Network public opinion monitoring method and system

Abstract

The invention provides a network public opinion monitoring method and system, and relates to the technical field of data processing, and the network public opinion monitoring method comprises the steps: obtaining network public opinion data according to a public opinion event; performing public opinion guide extraction on the network public opinion data to obtain public opinion initiation data; acquiring public opinion initiation source data according to the public opinion initiation data; according to the public opinion triggering source data, the public opinion triggering data and the expected target, the event updating data of the public opinion event are regulated and controlled, and event public relation data are generated for whole-network publishing. According to the network public opinion monitoring method and system provided by the invention, effective supervision and regulation on the development trend of public opinions can be realized.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for monitoring online public opinion. Background Technology

[0002] Online public opinion monitoring refers to the act of collecting, analyzing, and visualizing information across the entire internet through technical means to generate hot topic statistics and dynamic monitoring. Its core objective is to identify online public opinion trends and generate statistical reports using technologies such as natural language processing and data mining.

[0003] In the current process of online public opinion supervision, once a public opinion incident occurs, the monitoring of public opinion on various online platforms is insufficient, which easily leads to the leakage of public opinion data. Moreover, once a public opinion incident occurs, the public opinion public relations related to the incident cannot fully implement public opinion prevention and control measures, which leads to the public opinion incident continuing to develop in a vicious cycle after the public opinion public relations are done, which is not conducive to public opinion management. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method and system for monitoring online public opinion, which solves the problem that once public opinion occurs, the monitoring of public opinion on various online platforms is insufficient, and public opinion data is easily leaked; moreover, once public opinion occurs, the public opinion public relations of related public opinion events cannot fully implement public opinion prevention and control measures, thus causing public opinion to continue to develop in a vicious cycle after the public opinion public relations efforts.

[0005] To achieve the above and other related objectives, the present invention provides a method for monitoring online public opinion, comprising: acquiring online public opinion data based on public opinion events; performing public opinion guidance extraction on the online public opinion data to acquire public opinion triggering data; acquiring public opinion triggering source data based on the public opinion triggering data; and regulating the event update data of the public opinion event based on the public opinion triggering source data, the public opinion triggering data, and the expected goals to generate event public relations data for publication across the entire network.

[0006] In one embodiment of the present invention, obtaining online public opinion data based on public opinion events includes: extracting keywords from the public opinion events to obtain public opinion keywords. Search and public opinion keywords Semantically related keywords as public opinion-related terms ; Keywords related to public opinion Keywords related to public opinion Perform platform conversion to generate platform expressions corresponding to the platform language of the respective network platform. ; through public opinion keywords Keywords related to public opinion platform expressions Conduct public opinion searches on relevant online platforms to obtain online public opinion data.

[0007] In one embodiment of the present invention, public opinion keywords are... Keywords related to public opinion Perform platform conversion to generate platform expressions corresponding to the platform language of the respective network platform. This includes: keywords related to public opinion. Keywords related to public opinion Perform word segmentation to obtain a word data set. The word and phrase data set includes at least one of single-character data and word data; based on the word and phrase data set Word data in Find the first set of terms used by the network platform. ,in, This indicates a subset of terms used on the first platform. , Represents data for each word Corresponding terms used on the primary platform; select the terminology set used on the primary platform. A subset of terms used on several first-tier platforms And select multiple subsets of terms used on the first platform The corresponding first platform terminology Perform a combination, then combine it with other word data. A second combination is performed to generate the first platform's expressive words, wherein each combination includes at least one subset of the first platform's vocabulary. The corresponding first platform terminology ;Keywords for public opinion Keywords related to public opinion Semantic transformation is performed to obtain semantic information; based on the semantic information, the second platform expression term corresponding to the network platform is found; based on the first and second platform expression terms, the platform expression term corresponding to the platform language of the corresponding network platform is generated. .

[0008] In one embodiment of the present invention, public opinion guidance extraction is performed on online public opinion data to obtain public opinion triggering data, including: extracting dialogue data from the online public opinion data, the dialogue data including question data and answer data; sorting the dialogues according to the correspondence between the answer data and the question data to obtain related dialogue distribution data; extracting dialogue strings from the related dialogue distribution data to obtain multiple related dialogue strings, the related dialogue strings including question data and answer data that are hierarchically related; and performing related sentiment analysis on the question data and answer data in the related dialogue strings to obtain related sentiment classification and related sentiment feedback trends, which are used as public opinion triggering data.

[0009] In one embodiment of the present invention, dialogue sorting is performed based on the correspondence between answer data and question data to obtain associated dialogue distribution data, including: calculating the correspondence between each answer data and each question data to obtain a correspondence set. ; Set the corresponding degree Each correspondence in Correspondence threshold Comparison: When Correspondence Greater than the corresponding degree threshold When the value exceeds the corresponding degree threshold, it will be greater than the threshold value. Correspondence The corresponding answer data serves as the answer to the question data, generating a single dialogue; the single dialogue is then evaluated and categorized to determine the corresponding topic focus. and the direction of a single conversation correlation between ; relevance Greater than the set correlation threshold The same topic points to By comparing the content association levels between corresponding individual conversations, we can obtain the topic references for each conversation. The relationships between individual dialogues under the corresponding dimension are analyzed, including hierarchical relationships and parallel hierarchical relationships. The individual dialogues corresponding to the relationships are sorted according to the corresponding hierarchical arrangement order to obtain the distribution data of the related dialogues.

