CN114840725B

CN114840725B - Public opinion security analysis method, device and electronic equipment based on event correlation

Info

Publication number: CN114840725B
Application number: CN202210509195.7A
Authority: CN
Inventors: 厉山山; 吴业超; 刘方舟; 任天悦; 刘辉耀; 李冰; 郭佳
Original assignee: Shanghai Qiyue Information Technology Co Ltd
Current assignee: Shanghai Qiyue Information Technology Co Ltd
Priority date: 2022-05-10
Filing date: 2022-05-10
Publication date: 2025-05-30
Anticipated expiration: 2042-05-10
Also published as: CN114840725A

Abstract

The invention discloses a public opinion safety analysis method, a device and electronic equipment based on event correlation, wherein the method comprises the steps of classifying public opinion data into corresponding public opinion classification sets; the method comprises the steps of carrying out dimension reduction processing on public opinion data in a public opinion classification set, searching a historical event classification set to which the processed public opinion data belongs based on a first time window, classifying the public opinion data into the historical event classification set to which the processed public opinion data belongs, carrying out correlation matching on the public opinion data in the same historical event classification set, classifying the matched public opinion data into a negative public opinion set, and carrying out public opinion safety analysis based on the public opinion classification set, the historical event classification set and the negative public opinion set. The method can classify, trace, ferment and secondarily ferment public opinion, so that the complete occurrence and fermentation process of public opinion events are quickly restored, hidden public opinion risks are prevented, public opinion safety is ensured, and enterprise and social images are maintained.

Description

Public opinion security analysis method and device based on event correlation and electronic equipment

Technical Field

The invention relates to the technical field of data processing, in particular to a public opinion safety analysis method and device based on event correlation, electronic equipment and a computer readable medium.

Background

With the rapid development and popularization of the internet, people are used to publishing respective comments or utterances to social hotspots, social public matters and the like through networks, and meanwhile, with various forms of self-media and social platforms, such as public numbers, microblogs and the like, the social platform is also presented. When social events and social problems occur, people often quickly learn the cause and development process of the events by means of a media platform, and then send comments through a network medium, and the comments have a non-negligible influence on the development of the events, so that public opinion is generated. In the public opinion appearance and transmission process, high-repeatability accounts, content forwarding accounts and the like often appear to schedule events, so that public images of enterprises, individuals and even society are seriously affected, and therefore public opinion safety is important to the enterprises and society.

At present, due to the rapidness, the universality and the strong interactivity of network propagation, the network public opinion tends to be explosive growth, and the form is complex, so that the characteristics of large public opinion information data volume, various contents, difficult tracing, various identities of public opinion publishers, flexible forwarding, numerous attention points and the like are caused, and the public opinion cannot be rapidly and effectively analyzed.

Disclosure of Invention

Accordingly, the present invention is directed to a public opinion safety analysis method, apparatus, electronic device and computer readable medium based on event correlation, so as to at least partially solve at least one of the above problems.

In order to solve the above technical problems, a first aspect of the present invention provides a public opinion safety analysis method based on event correlation, the method comprising:

classifying the public opinion data into corresponding public opinion classification sets;

Performing dimension reduction processing on the public opinion data in the public opinion classification set, searching a historical event classification set to which the processed public opinion data belongs based on a first time window, and classifying the public opinion data into the historical event classification set to which the public opinion data belongs;

Carrying out correlation matching on public opinion data in the same historical event classification set, and dividing the matched public opinion data into negative public opinion sets;

And carrying out public opinion safety analysis based on the public opinion classification set, the historical event classification set and the negative public opinion set.

According to a preferred embodiment of the present invention, the performing a dimension reduction process on public opinion data in a public opinion classification set, and searching a historical event classification set to which the processed public opinion data belongs based on a first time window includes:

Binary conversion is carried out on public opinion data in the public opinion classification set, and the converted character strings are segmented to obtain public opinion segments;

searching character strings to be compared which are related to each public opinion section in a first time window;

comparing the character string of the public opinion section with the character string to be compared according to the bit to determine the character string similar to the public opinion data;

And determining a historical event classification set to which the public opinion data belongs according to the character strings similar to the public opinion data.

According to a preferred embodiment of the invention, the method further comprises:

performing dimension reduction processing on the newly-added public opinion data, searching a historical event classification set to which the processed newly-added public opinion data belongs based on a first time window, classifying the newly-added public opinion data into the belonging historical event classification set, and sending event fermentation alarm information;

If the classification set of the historical event which the processed newly-added public opinion data belongs to is not found in the first time window, replacing the first time window with a second time window, searching the classification set of the historical event which the processed newly-added public opinion data belongs to based on the second time window, classifying the newly-added public opinion data into the classification set of the historical event which the newly-added public opinion data belongs to, and sending event secondary fermentation alarm information;

wherein the second time window is earlier than the first time window.

According to a preferred embodiment of the present invention, the comparison mechanism based on the history event to which the newly added public opinion data after the first time window searching process belongs is different from the comparison mechanism based on the history event to which the newly added public opinion data after the second time window searching process belongs.

