Artificial intelligence-based sentiment analysis system for monitoring social media

Abstract

An artificial intelligence-driven sentiment analysis system for social media monitoring, comprising: a data collection module configured to retrieve user-generated content from at least one social media platform via application programming interfaces (APIs); a preprocessing module configured to process and clean the collected data by removing irrelevant content, tokenizing the text, removing stop words, and assigning sentiment indicators to emojis, slang, and abbreviations; a sentiment analysis engine comprising one or more deep learning models, wherein the deep learning models include recurrent neural networks (RNNs), long-short-term memory networks (LSTM), and transformer-based models configured to analyze the preprocessed data and classify the content into one or more sentiment categories; an emotion recognition module configured to detect emotional tones within the content and classify them into predefined emotional categories; a real-time data processing unit configured to continuously monitor social media content and provide real-time insights into moods and emotions; a feedback loop module configured to update and refine the deep learning models through reinforcement learning based on new data inputs and sentiment classification results; and a user interface module configured to visualize sentiment and emotion analysis results in real time, including heatmaps, sentiment trend analysis, and emotional breakdowns.

Description

Field of the invention:

[0001] The present invention relates to the field of data analysis and artificial intelligence (AI), in particular the use of AI techniques for sentiment analysis in social media monitoring. In particular, it relates to a system for real-time detection and analysis of user sentiments on various social media platforms. It enables the processing and categorization of these sentiments to enable a better understanding of public opinion, trends, and brand perception. Background of the invention:

[0002] Social media platforms have become a central source of information and communication for individuals and organizations alike. With billions of active users, these platforms generate vast amounts of data daily. While this data offers opportunities for various applications, it also presents challenges, particularly in understanding the sentiment behind social media posts, comments, and interactions. Sentiment analysis, which involves determining whether the opinions expressed are positive, negative, or neutral, has gained significant traction in recent years. Traditional methods of sentiment analysis often rely on simple rule-based approaches or basic machine learning techniques, which may lack accuracy and fail to capture the nuanced and dynamic nature of human sentiment, especially in the context of online interactions.

[0003] Existing sentiment analysis tools often struggle with the context-specific nuances of social media language, including slang, emojis, abbreviations, and evolving terminologies. They may also lack the scalability to process the massive amounts of real-time data generated by social media platforms, especially during critical events such as product launches, political campaigns, or crises. Therefore, there is a need for an advanced, scalable, and accurate sentiment analysis system that overcomes these limitations and leverages artificial intelligence to provide deeper insights into public sentiment.

[0004] The increasing amount of user-generated content on social media platforms has sparked growing interest in sentiment analysis technologies. These technologies enable companies, organizations, and researchers to gain valuable insights into public opinion, monitor brand reputation, track political sentiment, and even predict market trends. Sentiment analysis, also known as opinion mining, describes the process of determining whether a text expresses a positive, negative, or neutral sentiment and, in some cases, identifying underlying emotions such as joy, anger, or sadness. The ability to perform real-time sentiment analysis across large volumes of social media content has become a sought-after capability and has driven significant advances in natural language processing (NLP) and machine learning (ML).While existing sentiment analysis solutions are useful, they have some limitations that affect their effectiveness, accuracy, and scalability.

[0005] Early sentiment analysis systems were based on simple rule-based approaches that used predefined rule sets or lexicons to classify sentiment in text. These systems often used lists of positive and negative words with predefined rules to detect mood swings or emotional tones. While such rule-based approaches were effective in simple cases, they quickly proved inadequate given the complexity and diversity of language on social media platforms. The presence of slang, abbreviations, emojis, and sarcasm in online communication, as well as the constant evolution of language, made rule-based systems unreliable over time. Furthermore, these systems often struggled with contextual nuances, as the meaning of a word could change depending on the surrounding text.For example, the word "sick" could mean something positive (as in "That movie was awesome!") or negative (as in "I feel sick today"), and traditional rule-based systems couldn't distinguish between these contrasting contexts. As a result, rule-based systems were often inaccurate and couldn't capture the full complexity of user sentiment.