[0010] In one embodiment of the present invention, the correlation degree is... Greater than the set correlation threshold The same topic points to By comparing the content association levels between corresponding individual conversations, we can obtain the topic references for each conversation. The relationships between individual conversations within the corresponding dimension include: linking each individual conversation to the relevant topic. Semantic transformation is performed on the dimensions to obtain the topic content; causal relationships are compared between the topic content corresponding to each individual dialogue to obtain the causal correlation degree between the individual dialogues. Based on the relevance of a single dialogue Relevance Corresponding association weight and causal correlation The correlation level index was calculated. ,in, When related hierarchical indicators Greater than the indicator threshold When this happens, a hierarchical relationship is established between the corresponding individual dialogues; when related hierarchical indicators... Less than the indicator threshold In this case, a parallel hierarchical relationship is established between the corresponding individual dialogues.

[0011] In one embodiment of the present invention, sentiment analysis is performed on question and answer data in related dialogue strings to obtain sentiment classification and sentiment feedback trends, which are used as data to trigger public opinion. This includes: performing sentiment detection on all related dialogue strings under the same topic-oriented dimension to obtain sentiment elements and element evaluations; organizing and classifying all sentiment elements to obtain sentiment classification classes and a set of sentiment elements corresponding to each sentiment classification class; and classifying the sentiment elements according to the degree value of the element evaluations corresponding to the sentiment elements. and the correlation factors of the emotional elements at the level of the dialogue. The associated emotional feedback value of the emotional element is calculated. Based on the associated sentiment feedback value, the associated sentiment feedback trend is obtained.

[0012] In one embodiment of the present invention, the public opinion triggering data includes related sentiment classification, related sentiment feedback trends, and related dialogue strings; based on the public opinion triggering data, obtaining public opinion triggering source data includes: according to the related sentiment classification, performing dialogue search on the related dialogue strings to obtain question data and answer data arranged in the order of the related dialogue strings; according to the related sentiment feedback trends, performing upstream data search on the related dialogue strings corresponding to the related sentiment classification to obtain the initial data corresponding to the related sentiment feedback trends, which is used as the public opinion triggering source data, wherein the initial data includes one of initial question data and initial answer data.

[0013] In one embodiment of the present invention, based on the public opinion source data, public opinion triggering data, and expected goals, the event update data of the public opinion event is regulated to generate event public relations data for full online publication. This includes: performing data transformation on the related sentiment feedback trend and related sentiment classification of the public opinion source data to obtain the public opinion guidance of the public opinion source data; comparing the public opinion guidance with the expected guidance corresponding to the expected goals, finding the public opinion guidance closest to the expected guidance, and calculating the deviation degree of the public opinion guidance relative to the expected guidance; adjusting the related sentiment feedback trend and related sentiment classification according to the deviation degree to obtain the expected sentiment trend and expected sentiment classification; adjusting the public opinion source data according to the deviation degree, expected sentiment trend, and expected sentiment classification to obtain expected triggering source data; generating expected event data based on the expected triggering source data; and regulating the event update data of the public opinion event according to the expected event data to generate event public relations data for full online publication.

[0014] To achieve the above and other related objectives, the present invention also provides a network public opinion monitoring system, comprising: an acquisition unit for acquiring network public opinion data based on public opinion events; an extraction unit for performing public opinion-oriented extraction on the network public opinion data to acquire public opinion triggering data; a query unit for acquiring public opinion triggering source data based on the public opinion triggering data; and a publishing unit for regulating the event update data of the public opinion event based on the public opinion triggering source data, the public opinion triggering data, and the expected goals, generating event public relations data for publication across the entire network.

[0015] As described above, the online public opinion monitoring method and system of the present invention have the following beneficial effects: By using public opinion events to set diversified search keywords adapted to different online platforms, the acquisition of online public opinion data on different online platforms can be controlled, which can effectively improve the sufficiency of retrieval on each online platform and ensure the retrieval coverage of online public opinion data on each online platform. Moreover, by extracting public opinion guidance from the acquired online public opinion data, it is possible to quickly lock various public opinion guidance and public opinion triggering source data. Furthermore, in the process of further controlling public opinion events, the public opinion triggering source data can be transformed based on the expected goal to obtain expected event data corresponding to the expected goal. This allows for corresponding adjustments to the event update data after the evolution of the public opinion event based on the expected event data, and the adjusted data can be published as event public relations data. This enables the control of the evolution trend of public opinion towards the expected goal by utilizing the publication of event public relations, such as strengthening or weakening the impact of public opinion, thereby achieving the monitoring and control of the development trend of public opinion. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the online public opinion monitoring method provided in an embodiment of the present invention.

[0017] Figure 2 The diagram shows a tree structure of associated dialogue distribution data provided in an embodiment of the present invention.

[0018] Figure 3 The diagram shown is a structural block diagram of a network public opinion monitoring system provided in an embodiment of the present invention.

[0019] Figure 4 The diagram shown is a structural schematic of an electronic device according to an embodiment of the present invention.

[0020] Component labeling description: Electronic device 1; Network public opinion monitoring system 11; Memory 12; Processor 13; Acquisition unit 111; Extraction unit 112; Query unit 113; Publishing unit 114. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0022] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0023] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0024] This invention provides a method for monitoring online public opinion. By regulating the acquisition of online public opinion data from different online platforms based on public opinion events, the sufficiency of retrieval from various online platforms can be effectively improved. Furthermore, by extracting public opinion guidance from the acquired online public opinion data, it is possible to quickly identify various public opinion guidance and the source data that triggers public opinion. Then, during the process of public opinion regulation, the updated event data after the evolution of the public opinion event can be adjusted based on the expected goals and published as event public relations data. This allows for the effective regulation of public opinion development by using the publication of event public relations data to control the evolution trend of public opinion towards the expected goals.