According to a preferred embodiment of the present invention, the method further comprises searching and displaying target public opinion from the public opinion classification set, the historical event classification set and the negative public opinion sets according to user search information.

According to a preferred embodiment of the present invention, the searching and displaying the target public opinion from the public opinion classification set, the historical event classification set and the negative public opinion according to the user search information includes:

Matching target public opinion based on multiple dimensions from the public opinion classification set, the historical event classification set and the negative public opinion set according to user search information;

determining the matching degree of each target public opinion according to the weight value of each dimension;

And displaying each target public opinion according to the matching degree of the target public opinion.

According to a preferred embodiment of the present invention, before the displaying each target public opinion according to the matching degree of the target public opinion, the method further includes:

and adjusting the matching degree of the target public opinion according to the historical target public opinion matched with the user search information, or:

And adjusting the matching degree of the target public opinion according to the final public opinion selected from the historical target public opinion by the user.

According to a preferred embodiment of the present invention, the matching the target public opinion based on multiple dimensions from the public opinion classification set, the historical event classification set and the negative public opinion set according to the user search information includes:

And matching user search information based on multiple dimensions from the public opinion classification set, the historical event classification set and the negative public opinion set to obtain classified target public opinion, event target public opinion and negative target public opinion.

constructing a user portrait according to the user history search;

information is pushed to a user based on the user image.

According to a preferred embodiment of the present invention, the classifying the public opinion data into corresponding public opinion classification sets includes:

Carrying out correlation analysis on the public opinion data to obtain correlated public opinion data;

Classifying the related public opinion data according to the text attribute of the related public opinion data;

judging the emotion type of the classified public opinion data based on the positive and negative emotion corpus;

And processing the public opinion data subjected to emotion classification by adopting a classification model to obtain a public opinion classification set in which the public opinion data is located.

According to a preferred embodiment of the present invention, if the emotion type of the classified public opinion data is negative, a negative public opinion alarm is issued.

According to a preferred embodiment of the present invention, the public opinion data includes media public opinion data and social network public opinion data, and the method further includes:

And carrying out relevance matching on the social network public opinion data, and classifying the matched social network public opinion data into negative public opinion sets.

In order to solve the above technical problem, a second aspect of the present invention provides a public opinion safety analysis device based on event correlation, the device comprising:

The classification module is used for classifying the public opinion data into corresponding public opinion classification sets;

the dimension reduction searching module is used for carrying out dimension reduction processing on the public opinion data in the public opinion classification set, searching a historical event classification set to which the processed public opinion data belongs based on the first time window, and classifying the public opinion data into the historical event classification set to which the public opinion data belongs;

the first matching module is used for carrying out correlation matching on the public opinion data in the same historical event classification set, and dividing the matched public opinion data into negative public opinion sets;

And the analysis module is used for carrying out public opinion safety analysis based on the public opinion classification set, the historical event classification set and the negative public opinion set.

According to a preferred embodiment of the present invention, the dimension-reduction search module includes:

the segmentation module is used for binary conversion of public opinion data in the public opinion classification set and segmentation of the converted character strings to obtain public opinion segments;

The sub-searching module is used for searching character strings to be compared which are related to each public opinion section in a first time window;

the comparison module is used for comparing the character string in which the public opinion section is positioned with the character string to be compared according to the bit to determine the character string similar to the public opinion data;

And the sub-determining module is used for determining a historical event classification set to which the public opinion data belongs according to the character strings similar to the public opinion data.

According to a preferred embodiment of the invention, the device further comprises:

The first dimension-reduction searching module is used for dimension-reduction processing of the newly-added public opinion data, searching a historical event classification set to which the processed newly-added public opinion data belongs based on a first time window, classifying the newly-added public opinion data into the belonging historical event classification set, and sending out event fermentation alarm information;

The second dimension-reduction searching module is used for replacing the first time window with a second time window if the historical event classification set to which the processed newly-added public opinion data belongs is not found in the first time window, searching the historical event classification set to which the processed newly-added public opinion data belongs based on the second time window, classifying the newly-added public opinion data into the affiliated historical event classification set, and sending event secondary fermentation alarm information;

wherein the second time window is earlier than the first time window.

According to a preferred embodiment of the present invention, the comparison mechanism of the first dimension-reduction search module and the second dimension-reduction search module is different.

According to a preferred embodiment of the invention, the device further comprises a search display module, which is used for searching and displaying target public opinion from the public opinion classification set, the historical event classification set and the negative public opinion set according to user search information.

According to a preferred embodiment of the present invention, the search presentation module includes:

The multi-dimension matching module is used for matching target public opinion based on multiple dimensions from the public opinion classification set, the historical event classification set and the negative public opinion set according to user search information;

The determining module is used for determining the matching degree of each target public opinion according to the weight value of each dimension;

And the display module is used for displaying each target public opinion according to the matching degree of the target public opinion.

And the adjustment module is used for adjusting the matching degree of the target public opinion according to the historical target public opinion matched with the user search information, or adjusting the matching degree of the target public opinion according to the final public opinion selected from the historical target public opinion by the user.