[0006] With the rise of machine learning, more sophisticated approaches to sentiment analysis emerged. Machine learning models, particularly supervised learning methods, are trained on labeled datasets to classify text based on its sentiment. These models learn to recognize patterns in text that correlate with different sentiment categories. One of the first breakthroughs in sentiment analysis was the use of support vector machines (SVMs) and naive Bayes classifiers, which could process text as a set of features such as word frequencies or n-grams and make predictions based on them. Although these machine learning models represented an improvement over rule-based approaches, they still had significant drawbacks. First, they often required large amounts of labeled data for training, which could be costly and time-consuming to create.Furthermore, these models struggled with ambiguity and were unable to understand the deeper meaning of text. For example, a statement like "I love this product, but it's too expensive" might be incorrectly classified as positive by an SVM model because the word "love" is associated with positive sentiment, even though the overall sentiment of the statement is negative due to the mention of high cost.

[0007] To solve these problems, there is a growing need for advanced, AI-driven sentiment analysis systems that can provide accurate, real-time insights into social media content while overcoming the limitations of existing solutions. Summary of the invention:

[0008] The present invention provides an AI-powered sentiment analysis system for social media monitoring. It utilizes advanced natural language processing (NLP) techniques, deep learning models, and real-time data processing. The system captures, analyzes, and categorizes the sentiments expressed in user-generated content on various social media platforms such as Twitter, Facebook, Instagram, and LinkedIn.

[0009] The system includes a data collection module that interacts with social media APIs to capture posts, comments, and interactions from public feeds. The collected data is then preprocessed, filtering out irrelevant information and cleaning text data for analysis. Sentiment analysis is performed using a combination of deep learning models, including recurrent neural networks (RNNs), long-short-term memory networks (LSTMs), and transformer-based models such as BERT (Bidirectional Encoder Representations from Transformers). These models are trained and optimized on large datasets to detect context, tone, and sentiment, even in slang, emojis, or ambiguous expressions.

[0010] The sentiment analysis engine classifies content into positive, negative, and neutral sentiment categories. In addition to sentiment classification, the system uses emotion detection techniques to capture more detailed emotional insights such as joy, anger, surprise, and sadness. The results are displayed in a user interface (UI) that provides visual representations such as sentiment heatmaps, trend analysis, and the temporal distribution of emotions. SHORT DESCRIPTION OF THE FIGURE

[0011] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. Figure 1 shows a block diagram of an artificial intelligence-based sentiment analysis system for social media monitoring.

[0012] Those skilled in the art will also appreciate that the elements in the drawings are shown for convenience and are not necessarily to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may be represented in the drawings by conventional symbols. The drawing may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawing with details that would be readily apparent to those skilled in the art from the present description. Detailed description of the invention

[0013] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and a clear description thereof. However, the scope of the invention is not limited thereby. Changes and further modifications to the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to one skilled in the art to which the invention pertains.

[0014] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

[0015] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the language "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not refer to the same embodiment.

[0016] The terms "comprises," "comprising," or variations thereof are intended to be non-exclusive inclusion. A process or method that includes a list of steps includes not only those steps, but may also include additional steps not expressly listed or inherent in the process or method. Likewise, the statement "comprises" for one or more devices, subsystems, elements, structures, or components does not exclude, without further limitation, the existence of other devices, subsystems, elements, structures, or components, or of additional devices, subsystems, elements, structures, or components.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. The systems, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0018] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0019] In Fig.1, a block diagram of an AI-powered sentiment analysis system for social media monitoring is shown. The system 100 includes: a data acquisition module (102) that retrieves user-generated content via application programming interfaces (APIs) from at least one social media platform; a preprocessing module (104) that processes and cleans the acquired data by removing irrelevant content, tokenizing text, and mapping stop words, as well as emojis, slang, and abbreviations, to sentiment indicators; a sentiment analysis engine (106) that includes one or more deep learning models, including recurrent neural networks (RNNs), long-short-term memory networks (LSTMs), and transformer-based models, that analyze the preprocessed data and classify the content into one or more sentiment categories; an emotion detection module (108) that detects emotional tones in the content and maps them to predefined emotional categories;a real-time data processing unit (110) configured to continuously monitor social media content and provide real-time insights into sentiment and emotion; a feedback loop module (112) configured to update and refine the deep learning models through reinforcement learning based on new data inputs and sentiment classification results; and a user interface module (114) configured to visualize the results of sentiment and emotion analysis in real time, including heatmaps, sentiment trend analysis, and emotional breakdowns.