[0025] Figure 1 A flowchart of an exemplary embodiment of the online public opinion monitoring method of this application is shown, applied to an online public opinion monitoring system, including steps S10-S40. The following will be combined with... Figure 1 The technical solution of this application will be described in detail below.

[0026] First, execute step S10 to obtain online public opinion data based on the public opinion event.

[0027] When acquiring online public opinion data in an online public opinion monitoring system, since the spread of public opinion events requires a certain number of events, the data can be obtained from various online platforms based on public opinion events that have occurred some time ago.

[0028] In step S10, obtaining online public opinion data based on public opinion events may further include:

[0029] Extracting keywords from public opinion events to obtain public opinion keywords. ;

[0030] Search and public opinion keywords Semantically related keywords as public opinion-related terms ;

[0031] Key words for public opinion Keywords related to public opinion Perform platform conversion to generate platform expressions corresponding to the platform language of the respective network platform. ;

[0032] Through public opinion keywords Keywords related to public opinion platform expressions Conduct public opinion searches on relevant online platforms to obtain online public opinion data.

[0033] When acquiring online public opinion data based on public opinion events, the online public opinion monitoring system first processes the public opinion events to extract keywords from them, thereby obtaining public opinion keywords that reflect the information of the events. Specifically, this can be achieved by breaking down keywords into various types, then filtering them, and performing semantic comparisons with public opinion events to obtain public opinion keywords that match the events. Then, based on the derived public opinion keywords... By performing similar word searches, keywords related to public opinion can be obtained. Semantic related terms for public opinion In order to achieve the goal of utilizing public opinion keywords Keywords related to public opinion To improve the accuracy of online public opinion data searches, it's crucial to consider the potential differences in terminology across various online platforms. Furthermore, to ensure the accuracy of public opinion keywords... Keywords related to public opinion It can be applied to corresponding online platforms, and through the online public opinion monitoring system, it can further realize the analysis of public opinion keywords. Keywords related to public opinion Perform keyword conversion on relevant online platforms to transform public opinion keywords. Keywords related to public opinion Convert to platform-specific terms applicable to the corresponding online platforms This allows for the use of relevant online keywords to search for public opinion. Keywords related to public opinion platform expressions Simultaneously conducting public opinion searches on relevant online platforms allows for a more comprehensive and thorough search of public opinion across these platforms, improving the completeness of online public opinion data when acquired from these platforms.

[0034] Among them, public opinion keywords Keywords related to public opinion Perform platform conversion to generate platform expressions corresponding to the platform language of the respective network platform. It may further include:

[0035] Key words in public opinion Keywords related to public opinion Perform word segmentation to obtain a word data set. The word data set includes at least one of single-character data and word data;

[0036] Based on word and phrase data set Word data in Find the first set of terms used by the network platform. ,in, This indicates a subset of terms used on the first platform. , Represents data for each word The corresponding terminology used on the first platform;

[0037] Select the first platform's vocabulary set A subset of terms used on several first-tier platforms And select multiple subsets of terms used on the first platform The corresponding first platform terminology Perform a combination, then combine it with other word data. A second combination is performed to generate the first platform's expressive words, wherein each combination includes at least one subset of the first platform's vocabulary. The corresponding first platform terminology ;

[0038] Key words in public opinion Keywords related to public opinion Perform semantic transformation to obtain semantic information;

[0039] Find the second platform expression word corresponding to the network platform according to the semantic information;

[0040] Generate the platform expression word corresponding to the platform language of the corresponding network platform according to the first platform expression word and the second platform expression word .

[0041] When converting the public opinion keyword and the public opinion related word into the platform expression word corresponding to the platform language of the corresponding network platform , first split each keyword or related word in the public opinion keyword and the public opinion related word to obtain single-character data and word data. Since there will not only be single-character platform languages in the platform language of the network platform, of course, there will also be word platform languages, and there may be more than one expression form for each platform language. For example, "WeChat" in the chat tool is used as "Mouxin", "Green Bubble", "VX", etc. on a certain network platform. Therefore, after splitting out the word data set , multiple first platform word sets corresponding to the word data (which can be a single-character data or word data) can be obtained. And by constructing the first platform word subset , each first platform word subset may contain multiple first platform word sets .

[0042] When combining word data based on several first platform word subsets corresponding to the network platform in the public opinion event, it can be to first select the first platform words corresponding to multiple first platform word subsets for a first combination, and then, after the first combination, combine with other word data in the word data set for a second combination to generate the first platform expression word. Of course, it can also be to directly combine with other word data in the word data set after selecting the first platform words corresponding to multiple first platform word subsets to obtain the first platform expression word at one time. Among them, when selecting the first platform words corresponding to multiple first platform word subsets , any first platform word in the first platform word subset By combining words from other first-platform words in the same subset, a variety of first-platform expression words can be obtained, ensuring a variety of search keywords when retrieving online public opinion data.

[0043] In addition, to further enhance the diversity of search keywords, the online public opinion monitoring system will further analyze public opinion keywords separately. Keywords related to public opinion After performing corresponding semantic transformation and obtaining the relevant semantic information, further platform terminology queries are conducted based on the derived semantics. This yields second platform expressions corresponding to the online platform. Notably, public opinion keywords are included. Keywords related to public opinion These are words with similar meanings, but their actual meanings have certain differences.