According to a preferred embodiment of the present invention, the multi-dimensional matching module includes:

and the sub-matching module is used for matching the user search information based on a plurality of dimensions from the public opinion classification set, the historical event classification set and the negative public opinion set to obtain classified target public opinion, event target public opinion and negative target public opinion.

The construction module is used for constructing a user portrait according to the user history search;

And the pushing module is used for pushing information to the user based on the user portrait.

According to a preferred embodiment of the present invention, the classification module includes:

The correlation analysis module is used for carrying out correlation analysis on the public opinion data to obtain correlated public opinion data;

The sub-classification module is used for classifying the related public opinion data according to the text attribute of the related public opinion data;

the judging module is used for judging the emotion type of the classified public opinion data based on the positive and negative emotion corpus;

and the model processing module is used for processing the public opinion data subjected to emotion classification by adopting the classification model to obtain a public opinion classification set in which the public opinion data is located.

According to a preferred embodiment of the invention, the device further comprises an alarm module, which is used for sending out negative public opinion alarms if the emotion type of the classified public opinion data is negative.

According to a preferred embodiment of the present invention, the public opinion data includes media public opinion data and social network public opinion data, and the apparatus further includes:

And the second matching module is used for carrying out relevance matching on the social network public opinion data and dividing the matched social network public opinion data into negative public opinion sets.

To solve the above technical problem, a third aspect of the present invention provides an electronic device, including:

Processor, and

A memory storing computer executable instructions that, when executed, cause the processor to perform the method described above.

To solve the above technical problem, a fourth aspect of the present invention provides a computer-readable storage medium storing one or more programs, which when executed by a processor, implement the above method.

The method comprises the steps of classifying public opinion data into corresponding public opinion classification sets, performing dimension reduction treatment on the public opinion data in the public opinion classification sets, searching the historical event classification set to which the treated public opinion data belongs based on a first time window, classifying the public opinion data into the historical event classification set to which the treated public opinion data belongs, performing correlation matching on the public opinion data in the same historical event classification set, classifying the matched public opinion data into negative public opinion sets, analyzing the public opinion category according to the public opinion classification set, tracing, fermenting and secondary fermentation analysis on the public opinion according to the historical event classification set, tracking and analyzing repeated account numbers and forwarding account numbers in the public opinion generating process according to the negative public opinion set, and thus rapidly reducing the complete occurrence of the public opinion event and the fermentation process.

Drawings

In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects achieved more clear, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted, however, that the drawings described below are merely illustrative of exemplary embodiments of the present invention and that other embodiments of the drawings may be derived from these drawings by those skilled in the art without undue effort.

FIG. 1 is a flow chart of a public opinion safety analysis method based on event correlation according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method for performing dimension reduction processing on public opinion data in a public opinion classification set and searching a historical event classification set to which the processed public opinion data belongs based on a first time window according to the embodiment of the invention;

FIG. 3 is a flowchart of searching and displaying target public opinion from the public opinion classification set, the historical event classification set and the negative public opinion set according to user search information according to the embodiment of the invention;

FIG. 4 is a schematic diagram of a structural framework of a public opinion safety analysis device based on event correlation according to an embodiment of the present invention;

FIG. 5 is a block diagram of an exemplary embodiment of an electronic device in accordance with the present invention;

FIG. 6 is a schematic diagram of one embodiment of a computer readable medium of the present invention.

Detailed Description

Exemplary embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown, although the exemplary embodiments may be practiced in various specific ways. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art.

The structures, capabilities, effects, or other features described in a particular embodiment may be incorporated in one or more other embodiments in any suitable manner without departing from the spirit of the present invention.

In describing particular embodiments, specific details of construction, performance, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by those skilled in the art. It is not excluded that one skilled in the art may implement the present invention in a particular case in a solution that does not include the structures, properties, effects, or other characteristics described above.

The flow diagrams in the figures are merely exemplary flow illustrations and do not represent that all of the elements, operations, and steps in the flow diagrams must be included in the aspects of the present invention, nor that the steps must be performed in the order shown in the figures. For example, some operations/steps in the flowcharts may be decomposed, some operations/steps may be combined or partially combined, etc., and the order of execution shown in the flowcharts may be changed according to actual situations without departing from the gist of the present invention.

The block diagrams in the figures generally represent functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The same reference numerals in the drawings denote the same or similar elements, components or portions, and thus repeated descriptions of the same or similar elements, components or portions may be omitted hereinafter. It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various devices, elements, components or portions, these devices, elements, components or portions should not be limited by these terms. That is, these phrases are merely intended to distinguish one from the other. For example, a first device may also be referred to as a second device without departing from the spirit of the invention. Furthermore, the term "and/or," "and/or" is meant to include all combinations of any one or more of the items listed.

Referring to fig. 1, fig. 1 is a public opinion safety analysis method based on event correlation, as shown in fig. 1, provided by the present invention, the method includes:

S1, classifying the public opinion data into corresponding public opinion classification sets;

the public opinion data may include media public opinion data from official media, social network public opinion data from a network social platform, media public opinion data from self media, etc.

