Public opinion data processing method and device, equipment and storage medium
By cleaning and modeling the omni-channel data, and generating public opinion text data, the problem of old technical architecture of the public opinion system is solved, accurate and timely public opinion risk control services are achieved in the field of financial information, and the users' efficient data service needs are met. .
Patent Information
- Application Number
- CN202411263307.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-10
- Publication Date
- 2024-12-27
AI Technical Summary
The existing public opinion system technical structure is old and it is difficult to support the public opinion risk control service needs of banking, enterprises, industries and government users, and it is impossible to provide accurate, timely and effective professional information.
After obtaining omni-channel data for cleaning, it is input to the image recognition model, public opinion voice model, business environment model or risk prediction model, generate public opinion text data and store it in the database for search and analysis to provide accurate public opinion risk control services . The model ensures data quality through entity extraction, verification and update.
It realizes accurate processing and risk control services for public opinion data, can effectively avoid risks and meet the needs of efficient data services in the financial information field.
Smart Images
Figure CN119202229A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment and storage medium for processing public opinion data. Background Art
[0002] In the field of financial information, more accurate, authoritative, timely and effective professional information plays a decisive role in reference and can effectively avoid risks. The technical architecture of the public opinion system on the market is relatively old, and data services can no longer support the public opinion risk control service needs of a large number of banking, corporate, industry and government users.
[0003] To sum up, how to process data and provide accurate public opinion risk control services is a problem that technical personnel in this field urgently need to solve. Summary of the invention
[0004] In view of this, the present application provides a public opinion data processing method, device, equipment and storage medium, aiming to process the data and provide accurate public opinion risk control services.
[0005] In a first aspect, the present application provides a method for processing public opinion data, comprising:
[0006] Access omnichannel data;
[0007] Performing data cleansing on the omni-channel data;
[0008] Input the cleaned omni-channel data into the image recognition model, or the public opinion volume model, or the business environment model, or the risk prediction model to obtain the public opinion text data;
[0009] Storing the public opinion text data in a database;
[0010] In response to the search instruction, a search is performed in the database to obtain search result data.
[0011] Optionally, storing the public opinion text data in a database includes:
[0012] Storing the public opinion text data in a full-text retrieval library;
[0013] Analyze the public opinion text data to obtain analysis result data;
[0014] The analysis result data is stored in an analysis result library.
[0015] Optionally, in response to the search instruction, searching in the database to obtain search data includes:
[0016] In response to the full-text search instruction, searching in the full-text search library to obtain search result data;
[0017] In response to the result retrieval instruction, a search is performed in the analysis result library to obtain retrieval result data.
[0018] Optionally, the method further includes:
[0019] Use image recognition models to extract entities from image samples;
[0020] Test the entity extraction effect and obtain the first test result;
[0021] If the first inspection result is unsatisfactory, training the image recognition model using the image sample to update the image recognition model;
[0022] The step of extracting entities from image samples using the image recognition model is re-executed until the first inspection result meets the standard.
[0023] Optionally, the method further includes:
[0024] Input the business environment samples into the business environment model to obtain the business environment index and ranking;
[0025] Testing the business environment index and ranking to obtain a second test result;
[0026] If the second inspection result is unsatisfactory, training the business environment model using the business environment sample to update the business environment model;
[0027] Re-execute the step of inputting the business environment sample into the business environment model to obtain the business environment index and ranking until the second test result meets the standard.
[0028] Optionally, the method further includes:
[0029] Using the risk prediction model to predict the risk of risk prediction samples;
[0030] Testing the risk prediction effect to obtain a third test result;
[0031] If the third test result is not up to standard, the risk prediction model is trained using the risk prediction sample to update the risk prediction model;
[0032] The step of performing risk prediction on the risk prediction sample using the risk prediction model is re-executed until the third test result meets the standard.
[0033] Optionally, after performing a search in the database in response to the search instruction and obtaining the search result data, the method further includes:
[0034] The search result data is pushed.
[0035] In a second aspect, the present application provides a public opinion data processing device, comprising:
[0036] Acquisition module, used to obtain omni-channel data;
[0037] A data cleaning module, used for cleaning the omni-channel data;
[0038] The first input module is used to input the cleaned omni-channel data into an image recognition model, a public opinion volume model, a business environment model, or a risk prediction model to obtain public opinion text data;
[0039] A storage module, used to store the public opinion text data in a database;
[0040] The retrieval module is used to respond to the retrieval instruction, search in the database, and obtain retrieval result data.
[0041] Optionally, the storage module includes:
[0042] A first storage unit, used to store the public opinion text data in a full-text retrieval library;
[0043] An analysis unit, used to analyze the public opinion text data to obtain analysis result data;
[0044] The second storage unit is used to store the analysis result data in an analysis result library.