[0044] By utilizing keywords based on public opinion Keywords related to public opinion After word segmentation, the first platform expression words were derived from single-character and word analysis, and public opinion keywords were utilized. Keywords related to public opinion The second platform term, derived from semantic analysis, is used as the platform term for network platform language. This can improve the comprehensiveness of the platform's language conversion and enhance the ability to express vocabulary using the platform. Coverage when conducting public opinion searches.

[0045] Next, through public opinion keywords Keywords related to public opinion platform expressions Conducting public opinion searches on relevant online platforms to obtain online public opinion data may further include:

[0046] Through public opinion keywords Keywords related to public opinion platform expressions Perform data searches on the relevant online platforms to obtain search results.

[0047] Perform comprehensive semantic transformation on each search result to obtain the search semantics;

[0048] Compare the semantic correlation between the search semantics and the corresponding semantics of public opinion events;

[0049] When the semantic relevance between the search semantics and the corresponding semantics of the public opinion event reaches the semantic relevance threshold, the search result data is used as online public opinion data.

[0050] The online public opinion monitoring system uses public opinion keywords Keywords related to public opinion platform expressions In the process of conducting public opinion searches on relevant online platforms, one can first utilize public opinion keywords. Keywords related to public opinion platform expressions Data searches are conducted on relevant online platforms to obtain search results. To address the illusion of irrelevance to search results (where keywords in the online platform database are highly similar to the search terms but unrelated to the actual public opinion event), the semantics of the search results are simplified and summarized after acquisition. Then, a semantic relevance comparison is performed with the semantics corresponding to the public opinion event. Only when the semantic relevance between the search terms and the corresponding public opinion event reaches a threshold is the search result data considered as online public opinion data. This effectively ensures the correspondence between online public opinion data and the public opinion event itself. This facilitates subsequent accurate analysis and identification of data initiating and originating public opinion events based on the online public opinion data.

[0051] Next, step S20 is executed to extract public opinion guidance data from the online public opinion data and obtain data on the triggering of public opinion.

[0052] After acquiring online public opinion data, the online public opinion monitoring system will further extract public opinion guidance from the data to analyze and obtain the data that triggers public opinion. Based on the data that triggers public opinion, the system can identify the corresponding source data of public opinion and use the source data and the data that triggers public opinion to regulate the event update data of public opinion events in order to accurately respond to public opinion public relations crises.

[0053] In step S20, the online public opinion data is subjected to public opinion guidance extraction to obtain public opinion triggering data, which may further include:

[0054] Extract dialogue data from online public opinion data. Dialogue data includes question data and answer data.

[0055] Based on the correspondence between answer data and question data, the dialogues are analyzed to obtain related dialogue distribution data;

[0056] Dialogue strings are extracted from the associated dialogue distribution data to obtain multiple associated dialogue strings, which include hierarchically related question data and answer data.

[0057] Sentiment analysis is performed on the question and answer data in related dialogue strings to obtain sentiment classification and sentiment feedback trends, which can be used as data to trigger public opinion.

[0058] When extracting public opinion guidance from online public opinion data, the online public opinion monitoring system can first extract dialogue data from the data. This extraction can be based on dialogues between different users, yielding corresponding question and answer data for each user. Alternatively, dialogue data can be obtained using language recognition models and / or text dialogue models. After obtaining the dialogue data, further dialogue analysis is performed to determine the correspondence between the question and answer data, resulting in related dialogue distribution data. This addresses situations where answers are given multiple times in real-world dialogues, or where other users have already answered the same question in other dialogues. Dialogue analysis ensures accuracy and correspondence, resulting in a more reasonable distribution of related dialogue data. This related dialogue distribution data can be understood as a tree-like structure establishing the correspondence between question and answer data and their hierarchical order based on different users and topics (including topic direction). Figure 2 The distribution data of associated dialogues in this tree structure is shown.

[0059] Furthermore, after obtaining the associated dialogue distribution data, the dialogue strings within this data are extracted to identify interconnected dialogue strings containing multiple questions and answers. Then, sentiment analysis is performed on each associated topic string to classify sentiment and determine sentiment feedback trends, thus providing data for public opinion events. This sentiment analysis may include analyzing the focus of public opinion on each associated topic string and the attitudes expressed towards that focus, thereby determining the focus category of the corresponding associated topic string and the attitude trends towards that focus during discussion. Of course, the sentiment classification and sentiment feedback trends can also be other categories and trends.

[0060] This includes analyzing the dialogues based on the correspondence between answer data and question data to obtain related dialogue distribution data, which may further include:

[0061] Calculate the correspondence between each answer and each question to obtain the correspondence set. ;

[0062] Correspondence set Each correspondence in Correspondence threshold Comparison:

[0063] When Correspondence Greater than the corresponding degree threshold When the value exceeds the corresponding degree threshold, it will be greater than the threshold value. Correspondence The corresponding answer data serves as the answer to the question data, generating a single dialogue;

[0064] Evaluate and categorize topics from a single conversation to determine the corresponding topic focus. and the direction of a single conversation correlation between ;

[0065] correlation Greater than the set correlation threshold The same topic points to By comparing the content association levels between corresponding individual conversations, we can obtain the topic references for each conversation. The relationships between individual dialogues under the corresponding dimension, including hierarchical relationships and parallel hierarchical relationships;

[0066] The individual dialogues corresponding to the association relationships are sorted according to their respective hierarchical arrangement to obtain the associated dialogue distribution data.