In this embodiment, public opinion data is processed in text form. Therefore, before this step, public opinion texts can be obtained from official media, self media and network social platforms, and then the public opinion texts are classified according to the length, time, emotion attribute, word frequency-reverse file frequency, source and other attributes of the texts, which may include:

s11, carrying out correlation analysis on public opinion data to obtain correlated public opinion data;

for example, similar or related text in the public opinion text can be identified based on the semantic model, and the similar or related public opinion text is used as related public opinion data.

S12, classifying the related public opinion data according to the text attribute of the related public opinion data;

By way of example, text attributes may include attributes of length, time, word frequency-inverse frequency, source, etc. of the text. Taking word frequency-inverse document frequency processing as an example, firstly inputting public opinion texts into an Analyzer word segmentation device for word segmentation, processing the public opinion texts into word segments, filtering dead words in the word segments to obtain effective word segments, and then carrying out word frequency-inverse document frequency processing on the public opinion texts based on the effective word segments to obtain keywords of each public opinion text and classifying the public opinion texts based on the keywords. The step of filtering the stop words in the word segments to obtain effective word segments may include determining whether there are stop words in the word segments, if there are stop words, filtering the stop words from the word segments to obtain effective word segments, and if there are no stop words, using all the word segments as effective words.

S13, judging the emotion type of the classified public opinion data based on the positive and negative emotion corpus;

In this embodiment, the positive and negative emotion corpus is a corpus constructed based on historical corpus and containing positive and negative text keywords, and word segmentation and emotion analysis can be performed on the newly added situation in real time through the corpus. In the step, the word segmentation after the stop word filtering is compared with positive and negative text keywords in a positive and negative emotion corpus, the number of the obtained positive words and negative words is counted, and the emotion type of the public opinion text is determined according to the proportion of the positive words and the negative words. For example, the ratio of positive words to negative words is larger than a first threshold, the emotion type of the public opinion text is positive, the ratio of positive words to negative words is smaller than a second threshold, and the emotion type of the public opinion text is negative.

Further, if the emotion type of the public opinion text is judged to be negative in the step, a negative public opinion alarm is sent, so that monitoring, focusing and processing of public opinion are conveniently and timely carried out, and risk public opinion is controlled early.

And S14, processing the public opinion data subjected to emotion classification by adopting a classification model to obtain a public opinion classification set in which the public opinion data is located.

The method comprises the steps of inputting the public opinion texts with the same initial classification and emotion classification and/or the public opinion segmentation corresponding to the public opinion texts into a pre-trained classification model, and finally classifying the public opinion according to the output result of the model to obtain a public opinion classification set of each public opinion text and/or the public opinion segmentation. Wherein, a public opinion classification set corresponds to a public opinion category of an emotion type, and the emotion category can comprise support, expression, objection, criticism and the like. Public opinion categories may include science, technology, military, entertainment, politics, news, and the like.

Furthermore, the same label can be marked on the same type of public opinion texts and/or public opinion segmentation words to show and distinguish, and a public opinion classification database is constructed through the marked public opinion text structures and/or public opinion segmentation words, so that the method is convenient for later use. In order to facilitate the subsequent search based on the public opinion classification database, an inverted index can be established for the public opinion data while constructing the public opinion classification database.

In addition, because the account numbers such as the account numbers with high repeatability and the content forwarding account numbers mainly originate from the self-media and social network platforms, the step can also be used for carrying out correlation matching on the self-media public opinion data and the social network public opinion data, and the matched social network public opinion data are classified into negative public opinion concentrations to carry out important attention. For example, the relevance matching may be performed by keywords and/or public opinion publishing account numbers.

S2, performing dimension reduction processing on the public opinion data in the public opinion classification set, searching a historical event classification set to which the processed public opinion data belongs based on a first time window, and classifying the public opinion data into the historical event classification set to which the public opinion data belongs;

Considering that massive public opinion data can influence the processing efficiency, the embodiment performs dimension reduction processing on the public opinion data, searches related historical events based on a sliding event window, and achieves traceability analysis on the public opinion. The dimension reduction process may include PCA, ICA LDA, ISOMAP, LLE, etc., and for convenience of processing, in this embodiment, the dimension reduction process converts public opinion data in the public opinion classification set into binary character strings, as shown in fig. 2, the steps may include:

S21, binary conversion is carried out on public opinion data in the public opinion classification set, and the converted character strings are segmented to obtain public opinion segments;

if the public opinion classification set only comprises public opinion texts, firstly inputting the public opinion texts into an Analyzer word segmentation device for word segmentation, processing the public opinion texts into word segments, filtering dead words in the word segments to obtain effective word segments, and converting the effective word segments into 64-bit binary character strings. If the public opinion classification set comprises both public opinion texts and effective word segmentation corresponding to the public opinion texts, the effective word segmentation is directly converted into 64-bit binary character strings.

In this embodiment, in order to further increase the processing speed, the character string obtained by dimension reduction may be segmented, one of the public opinion segments is adopted to find a related historical event classification set, and then all public opinion data of the related historical event classification set are recalled and compared with the whole character string, so that the calculation amount for comparison is greatly reduced, and the processing speed is increased.