[0020] In one embodiment, the data collection module is further configured to retrieve data from multiple social media platforms simultaneously, including at least one of Twitter, Facebook, Instagram, and LinkedIn, and aggregate the retrieved data for unified sentiment and emotion analysis.

[0021] In one embodiment, the sentiment analysis engine comprises a BERT (Bidirectional Encoder Representations from Transformers) model that has been pre-trained on a large corpus of social media-specific text data and optimized for sentiment classification in social media content.

[0022] In one embodiment, the mood categories include at least one of the categories "positive", "negative", and "neutral", and the emotion recognition module is configured to classify the detected emotions into predefined emotional states, including happiness, sadness, anger, fear, and surprise.

[0023] In one embodiment, the feedback loop module is configured to leverage user feedback and data-driven performance metrics to adjust the weights and parameters of the deep learning models to improve the accuracy of sentiment and emotion classification over time.

[0024] In one embodiment, the preprocessing module comprises a context analyzer configured to detect and disambiguate context-sensitive terms or phrases in the social media content. This ensures accurate sentiment classification even when terms have different meanings depending on the context.

[0025] In one embodiment, the user interface module is configured to provide the user with filtering options to select specific keywords, hashtags, or user groups and to display sentiment trends and emotional insights associated with the selected parameters.

[0026] In one embodiment, the real-time computing unit is configured to process high-throughput, high-latency data streams. This ensures analysis of social media content as it is posted, with minimal delay in sentiment and emotion results.

[0027] In one embodiment, the sentiment analysis engine comprises an ensemble model that combines multiple deep learning architectures, including RNNs, LSTMs, and transformer-based models, in parallel to improve the accuracy of sentiment classification across different social media platforms.

[0028] In one embodiment, the sentiment analysis engine further comprises a sentiment scoring processing unit configured to assign a confidence score to each sentiment classification result indicating the degree of certainty in analyzing the sentiment of the content.

[0029] The invention relates to an AI-based sentiment analysis system for real-time social media monitoring. It leverages advanced deep learning models and natural language processing (NLP) techniques to classify and interpret the sentiments expressed in user-generated content. The system employs a set of key modules that work together to ensure accurate sentiment and emotion detection, real-time data processing, and insightful visualizations for users. The following detailed description of the system includes its various components and underlying technology, emphasizing the deep learning-based architecture.

[0030] The system is based on the data collection module, which retrieves user-generated content from social media platforms via application programming interfaces (APIs). This content is retrieved in real time from various sources such as Twitter, Facebook, Instagram, and LinkedIn and can be scaled and monitored across multiple platforms simultaneously. The collected data includes text posts, comments, likes, shares, and other forms of interaction that reflect users' opinions and emotions. The preprocessing module cleans and prepares this raw data for analysis. It removes irrelevant content, processes missing data, tokenizes the text, and filters out stop words—common words that do not contribute to sentiment analysis, such as "the," "and," or "is."Additionally, the preprocessing unit assigns emojis, slang, and abbreviations commonly used in social media to a predefined set of sentiment indicators, ensuring that non-standardized language is interpreted correctly. This helps the system account for the informal and diverse nature of social media communication.

[0031] Once the data is preprocessed, it is passed to the sentiment analysis engine, which forms the heart of the system. The sentiment analysis engine is based on advanced deep learning techniques, specifically recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer-based models trained to understand the structure and context of the language used in social media content. These models work together to process the preprocessed data and classify the text into one or more sentiment categories, such as positive, negative, or neutral. This attention mechanism also helps resolve ambiguities, for example, by distinguishing between the positive or negative connotations of words based on their surrounding context.By leveraging such models, the sentiment analysis engine achieves a high level of accuracy, even with ambiguous or complex social media language.