[0067] In the process of analyzing dialogues through the online public opinion monitoring system, the first step is to use answer data to calculate the correspondence between each answer data point and each question data point, thus constructing a correspondence set. When calculating correspondence, a question-and-answer matching model can be used, such as a similarity measurement model, employing algorithms like cosine similarity to calculate the semantic relevance between questions and answers as the correspondence. Alternatively, other correspondence calculation models can be used to calculate the correspondence between each answer and each question. Then, based on the correspondence between answer data... This involves determining the relationships between each individual dialogue, and then using these relationships to construct related dialogue distribution data. Specifically, this is done by first assigning each correspondence degree... Correspondence threshold Comparisons are made to determine whether there is a correspondence between the data for each question and the data for the answers. If the correspondence is... Greater than the corresponding degree threshold If a relationship exists, a single dialogue can be generated. This process can be repeated to generate multiple single dialogues composed of question and answer data. Then, the topics discussed in each single dialogue are evaluated and categorized to determine the corresponding topic focus. and the direction of a single conversation correlation between When classifying topics for evaluation, one can use a topic classification model to classify each dialogue, or other classification models can be used. Furthermore, the resulting classification can be a known classification; alternatively, to increase flexibility, a summary of the topic focus of the current dialogue can be provided.

[0068] Specifically, semantic recognition is performed on each individual dialogue, and multiple topic orientations are summarized from different analytical perspectives. The correlation between each topic orientation and the individual dialogue is then calculated. And select the correlation degree Greater than the set correlation threshold The same topic points to The corresponding single dialogue simultaneously summarizes the same topic. Single conversations are extracted, and then the extracted conversations are compared in terms of content relevance to identify those that share the same topic. Is the relationship between individual dialogues hierarchical or parallel? Based on the association relationship within individual dialogues, can we achieve targeting of the same topic? All corresponding single dialogues are sorted together to obtain a tree-structured distribution of associated dialogues.

[0069] In the process of sorting the single dialogues corresponding to the association relationship according to the corresponding hierarchical arrangement to obtain the association dialogue distribution data, the single dialogues with upper and lower hierarchical association relationships can be sorted level by level as the first association dialogue distribution data to represent the distribution of the hierarchical extension relationship of the topic; the single dialogues with parallel association relationships can be sorted in parallel as the second association dialogue distribution data to represent the distribution of topics that are all at the same level; then, the association dialogue distribution data is obtained based on the first association dialogue distribution data and the second association dialogue distribution data.

[0070] Furthermore, the correlation Greater than the set correlation threshold The same topic points to By comparing the content association levels between corresponding individual conversations, we can obtain the topic references for each conversation. The relationships between individual conversations under the corresponding dimension can include:

[0071] Each individual dialogue will be assigned to a corresponding topic. The semantics of the dimensions are transformed to obtain the topic content;

[0072] By comparing the causal relationships of the topics corresponding to each individual dialogue, the degree of causal correlation between the individual dialogues is obtained. ;

[0073] Based on the relevance of a single dialogue Relevance Corresponding association weight and causal correlation The correlation level index was calculated. ,in, ;

[0074] When related hierarchical indicators Greater than the indicator threshold In this case, a hierarchical relationship is established between the corresponding individual dialogues;

[0075] When related hierarchical indicators Less than the indicator threshold In this case, a parallel hierarchical relationship is established between the corresponding individual dialogues.

[0076] Establish the direction of each topic through the online public opinion monitoring system. In the process of establishing connections between individual dialogues within the corresponding dimension, based on the same topic... By semantically transforming each individual dialogue into its corresponding dimension, we can obtain the corresponding topic. The topic content, that is, transforming a single conversation into one centered around a specific topic. Then, the topic content is used for reasonable narration. Next, the causal relationship between the topic content of each individual dialogue is compared to determine whether each question and answer data points to the same topic. Causal correlation in topic content under different dimensions In determining the degree of causal relationship In this case, a causal correlation prediction model can be obtained by first training the model based on the topic content and the labeled causal correlation between the topics, and then identifying and predicting the corresponding causal correlation based on different topic content. Of course, other methods can also be used to measure the degree of causal relationship. The calculation of causal correlation. Then, the relevance of a single conversation is analyzed. The degree of correlation obtained by looking up the table Corresponding association weight To achieve the management of related hierarchical indicators The calculation can then be performed by associating hierarchical indicators. With indicator threshold By comparing the relevant hierarchical indicators, the appropriate level of indicators can be determined. Greater than the indicator threshold When this happens, a hierarchical relationship is established between the corresponding individual dialogues, and the associated hierarchical indicators are... Less than the indicator threshold In this case, a parallel hierarchical relationship is established between the corresponding individual dialogues. Specifically, the associated hierarchical indicators... The calculation formula can be expressed as: , Indicates the relevance of another single dialogue. This represents the association weight corresponding to the association degree of another single dialogue.

[0077] In addition, sentiment analysis is performed on the question and answer data in related dialogue strings to obtain sentiment classification and sentiment feedback trends, which can be used as data to trigger public opinion, including:

[0078] Sentiment detection is performed on all related dialogue strings under the same topic-oriented dimension to obtain sentiment elements and element evaluations; among them, element evaluations can be "extremely", "very", "extremely", "okay", "neutral", etc.

[0079] All emotional elements are organized and categorized to obtain related emotional classifications and a set of emotional elements corresponding to each related emotional classification;

[0080] Based on the degree value of the element evaluation corresponding to the emotional element. and the correlation factors of the emotional elements at the level of the dialogue. The associated emotional feedback value of the emotional element is calculated. ;

[0081] Based on the associated sentiment feedback value, the associated sentiment feedback trend is obtained.