The character string can be segmented in equal length, for example, a 64-bit binary character string is segmented into 4 16-bit character strings in equal length, and each equally-divided character string is a public opinion segment. Optionally, each public opinion segment can be cached in the redis database, and the public opinion segments obtained by dimension reduction segmentation of the same public opinion text are marked by the same identifier, so that the subsequent use is convenient.

S22, searching character strings to be compared which are related to each public opinion section in a first time window;

The time window can be set according to actual needs, and the sliding time window can be adopted in this embodiment, so that the first time window and the second time window are adopted to distinguish. For example, the first time window may be set within two days from date, and the second time window may be set within two to four days from date.

For example, the step may first obtain each public opinion segment after the same public opinion text is dimension-reduced and segmented, and search the to-be-compared character strings related to the public opinion segments, which occur in the first time window, according to each public opinion segment. The searching of the character strings to be compared occurring in the first time window according to the public opinion sections can comprise obtaining character strings after the dimension reduction processing of the public opinion texts occurring in the first time window, marking the character strings as character strings to be matched, and carrying out correlation matching on each public opinion section and each character string to be matched to obtain character strings to be compared. Taking public opinion text dimension reduction segmentation as an example, 4 public opinion segments of 16-bit character strings are obtained from a redis database according to an identifier, 4 16-bit character strings after dimension reduction segmentation of the same public opinion text are obtained from the redis database, character strings to be matched after dimension reduction processing of the public opinion text occurring in a first time window are obtained from the redis database, for example, 10 64-bit character strings to be matched are obtained, each 16-bit character string is subjected to correlation matching with each 64-bit character string to be matched, and the character strings to be matched which are successfully matched are used as character strings to be compared.

S23, comparing the character string of the public opinion section with the character string to be compared according to the bit, and determining the character strings with similar public opinion data.

The character string of the public opinion section is a character string before being segmented, in this example, the character string of the public opinion section is a 64-bit character string obtained by word segmentation binary conversion of the public opinion text, in this step, the 64-bit character string is compared with each 64-bit character string to be compared according to the bit, if the number of different bits is smaller than a preset value (such as 4), the 64-bit character string to be compared is used as a character string similar to the public opinion data. Illustratively, simhash methods and emotion analysis results may be used in the comparison process for comparison.

S24, determining a historical event classification set to which the public opinion data belongs according to the character strings similar to the public opinion data.

For example, the historical event classification set to which each string similar to public opinion data belongs may be searched first, and the historical event classification set with the largest string similar to public opinion data is used as the historical event classification set to which public opinion data belongs.

And then, classifying public opinion data (such as public opinion texts, character strings corresponding to the public opinion texts and word segmentation corresponding to the public opinion texts) into a historical event classification set to which the public opinion data belong. If the character strings similar to the public opinion data do not exist, a new historical event classification set is created for the public opinion data.

Furthermore, the newly added public opinion data can be subjected to dimension reduction processing in real time, and related historical events are searched based on a sliding event window, so that early warning of fermentation and secondary fermentation of public opinion is realized. Thus, the method may further comprise:

S201, performing dimension reduction processing on the newly-added public opinion data, searching a historical event classification set to which the processed newly-added public opinion data belongs based on a first time window, classifying the newly-added public opinion data into the belonging historical event classification set, and sending event fermentation alarm information;

S202, if a historical event classification set of the processed newly-added public opinion data is not found in the first time window, replacing a preset time window with a second time window, searching the historical event classification set of the processed newly-added public opinion data based on the second time window, classifying the newly-added public opinion data into the affiliated historical event classification set, and sending event secondary fermentation alarm information;

Wherein the second time window is earlier than the first time window. The searching method of the historical event classification set to which the newly added public opinion data belongs can refer to the steps S21-S24.

Further, in order to make the tracing analysis of the event clearer, the primary fermentation is distinguished from the secondary fermentation, and the comparison mechanism of the historical event which belongs to the newly added public opinion data which is searched and processed based on the first time window is different from the comparison mechanism of the historical event which belongs to the newly added public opinion data which is searched and processed based on the second time window. The comparison mechanism can comprise a comparison method, such as simhash method, emotion analysis result, and the like, and content, such as text, character strings, account numbers, keywords, and the like.

In addition, the public opinion texts, public opinion segmentation and character strings in the same historical event classification set can be labeled with the same labels to show the distinction, and the historical event classification database is constructed through the labeled public opinion texts, public opinion segmentation and character strings, so that the later use is convenient. In order to facilitate the subsequent searching based on the historical event classification database, an inverted index can be established for the public opinion data while the historical event classification database is constructed.

S3, carrying out correlation matching on public opinion data in the same historical event classification set, and dividing the matched public opinion data into negative public opinion sets;

In this embodiment, the correlation matching may be performed by at least one of a character string, a keyword, and a public opinion publishing account. The event relevance based public opinion security analysis dimension can be supplemented by creating negative public opinion sets.

And S4, carrying out public opinion security analysis based on the public opinion classification set, the historical event classification set and the negative public opinion set.