[0032] In addition to sentiment classification, the system includes an emotion recognition module that detects and categorizes emotions expressed in social media content. This module uses a combination of deep learning models, including convolutional neural networks (CNNs) and fine-tuned BERT models, to detect specific emotional states such as joy, sadness, anger, fear, and surprise. Emotion recognition is more complex than simple sentiment classification because the system must identify subtle emotional cues within the text. Sarcasm, irony, or implicit emotional expressions, for example, pose a significant challenge to conventional sentiment analysis models. However, through a multi-layered approach with emotion-specific training datasets and attention mechanisms, the emotion recognition module is able to detect these nuanced expressions and accurately categorize them.This enables the system to analyze social media content more comprehensively, gaining insights not only into whether content is positive or negative, but also into the emotions underlying user sentiments.

[0033] The real-time data processing unit ensures that the entire process—from data collection to sentiment and emotion classification—runs in near real time. It is designed to process high-throughput data streams and large volumes of social media posts with minimal delay. It is optimized for scalability and can continuously monitor and analyze content as it is published across multiple platforms. The system processes data in real time, providing immediate feedback and analysis on trends, emerging issues, or sentiment shifts. This real-time processing is particularly valuable in scenarios such as crisis management, political sentiment analysis, and live event monitoring, where immediate insights are critical.

[0034] The feedback loop module is another critical component that allows the system to learn and improve over time. As the system processes more data, it receives feedback on the accuracy of its sentiment and emotion classifications. This feedback is used to retrain the models and adjust their parameters, improving their ability to accurately classify sentiments and detect emotions. The feedback loop utilizes reinforcement learning techniques, where the model continuously refines its predictions by adjusting user corrections and real-world performance metrics. This self-learning mechanism helps the system keep pace with evolving language, new slang, and changing social media trends, ensuring it remains effective and accurate in a dynamic environment.

[0035] The system's user interface module presents the results of sentiment and emotion analysis using visual tools and dashboards. These visualizations include heat maps that depict the overall sentiment distribution, trend analysis charts that demonstrate sentiment changes over time, and emotional breakdowns that categorize the emotions detected in content. Users can interact with the dashboard to filter results by specific keywords, hashtags, or user groups, allowing them to drill down into specific topics, events, or demographics. The intuitive interface provides both tech-savvy and non-technical users with clear insights into social media sentiment and emotions. The system also includes predictive analytics capabilities that allow users to predict sentiment trends based on historical data and current patterns.

[0036] The sentiment scoring processing unit assigns a confidence score to each sentiment classification, indicating the system's confidence in the accuracy of the analysis. The confidence score reflects the model's confidence in classifying a given piece of content as positive, negative, or neutral. A higher score indicates higher confidence, while a lower score indicates a less certain sentiment classification. This module is particularly useful in ambiguous cases where the sentiment of the content may be unclear or mixed. The system can also flag these cases for further human review or additional model training.

[0037] The entire system works as an integrated whole, with each module feeding into the next, creating a seamless and robust sentiment and emotion analysis process. At its core, the system leverages deep learning models and real-time processing to deliver accurate, scalable, and actionable insights from social media content. The feedback loop ensures the system remains adaptable and continuously improves its performance based on new data and user feedback. By integrating sentiment and emotion detection, predictive analytics, and real-time processing, the system provides companies with a comprehensive social media monitoring tool, enabling deep insights into public opinion, brand perception, and emerging social trends.

[0038] The AI-powered sentiment analysis system for social media monitoring consists of several interconnected components that work seamlessly together to enable real-time data analysis and actionable insights. At the core of the system is the data collection module, which interacts with social media platform application programming interfaces (APIs) to retrieve posts, comments, and interactions from various users. These social media APIs can provide both public and private data feeds, depending on user permissions and platform regulations.

[0039] The collected data first undergoes a preprocessing phase. This involves normalizing the text to remove irrelevant elements such as advertisements, non-textual content, and spam. The text data is then tokenized, lowercased, and cleaned of stop words, punctuation, and other noise. This phase also detects emojis, slang, and abbreviations and assigns them to their corresponding meanings or sentiment indicators to ensure that no emotional context is lost in translation.

[0040] After preprocessing is complete, the system's deep learning-based sentiment analysis engine is deployed to classify the sentiments expressed in the text. The engine leverages various machine learning models, including RNNs and LSTMs, which are well-suited for capturing the sequential nature of speech, especially in social media content whose context can change rapidly. These models are complemented by transformer-based architectures such as BERT, which are pre-trained on large corpora of diverse text data and then optimized on specific datasets of social media speech and expressions.