[0082] In conducting sentiment analysis using a network public opinion monitoring system, the process first involves identifying the sentiment elements and their evaluations across all related dialogues pointing to the same topic. Then, these sentiment elements are categorized to determine the corresponding sentiment classification class for each element, resulting in a set of sentiment elements for each class. For each sentiment element, a corresponding degree value is derived based on its evaluation. Based on the hierarchy of emotional elements within the dialogue string, the corresponding association factors for each hierarchy are obtained. Then, based on the degree value and related factors The associated emotional feedback value of the emotional element is calculated. Finally, the associated emotional feedback value corresponding to each emotional element is used. This is used to map the trend of related emotional feedback.

[0083] Preferably, the data triggered by public opinion may include related sentiment classification, related sentiment feedback trends, and related dialogue strings.

[0084] Next, step S30 is executed to obtain the source data of the public opinion incident based on the public opinion incident data.

[0085] After obtaining the data that triggered public opinion through the network public opinion monitoring system, the related dialogue strings in the source data of public opinion can be analyzed and searched to determine the source data of public opinion that triggered the data, which is to form the source data corresponding to the data that triggered the public opinion.

[0086] In step S30, obtaining the source data of the public opinion incident based on the public opinion incident data may further include:

[0087] Based on the sentiment association classification, the related dialogue strings are searched to obtain question data and answer data arranged in the order of the related dialogue strings;

[0088] Based on the trend of related sentiment feedback, upstream data is searched for the related dialogue strings corresponding to the sentiment classification to obtain the initial data corresponding to the sentiment feedback trend, which is used as the source data for triggering public opinion. The initial data includes either initial question data or initial answer data.

[0089] In the process of locating the source data of public opinion events through the online public opinion monitoring system, the first step is to classify the sentiment correlation within the public opinion event data. This involves identifying all question and answer data corresponding to the sentiment correlation categories within the related dialogue strings, arranged hierarchically within the related dialogue strings. Then, based on the sentiment correlation feedback trend in the public opinion event data, upstream data searches are performed on all question and answer data under the sentiment correlation categories to find the initial data corresponding to the sentiment correlation feedback trend, such as initial question or answer data, which serves as the source data for the public opinion event. In other words, the resulting sentiment correlation feedback trend and the corresponding dialogue under the sentiment correlation category are triggered by this source data, thus constituting the originating data.

[0090] It is worth noting that the source data refers to a series of public opinion data information with related emotional feedback trends that are triggered by the topic of a certain public opinion. This related emotional feedback trend can directly reflect the extent to which the source data leads to the further extension of discussion or public opinion fermentation.

[0091] Next, step S40 is executed, which involves adjusting the event update data of the public opinion event based on the source data, the triggering data, and the expected goals, to generate event public relations data for publication across the entire network.

[0092] In step S40, based on the source data of the public opinion event, the data on the triggering of the public opinion event, and the expected goals, the event update data of the public opinion event is adjusted to generate event public relations data for publication across the entire network. This may further include:

[0093] The data source data of public opinion triggers is transformed to obtain the public opinion guidance of the public opinion triggering data by analyzing the correlation sentiment feedback trend, correlation sentiment classification and data source data of public opinion triggers.

[0094] The public opinion guidance is compared with the expected guidance corresponding to the expected goal, the public opinion guidance that is closest to the expected guidance is found, and the deviation of the public opinion guidance from the expected guidance is calculated.

[0095] Based on the deviation, the associated sentiment feedback trend and associated sentiment classification are adjusted accordingly to obtain the expected sentiment trend and expected sentiment classification.

[0096] Based on the deviation, expected sentiment trend, and expected sentiment classification, the data of the source of public opinion is adjusted to obtain the expected source data.

[0097] Based on the expected source data, generate expected event data;

[0098] Based on the expected event data, the event update data of the public opinion event is adjusted to generate event public relations data for publication across the entire network.

[0099] In the process of regulating the update data of public opinion events through a network public opinion monitoring system based on the source data, the triggering data, and the expected goals, the system can first transform the related sentiment feedback trends, related sentiment classifications, and the source data to derive the public opinion guidance of the source data. Similarly, for the expected goals, a corresponding expected guidance can also be derived. Public opinion guidance can be the trend of public opinion facts in one direction, while the expected guidance is the direction that staff hope for. Therefore, the public opinion guidance and the expected guidance can be compared to determine the public opinion guidance closest to the expected guidance. Then, the deviation between the closest public opinion guidance and the expected guidance is calculated. This deviation can then be used to adjust the related sentiment feedback trends and related sentiment classifications to obtain the corresponding expected sentiment trends and expected sentiment classifications. Finally, based on the expected sentiment trends and expected sentiment classifications, the source data of the public opinion events can be adjusted to derive the corresponding expected source data. Therefore, the expected source data can be used to generate corresponding expected event data, which can be used to adjust the event update data of public opinion events to obtain adjusted event public relations data. Thus, the release of this event public relations data can be used to control the direction of public opinion.

[0100] Based on deviation, expected sentiment trend, and expected sentiment classification, the data of public opinion triggering sources are adjusted to obtain expected triggering source data. This can be achieved by training a model based on deviation, expected sentiment trend, expected sentiment classification, and the labeled public opinion triggering source data to establish a triggering source prediction model. In this way, the corresponding expected triggering source data can be predicted based on the corresponding deviation, expected sentiment trend, and expected sentiment classification.

[0101] Of course, when adjusting the data of the source of public opinion based on deviation, expected sentiment trend, and expected sentiment classification to obtain the expected source data, one can first use deviation to adjust the correlation sentiment feedback value and sentiment classification of the source data to obtain the deviation source data. Then, the adjusted deviation source data can be fine-tuned using the expected sentiment trend and expected sentiment classification to obtain the expected source data. Other methods can also be used to obtain the expected source data, which will not be elaborated upon here.