The method is characterized in that the public opinion classification set is used for analyzing the public opinion category according to the public opinion classification set, tracing, fermenting and secondary fermentation analysis can be carried out on the public opinion according to the historical event classification set, and the repeated account, the forwarding account and the like in the public opinion generating process are tracked and analyzed according to the negative public opinion classification set, so that the complete occurrence and the fermentation process of the public opinion event are quickly restored.

Furthermore, the embodiment of the invention can search and display the user input information based on the public opinion classification set, the historical event classification set and the negative public opinion set, thereby ensuring the safety and reliability of information display to the user. Based thereon, the method further comprises:

And S5, searching and displaying target public opinion from the public opinion classification set, the historical event classification set and the negative public opinion set according to the user search information.

The user search information can be keywords, text and other information input by the user. Illustratively, as shown in FIG. 3, this step may include:

S51, matching target public opinion based on multiple dimensions from the public opinion classification set, the historical event classification set and the negative public opinion set according to user search information;

The dimension is used for matching the target public opinion from different angles, and the target public opinion can be matched from the dimensions of article title matching degree, content matching degree, popularity, tf-idf value, originality, author portrait and the like by way of example. Illustratively, the multi-dimensional matching may be performed by a matching rule or a matching model.

S52, determining the matching degree of each target public opinion according to the weight value of each dimension;

In this embodiment, a weight may be set in advance for each dimension, and the matching degree of each target public opinion may be calculated based on the score of each dimension of the target public opinion and the weight of the corresponding dimension.

In a preferred example, the matching degree of the target public opinion can be adjusted by integrating the historical search results of the user, so that the display result is closer to the user requirement, and the user experience is improved. The step can also adjust the matching degree of the target public opinion according to the historical target public opinion matched with the user search information, specifically acquire the historical target public opinion matched with the user search information, compare the historical target public opinion with the target public opinion, and adjust the matching degree of the target public opinion (namely the display sequence of the target public opinion) according to the comparison result. The matching degree of the target public opinion is adjusted according to the number of the target public opinion with the same history, for example, the number of the target public opinion with the same history is used as a dimension, a corresponding weight is set, and the matching degree is adjusted based on the dimension. Or directly adjusting the matching degree and the like according to the number of the public opinion with the same historical target.

In addition, the matching degree of the target public opinion can be adjusted according to the final public opinion selected by the user from the displayed historical target public opinion, and the step can further comprise the steps of obtaining the final public opinion selected by the user from the historical target public opinion, comparing the final public opinion with the target public opinion, and adjusting the matching degree of the target public opinion according to the comparison result.

And S53, displaying each target public opinion according to the matching degree of the target public opinion.

For example, the target public opinion is displayed from top to bottom according to the matching degree in a pull-down list mode. Or setting different display areas, and displaying target public opinion according to the significant level of the area based on the matching degree, for example, displaying the target public opinion with the maximum matching degree in the display area of the first significant level, and displaying the target public opinion with the second matching degree in the display area of the second significant level. Wherein the display level reflects how fast or slow the user finds the content of the presentation area, e.g. the content of the area in the middle of the display screen is most easily found by the user.

Furthermore, the embodiment of the invention can also analyze the user attribute based on the historical search of the public opinion classification set, the historical event classification set and the negative public opinion set of the user, construct a user portrait and push information to the user. Based thereon, the method further comprises:

s6, searching and constructing a user portrait according to the user history, and pushing information to the user based on the user image.

The user history search can comprise information such as keywords and texts input by a user, and can also comprise texts finally selected by the user in search results.

Compared with the prior art, the invention has at least the following beneficial effects:

1. The system can classify, trace, ferment and secondary ferment massive public opinion and safety information in the system database, restore the complete occurrence of events and ferment process, facilitate staff to timely prevent the events to be fermented, and ensure public opinion safety of enterprises and society.

2. And carrying out dimension reduction processing on the public opinion data, dividing the dimension-reduced public opinion data, searching related character strings to be compared by adopting one public opinion segment, and recalling the whole character string where the public opinion segment is positioned and each character string to be compared for comparison, thereby greatly reducing the calculation amount for comparison and improving the processing speed.

3. The method has the advantages that the recall quantity of the analysis process algorithm is large, the calculated amount is large, the time consumption is high, and the calculated amount cannot be reduced, so that the core idea is to reduce the recall time, and the recall of each 16-bit substring can be directly obtained from the redis by using the space exchange time, so that the inquiry is not needed, and the comparison time is greatly reduced.

4. And (3) constructing a structured massive information library, classifying the text according to the length, time, emotion attribute, word frequency-reverse file frequency, source and other attributes of the text, and warehousing and constructing an inverted index.

5. The article title matching degree, the content matching degree, the heat degree, tf-idf value, whether original or not, author portraits and other information are weighted and calculated. And meanwhile, analyzing historical search habits of the user, finding out high-frequency keywords, and carrying out secondary search and score calculation on primary search results. And calculating the search scores of the two times to obtain the final article presentation sequence.

6. And searching and displaying the user input information based on the public opinion classification set, the historical event classification set and the negative public opinion set, so that the safety and reliability of displaying the information to the user are ensured.