[0041] Sentiment classification is based on the identified patterns in the text, categorizing it as positive, negative, or neutral. Additionally, emotion detection techniques are used to identify specific emotions in the content, such as joy, anger, sadness, or fear. These emotion categories are then assigned to the corresponding emotional labels, creating a multidimensional view of user sentiment.

[0042] The results of the sentiment and emotion analysis are visualized in an intuitive user interface. It shows sentiment trends over time, heatmaps of positive and negative sentiment concentrations, and an emotional breakdown of the content. This interface allows users to interact with the data, filter by specific keywords or hashtags, and monitor sentiment changes in real time.

[0043] The AI-powered sentiment analysis system is scalable and capable of processing large amounts of real-time data from various social media platforms. It provides valuable insights for various use cases, including brand reputation management, market research, political analysis, and social trend monitoring. The system's ability to adapt to new linguistic patterns and continuously improve its accuracy makes it an indispensable tool for companies, organizations, and individuals seeking to understand public sentiment on social media platforms.

[0044] The drawings and the foregoing description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the order shown; nor do all actions necessarily have to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0045] Advantages, further benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, advantages, solutions to problems, and any components that may result in or enhance an advantage, benefit, or solution are not to be construed as critical, required, or essential features or components of any or all of the claims. REFERENCES 100 An artificial intelligence-based sentiment analysis system for social media monitoring. 102 Data acquisition module 104 Preprocessing module 106 Sentiment Analysis Engine 108 Emotion Recognition Module 110 Real-time data processing unit 112 Feedback loop module 114 User interface module

Claims

[1] An artificial intelligence-driven sentiment analysis system for social media monitoring, comprising: a data collection module configured to retrieve user-generated content from at least one social media platform via application programming interfaces (APIs); a preprocessing module configured to process and clean the collected data by removing irrelevant content, tokenizing the text, removing stop words, and assigning sentiment indicators to emojis, slang, and abbreviations; a sentiment analysis engine comprising one or more deep learning models, wherein the deep learning models include recurrent neural networks (RNNs), long-short-term memory networks (LSTM), and transformer-based models configured to analyze the preprocessed data and classify the content into one or more sentiment categories; an emotion recognition module configured to detect emotional tones within the content and classify them into predefined emotional categories; a real-time data processing unit configured to continuously monitor social media content and provide real-time insights into moods and emotions; a feedback loop module configured to update and refine the deep learning models through reinforcement learning based on new data inputs and sentiment classification results; and a user interface module configured to visualize sentiment and emotion analysis results in real time, including heatmaps, sentiment trend analysis, and emotional breakdowns. [2] The system of claim 1, wherein the sentiment analysis engine comprises a BERT (Bidirectional Encoder Representations from Transformers) model pre-trained on a large corpus of social media-specific text data and optimized for sentiment classification in social media content. [3] The system of claim 1, wherein the mood categories include at least one of positive, negative, and neutral, and wherein the emotion recognition module is configured to classify the detected emotions into predefined emotional states, including happiness, sadness, anger, fear, and surprise. [4] The system of claim 1, wherein the feedback loop module is configured to utilize user feedback and data-driven performance metrics to adjust the weights and parameters of the deep learning models, thereby improving the accuracy of sentiment and emotion classification over time. [5] The system of claim 1, wherein the preprocessing module comprises a context analyzer configured to detect and disambiguate context-sensitive terms or phrases in the social media content, thereby ensuring accurate sentiment classification even when terms have different meanings depending on the context. [6] The system of claim 1, wherein the user interface module is configured to provide the user with filtering options for selecting specific keywords, hashtags, or user groups and to display sentiment trends and emotional insights associated with the selected parameters. [7] The system of claim 1, wherein the real-time data processing unit is configured to process high-throughput, high-latency data streams, thereby ensuring the analysis of social media content at the time of posting with minimal delay in the results of sentiment and emotions. [8] The system of claim 1, wherein the sentiment analysis engine further comprises a sentiment evaluation processing unit configured to assign to each sentiment classification result a confidence value indicating the degree of certainty in analyzing the sentiment of the content.

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