[0102] Furthermore, based on anticipated event data, the event update data of public opinion events is adjusted to generate event public relations data for nationwide publication. This can further include: performing deletion or retention processing on the event update data of public opinion events based on anticipated event data to generate event public relations data for nationwide publication. Depending on the expected objectives, it may be necessary to delete anticipated event data corresponding to the expected direction in the event update data to eliminate further impact on public opinion. Alternatively, it may be necessary to retain anticipated event data in the event update data to expand the scope of public opinion influence.

[0103] Please refer to 3. The present invention also provides a network public opinion monitoring system 11, including: an acquisition unit 111, used to acquire network public opinion data according to public opinion events; an extraction unit 112, used to perform public opinion-oriented extraction on the network public opinion data to acquire public opinion triggering data; a query unit 113, used to acquire public opinion triggering source data according to the public opinion triggering data; and a publishing unit 114, used to regulate the event update data of the public opinion event according to the public opinion triggering source data, the public opinion triggering data and the expected goals, and generate event public relations data for publication across the entire network.

[0104] It should be noted that the network public opinion monitoring system 11 provided in the above embodiments and the network public opinion monitoring method provided in the above embodiments belong to the same concept. The specific ways in which each module and unit performs operations have been described in detail in the method embodiments, and will not be repeated here. In practical applications, the network public opinion monitoring system 11 provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0105] Please see Figure 4 The electronic device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a network public opinion monitoring program.

[0106] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive of the electronic device 1. In other embodiments, the memory 12 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 12 can include both internal and external storage units of the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as code for online public opinion monitoring, but also to temporarily store data that has been output or will be output.

[0107] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control unit of the electronic device 1, connecting various components of the electronic device 1 through various interfaces and lines. It executes programs or modules (such as online public opinion monitoring programs) stored in the memory 12, and calls data stored in the memory 12 to perform various functions of the electronic device 1 and process data.

[0108] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-described online public opinion monitoring method.

[0109] For example, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into units in a network public opinion monitoring system.

[0110] The integrated unit implemented as a software functional module can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module, stored in the storage medium, includes several instructions to cause a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor to execute some functions of the network public opinion monitoring method described in the various embodiments of this application.

[0111] In summary, the online public opinion monitoring method and system disclosed in this invention utilizes public opinion events to set diversified search keywords adapted to different online platforms, thereby regulating the acquisition of online public opinion data on different platforms. This effectively improves the sufficiency of retrieval on each platform, ensuring the coverage of online public opinion data on every platform. Furthermore, by extracting public opinion guidance from the acquired online public opinion data, it enables rapid identification of various public opinion guidance and the source data of public opinion. In the process of further regulating public opinion events, the source data of public opinion can be transformed based on the expected goals to obtain expected event data corresponding to the expected goals. This allows for corresponding adjustments to the updated event data after the evolution of the public opinion event based on the expected event data, which is then published as event public relations data. This enables the regulation of the evolution trend of public opinion towards the expected goals through the publication of event public relations, such as strengthening or weakening the impact of public opinion, thereby achieving monitoring and regulation of the development trend of public opinion. Therefore, this invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0112] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for monitoring online public opinion, characterized in that, include: Obtain online public opinion data based on public opinion events; The online public opinion data is subjected to public opinion guidance extraction to obtain data on public opinion triggers; Based on the aforementioned public opinion triggering data, obtain the data on the source of the public opinion triggering event; Based on the source data of the public opinion event, the data on the triggering of the public opinion event, and the expected goals, the event update data of the public opinion event is adjusted to generate event public relations data for publication across the entire network.

2. The online public opinion monitoring method according to claim 1, characterized in that, Obtain online public opinion data based on public opinion events, including: Keyword extraction was performed on the aforementioned public opinion event to obtain public opinion keywords. ; Search for keywords related to public opinion. Semantically related keywords as public opinion-related terms ; For the aforementioned public opinion keywords Related keywords to the aforementioned public opinion Perform platform conversion to generate platform expressions corresponding to the platform language of the respective network platform. ; Through the aforementioned public opinion keywords The aforementioned public opinion-related keywords and the platform expression words Conduct public opinion searches on relevant online platforms to obtain the aforementioned online public opinion data.

3. The online public opinion monitoring method according to claim 2, characterized in that, For the aforementioned public opinion keywords Related keywords to the aforementioned public opinion Perform platform conversion to generate platform expressions corresponding to the platform language of the respective network platform. ,include: The aforementioned public opinion keywords Related keywords to the aforementioned public opinion Perform word segmentation to obtain a word data set. The word data set includes at least one of single-character data and word data; According to the word data set Word data in The first platform terminology set corresponding to the network platform is found. ,in, This indicates a subset of terms used on the first platform. , Represents data for each word The corresponding terminology used on the first platform; Select the terminology set of the first platform Several subsets of terms used on the first platform And select multiple subsets of terms used on the first platform The corresponding first platform terminology Perform a combination, then combine it with other word data. A secondary combination is performed to generate the first platform expression terms, wherein the first combination includes at least one subset of the first platform terms. The corresponding first platform terminology ; The aforementioned public opinion keywords Related keywords to the aforementioned public opinion Perform semantic transformation to obtain semantic information; Based on the semantic information, the second platform expression term corresponding to the network platform is obtained; Based on the first platform terminology and the second platform terminology, generate platform terminology corresponding to the platform language of the respective network platform. .