7. Based on different comparison mechanisms of the newly added public opinion in different time windows, the primary, secondary and multiple repeated fermentation of the event can be distinguished, and the propagation process and the link of each event can be accurately traced.

8. The method can analyze and judge behaviors such as multiple numbers and forwarding of one person in the time propagation process, find key propagation crowd, and reduce public opinion analysis errors.

9. By extracting the user search history, the user portrait is constructed, personalized user recommendation of thousands of people and thousands of sides is realized, and user search experience is optimized.

10. The method can carry out duplication removal processing on a large number of public opinion and safe texts, and can judge whether the texts are positive or negative according to the attributes of the public opinion and the safe texts.

Fig. 4 is a public opinion safety analysis device based on event correlation according to the present invention, as shown in fig. 4, the device includes:

the classification module 41 is configured to classify the public opinion data into a corresponding public opinion classification set;

The dimension-reduction searching module 42 is configured to perform dimension-reduction processing on the public opinion data in the public opinion classification set, and search a historical event classification set to which the processed public opinion data belongs based on the first time window, so as to classify the public opinion data into the historical event classification set to which the public opinion data belongs;

A first matching module 43, configured to perform relevance matching on public opinion data in the same historical event classification set, and divide the matched public opinion data into negative public opinion sets;

an analysis module 44 for performing public opinion security analysis based on the public opinion score, historical event score and the negative public opinion score.

In one embodiment, the dimension-reduction search module 42 includes:

Further, the device further comprises:

wherein the second time window is earlier than the first time window.

The comparison mechanism of the first dimension reduction search module and the second dimension reduction search module is different.

Further, the device also comprises a search display module which is used for searching and displaying target public opinion from the public opinion classification set, the historical event classification set and the negative public opinion according to the search information of the user.

In one embodiment, the search presentation module includes:

Further, the device further comprises:

In one embodiment, the multi-dimensional matching module includes:

Further, the device further comprises:

In one embodiment, the classification module comprises:

Further, the device also comprises an alarm module, which is used for sending out negative public opinion alarms if the emotion type of the classified public opinion data is negative.

Further, the public opinion data comprises media public opinion data and social network public opinion data, and the device further comprises:

It will be appreciated by those skilled in the art that the modules in the embodiments of the apparatus described above may be distributed in an apparatus as described, or may be distributed in one or more apparatuses different from the embodiments described above with corresponding changes. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.

The following describes an embodiment of an electronic device of the present invention, which may be regarded as a physical form of implementation for the above-described embodiment of the method and apparatus of the present invention. The details described in the embodiments of the electronic device according to the invention should be regarded as additions to the embodiments of the method or the apparatus described above, and the details not disclosed in the embodiments of the electronic device according to the invention may be realized by referring to the embodiments of the method or the apparatus described above.

Fig. 5 is a block diagram of an exemplary embodiment of an electronic device according to the present invention. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

As shown in fig. 5, the electronic device 500 of the exemplary embodiment is embodied in the form of a general-purpose data processing device. The components of electronic device 500 may include, but are not limited to, at least one processing unit 510, at least one memory unit 520, a bus 530 connecting the different electronic device components (including memory unit 520 and processing unit 510), a display unit 540, and the like.

The storage unit 520 stores a computer readable program, which may be a source program or code of a read only program. The program may be executed by the processing unit 510 such that the processing unit 510 performs the steps of various embodiments of the present invention. For example, the processing unit 510 may perform the steps shown in fig. 1.

The memory unit 520 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 5201 and/or cache memory unit 5202, and may further include Read Only Memory (ROM) 5203. The storage unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5205, such program modules 4205 including, but not limited to, an operating electronic device, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

Bus 530 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 500 may also communicate with one or more external devices 100 (e.g., keyboard, display, network device, bluetooth device, etc.), such that a user can interact with the electronic device 500 via the external devices 100, and/or such that the electronic device 500 can communicate with one or more other data processing devices (e.g., router, modem, etc.). Such communication may occur through an input/output (I/O) interface 550, and may also occur through a network adapter 560 to one or more networks, such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet. The network adapter 560 may communicate with other modules of the electronic device 500 via the bus 530. It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in electronic device 500, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID electronics, tape drives, data backup storage electronics, and the like.

FIG. 6 is a schematic diagram of one embodiment of a computer readable medium of the present invention. As shown in fig. 6, the computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electronic device, apparatus, or means of electronic, magnetic, optical, electromagnetic, infrared, or semiconductor, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of a readable storage medium include an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. When the computer program is executed by one or more data processing devices, the computer readable medium can realize the method of the invention, namely, classification processing is carried out on the public opinion data to be classified into corresponding public opinion classification sets, dimension reduction processing is carried out on the public opinion data in the public opinion classification sets, the public opinion data is classified into the affiliated historical event classification sets based on the history event classification sets to which the public opinion data belongs after the first time window is searched, correlation matching is carried out on the public opinion data in the same historical event classification set, the matched public opinion data is classified into negative public opinion sets, and public opinion safety analysis is carried out based on the public opinion classification sets, the history event classification sets and the negative public opinion sets.

