavioral patterns, and event change trends; Monitor information on online platforms and obtain user sentiment feedback; Comprehensively analyzing the emotional state indicators and user emotional feedback to generate emotional analysis results; A target output content is generated based on the emotion analysis result to respond to the user's emotional state.

2. The information processing method based on sentiment analysis according to claim 1, characterized in that: Based on the emotional characteristics, behavior patterns and event change trends, determine emotional state indicators, including: Based on the emotion polarity and emotion intensity in the emotion feature, generating a quantitative value of the emotion feature; Analyzing the behavior frequency and the degree of behavior change in the behavior pattern to generate a quantitative value of the behavior pattern; Generate a quantitative value of the event change trend according to the event impact intensity and the event's impact coefficient on the user's emotion; The emotional state index is generated by performing weighted analysis on the quantitative values ​​of emotional characteristics, behavioral patterns and event change trends.

3. The information processing method based on sentiment analysis according to claim 1, characterized in that: Monitor information on online platforms and obtain user sentiment feedback, including: Collect user-generated text content from online platforms; Extracting sentiment polarity and sentiment intensity from the text content based on a sentiment analysis module, and generating sentiment data according to the sentiment polarity and sentiment intensity; The emotional data is combined with the user's behavior pattern and historical emotional data, and a comprehensive analysis is performed to generate the user emotional feedback.

4. The information processing method based on sentiment analysis as claimed in claim 1, characterized in that: Comprehensively analyzing the emotional state indicators and user emotional feedback to generate emotional analysis results, including: Analyzing the deviation between the emotional state indicator and the user's emotional feedback; A sentiment analysis result is generated based on the deviation, and the sentiment analysis result is used to reflect the degree of sentiment fluctuation or the sentiment stability of the user.

5. The information processing method based on sentiment analysis according to claim 1, characterized in that: Before performing natural language processing and sentiment analysis on the text data and identifying the sentiment features of the text data, the method further includes: Collect historical data and corresponding sentiment analysis results; Based on the historical data and the corresponding sentiment analysis results, a sentiment analysis model is trained and generated, and the sentiment analysis model is used to perform natural language processing and sentiment analysis on text data.

6. The information processing method based on sentiment analysis according to claim 1, characterized in that: After determining the emotional state index based on the emotional characteristics, behavior patterns and event change trends, it also includes: Set warning thresholds for emotional state indicators; When the emotional state indicator reaches the warning threshold, generating warning information; The warning information is sent to the business processing end.

7. The information processing method based on sentiment analysis according to claim 1, characterized in that: Generating target output content for responding to the user's emotional state based on the emotion analysis result includes: Obtain risk preference information and expected results from target users; Generate personalized output content for the target user based on the risk preference information, expected results, and sentiment analysis results; The personalized output content is sent to the target user.

8. An information processing device based on sentiment analysis, characterized in that: The information processing device based on sentiment analysis comprises: A data collection module, used to collect data and information, including text data, user behavior data and event information; A sentiment analysis module, used to perform natural language processing and sentiment analysis on the text data to identify sentiment features of the text data; A user behavior analysis module, used to analyze the user behavior data and determine the user's behavior pattern; An event analysis and trend prediction module is used to analyze the event information and determine the event change trend; An emotional state analysis module, used to determine emotional state indicators based on the emotional characteristics, behavior patterns and event change trends; User emotional feedback collection module, used to monitor the information of online platforms and obtain user emotional feedback; The comprehensive emotion analysis module is used to comprehensively analyze the emotion state indicators and user emotion feedback to generate emotion analysis results; The output generation module is used to generate target output content for responding to the user's emotional state based on the emotion analysis result.

9. A computer device, characterized in that: The computer device includes a memory, a processor, and an information processing program based on sentiment analysis stored in the memory and executable on the processor. When the information processing program based on sentiment analysis is executed by the processor, the steps of the information processing method based on sentiment analysis as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores an information processing program based on sentiment analysis, and when the information processing program based on sentiment analysis is executed by the processor, the steps of the information processing method based on sentiment analysis as described in any one of claims 1 to 7 are implemented.

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