4. The online public opinion monitoring method according to claim 1, characterized in that, The online public opinion data is subjected to public opinion guidance extraction to obtain public opinion triggering data, including: Extract dialogue data from the online public opinion data, the dialogue data including question data and answer data; Based on the correspondence between the answer data and the question data, the dialogue is analyzed to obtain the associated dialogue distribution data; The associated dialogue distribution data is subjected to dialogue string extraction to obtain multiple associated dialogue strings, which include the question data and the answer data that are associated in layers. Sentiment analysis is performed on the question data and answer data in the associated dialogue string to obtain sentiment classification and sentiment feedback trends, which are used as the data for triggering public opinion.

5. The online public opinion monitoring method according to claim 4, characterized in that, Based on the correspondence between the answer data and the question data, the dialogue is analyzed to obtain associated dialogue distribution data, including: Calculate the correspondence between each answer data and each question data to obtain a correspondence set. ; The correspondence set Each correspondence in Correspondence threshold Comparison: When the correspondence Greater than the corresponding degree threshold When the value is greater than the corresponding degree threshold, it will be greater than the threshold value. The corresponding degree The corresponding answer data serves as the answer to the question data, thereby generating a single dialogue; The single dialogue is evaluated and categorized to determine the corresponding topic orientation. and the single dialogue and the topic orientation. correlation between ; correlation Greater than the set correlation threshold The same topic refers to The content association hierarchy is compared between the corresponding single dialogues to obtain the topic pointers for each dialogue. The relationships between individual dialogues under the corresponding dimension, wherein the relationships include hierarchical relationships and parallel hierarchical relationships; The individual dialogues corresponding to the aforementioned relationships are sorted according to their respective hierarchical arrangement to obtain the associated dialogue distribution data.

6. The online public opinion monitoring method according to claim 5, characterized in that, correlation Greater than the set correlation threshold The same topic refers to The content association hierarchy is compared between the corresponding single dialogues to obtain the topic pointers for each dialogue. The relationships between individual dialogues under the corresponding dimension include: Each of the aforementioned single dialogues will be assigned to the corresponding topic. The semantics of the dimensions are transformed to obtain the topic content; By comparing the causal relationships of the topics corresponding to each individual dialogue, the causal correlation degree between each individual dialogue is obtained. ; Based on the relevance of the single dialogue The degree of correlation Corresponding association weight and causal correlation The correlation level index was calculated. ,in, ; When the associated hierarchical indicators Greater than the indicator threshold When this happens, the hierarchical relationship is established between the corresponding single dialogues; When the associated hierarchical indicators Less than the indicator threshold When this happens, the parallel hierarchical relationship is established between the corresponding single dialogues.

7. The online public opinion monitoring method according to claim 4, characterized in that, Sentiment analysis is performed on the question and answer data in the associated dialogue strings to obtain sentiment classification and sentiment feedback trends, which are used as the public opinion triggering data, including: Sentiment detection is performed on all related dialogue strings under the same topic-pointing dimension to obtain sentiment elements and element evaluations; All the emotional elements are sorted and classified to obtain the associated emotional classification class and the emotional element set corresponding to each associated emotional classification class; The degree value of the evaluation of the corresponding emotional element. and the correlation factor of the emotional element at the level of the dialogue string. The associated emotional feedback value of the emotional element is calculated. ; The associated sentiment feedback trend is obtained based on the associated sentiment feedback value.

8. The online public opinion monitoring method according to claim 1, characterized in that, The data triggered by public opinion includes related sentiment classification, related sentiment feedback trends, and related dialogue strings; Based on the aforementioned public opinion triggering data, obtain the public opinion triggering source data, including: Based on the sentiment classification, a dialogue search is performed on the associated dialogue strings to obtain question data and answer data arranged in the order of the associated dialogue strings; Based on the associated sentiment feedback trend, upstream data search is performed on the associated dialogue strings corresponding to the associated sentiment classification to obtain the initial data corresponding to the associated sentiment feedback trend, which is used as the source data for triggering public opinion. The initial data includes one of initial question data and initial answer data.

9. The online public opinion monitoring method according to claim 1, characterized in that, Based on the aforementioned public opinion source data, the public opinion triggering data, and the expected goals, the event update data of the public opinion event is adjusted to generate event public relations data for publication across the entire network, including: The associated sentiment feedback trend, associated sentiment classification, and the data transformation of the public opinion source data are used to obtain the public opinion guidance of the public opinion source data. The public opinion guidance is compared with the expected guidance corresponding to the expected goal, the public opinion guidance that is closest to the expected guidance is found, and the deviation of the public opinion guidance from the expected guidance is calculated. Based on the deviation, the associated sentiment feedback trend and the associated sentiment classification are adjusted accordingly to obtain the expected sentiment trend and the expected sentiment classification. Based on the deviation, the expected sentiment trend, and the expected sentiment classification, the data of the public opinion triggering source is adjusted to obtain the expected triggering source data; Based on the expected source data, expected event data is generated; Based on the expected event data, the event update data of the public opinion event is adjusted to generate event public relations data for publication across the entire network.

10. A network public opinion monitoring system, characterized in that, include: The acquisition unit is used to acquire online public opinion data based on public opinion events; The extraction unit is used to perform public opinion guidance extraction on the online public opinion data to obtain public opinion triggering data. The query unit is used to obtain the source data of the public opinion incident based on the public opinion incident data; The publishing unit is used to regulate the event update data of the public opinion event based on the source data of the public opinion event, the data of the public opinion event, and the expected goals, and generate event public relations data for publication across the entire network.

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