From the above description of embodiments, those skilled in the art will readily appreciate that the exemplary embodiments described herein may be implemented in software, or may be implemented in software in combination with necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a computer readable storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, comprising several instructions to cause a data processing device (may be a personal computer, a server, or a network device, etc.) to perform the above-described method according to the present invention.

The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution electronic device, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

In summary, the present invention may be implemented in a method, apparatus, electronic device, or computer readable medium that executes a computer program. Some or all of the functions of the present invention may be implemented in practice using a general purpose data processing device such as a microprocessor or Digital Signal Processor (DSP).

The above-described specific embodiments further describe the objects, technical solutions and advantageous effects of the present invention in detail, and it should be understood that the present invention is not inherently related to any particular computer, virtual device or electronic apparatus, and various general-purpose devices may also implement the present invention. The foregoing description of the embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.

Claims

1. A public opinion security analysis method based on event correlation, the method comprising:

Obtaining each public opinion segment after the dimension reduction of the same public opinion text, obtaining the character string after the dimension reduction treatment of the public opinion text occurring in a first time window, marking the character string as a character string to be matched, carrying out correlation matching on each public opinion segment and each character string to be matched to obtain a character string to be compared, comparing the character string of the public opinion segment with the character string to be compared according to the bit, if the number of different bits is smaller than a preset value, taking the character string to be compared as the character string similar to the public opinion data, searching the historical event classification set to which each character string similar to the public opinion data belongs, taking the historical event classification set with the most character strings similar to the public opinion data as the historical event classification set to which the public opinion data belongs, and classifying the public opinion data into the historical event classification set to which the public opinion data belongs;

2. The method according to claim 1, wherein the method further comprises:

wherein the second time window is earlier than the first time window.

3. The method of claim 2, wherein a comparison mechanism based on the first time window to find a historical event to which the processed newly-added public opinion data belongs is different from a comparison mechanism based on the second time window to find a historical event to which the processed newly-added public opinion data belongs.

4. The method of claim 1, further comprising searching and presenting target public opinion from the public opinion classification set, historical event classification set, and negative public opinion set based on user search information.

5. The method of claim 4, wherein said locating and presenting target public opinion from the public opinion classification set, historical event classification set, and negative public opinion set based on user search information comprises:

6. The method of claim 5, wherein before the displaying each target public opinion according to the matching degree of the target public opinion, the method further comprises:

7. The method of claim 5, wherein matching target public opinion based on multiple dimensions from the public opinion classification set, historical event classification set, and negative public opinion set according to user search information comprises:

8. The method according to claim 4, wherein the method further comprises:

constructing a user portrait according to the user history search;

information is pushed to a user based on the user image.

9. The method of claim 1, wherein classifying the public opinion data into corresponding public opinion classification sets comprises:

10. The method of claim 9, wherein if the emotion type of the classified public opinion data is negative, a negative public opinion alert is issued.

11. The method of claim 9, wherein the public opinion data comprises media public opinion data and social network public opinion data, the method further comprising:

12. A public opinion safety analysis device based on event correlation, the device comprising:

The system comprises a dimension reduction search module, a history event classification set and a comparison module, wherein the dimension reduction search module is used for binary conversion of public opinion data in a public opinion classification set, dividing the converted character strings to obtain public opinion segments, acquiring each public opinion segment after dimension reduction division of the same public opinion text, acquiring character strings after dimension reduction processing of the public opinion text occurring in a first time window, recording the character strings as character strings to be matched, carrying out correlation matching on each public opinion segment and each character string to be matched to obtain character strings to be compared, comparing the character strings where the public opinion segments are positioned with the character strings to be compared according to the bits, if the number of different bits is smaller than a preset value, taking the character strings to be compared as character strings similar to the public opinion data, searching a history event classification set to which each character string similar to the public opinion data belongs, taking the history event classification set with the most character strings similar to the public opinion data as the history event classification set to which the public opinion data belongs;

13. The apparatus of claim 12, wherein the apparatus further comprises:

wherein the second time window is earlier than the first time window.

14. The apparatus of claim 13, wherein the comparison mechanism of the first dimension reduction lookup module and the second dimension reduction lookup module is different.

15. The apparatus of claim 12, further comprising a search presentation module for searching and presenting target public opinion from the public opinion classification set, historical event classification set, and negative public opinion set based on user search information.

16. The apparatus of claim 15, wherein the search presentation module comprises:

17. The apparatus of claim 16, wherein the apparatus further comprises:

18. The apparatus of claim 16, wherein the multi-dimensional matching module comprises:

19. The apparatus of claim 15, wherein the apparatus further comprises:

20. The apparatus of claim 12, wherein the classification module comprises:

21. The apparatus of claim 20, further comprising an alarm module for issuing a negative public opinion alarm if the emotion type of the classified public opinion data is negative.

22. The apparatus of claim 20, wherein the public opinion data comprises media public opinion data and social network public opinion data, the apparatus further comprising:

23. An electronic device, comprising:

Processor, and

A memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-11.

24. A computer readable storage medium storing one or more programs, which when executed by a processor, implement the method of any of claims 1-11.