[0045] Optional, retrieval module, including:
[0046] A first retrieval unit, configured to perform a search in the full-text retrieval library in response to a full-text retrieval instruction to obtain retrieval result data;
[0047] The second retrieval unit is used to respond to the result retrieval instruction and perform a search in the analysis result library to obtain retrieval result data.
[0048] Optionally, the device further comprises:
[0049] An entity extraction module is used to extract entities from image samples using an image recognition model;
[0050] A first inspection module is used to inspect the entity extraction effect and obtain a first inspection result;
[0051] A first updating module, configured to train the image recognition model using the image sample to update the image recognition model if the first inspection result is not up to standard;
[0052] The first execution module is used to re-execute the step of extracting entities from image samples using the image recognition model until the first inspection result meets the standard.
[0053] Optionally, the device further comprises:
[0054] The second input module is used to input the business environment sample into the business environment model to obtain the business environment index and ranking;
[0055] A second testing module is used to test the business environment index and ranking to obtain a second testing result;
[0056] A second updating module, configured to train the business environment model using the business environment sample to update the business environment model if the second inspection result is not up to standard;
[0057] The second execution module is used to re-execute the step of inputting the business environment sample into the business environment model to obtain the business environment index and ranking until the second test result meets the standard.
[0058] Optionally, the device further comprises:
[0059] A risk prediction module is used to perform risk prediction on risk prediction samples using a risk prediction model;
[0060] The third testing module is used to test the risk prediction effect and obtain a third testing result;
[0061] A third updating module, configured to train the risk prediction model using the risk prediction sample to update the risk prediction model if the third test result is not up to standard;
[0062] The third execution module is used to re-execute the step of using the risk prediction model to perform risk prediction on the risk prediction sample until the third test result meets the standard.
[0063] Optionally, the device further comprises:
[0064] The push module is used to push the search result data.
[0065] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, a method for processing public opinion data as described in any one of the first aspects of the embodiments of the present application is implemented.
[0066] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal device, the terminal device executes a public opinion data processing method as described in any one of the first aspects of the embodiment of the present application.
[0067] The present application provides a method for processing public opinion data. When executing the method, first obtain omni-channel data, then input the obtained omni-channel data into an image recognition model, or a public opinion volume model, or a business environment model, or a risk prediction model, and obtain public opinion text data. Finally, store the public opinion text data in a database, and respond to a search instruction to search in the database to obtain search result data. In this way, the omni-channel data is processed by an image recognition model, or a public opinion volume model, or a business environment model, or a risk prediction model to obtain public opinion text data, and use the public opinion text data to provide accurate public opinion risk control services. BRIEF DESCRIPTION OF THE DRAWINGS
[0068] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0069] Figure 1 A flowchart of a method for processing public opinion data provided in an embodiment of the present application;
[0070] Figure 2 A schematic diagram of the structure of a public opinion data processing device provided in an embodiment of the present application;
[0071] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0072] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. This application provides a method, device, equipment and storage medium for processing public opinion data, which are used in the field of data processing technology. The above is only an example and does not limit the application field of the method and device name provided in this application.
[0073] In the field of financial information, more accurate, authoritative, timely and effective professional information plays a decisive role in reference and can effectively avoid risks. The technical architecture of the public opinion system on the market is relatively old, and data services can no longer support the public opinion risk control service needs of a large number of banking, corporate, industry and government users.
[0074] After research, the inventor proposed the technical solution of this application. When executing the method, first obtain omni-channel data, then input the cleaned omni-channel data into the image recognition model, or the public opinion volume model, or the business environment model, or the risk prediction model to obtain the public opinion text data, and finally store the public opinion text data in the database, respond to the search instruction, search in the database, and obtain the search result data. In this way, the omni-channel data is processed by the image recognition model, or the public opinion volume model, or the business environment model, or the risk prediction model to obtain the public opinion text data, and use the public opinion text data to provide accurate public opinion risk control services.
[0075] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. It should be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.
[0076] See also Figure 1 , Figure 1 A flowchart of a method for processing public opinion data provided in an embodiment of the present application includes:
[0077] S101: Get omnichannel data.
[0078] Data is obtained from various channels on the Internet, where the data may include Internet text, business data, images, audio and video, etc. In some implementations, the data sources may be classified according to the following dimensions: communication channel dimension, media weight dimension, industry field dimension, and region of origin dimension.
[0079] S102: Perform data cleansing on all channel data.
[0080] Data cleaning is performed on the acquired omni-channel data. Data cleaning may include: automatic word segmentation, automatic clustering, automatic classification, rule classification, mixed classification, text similarity retrieval, automatic summarization, subject word indexing, common sense proofreading, information filtering, pinyin, homophone retrieval, related phrase retrieval, and natural language retrieval. In the multiple stages of public opinion data processing, preprocessing before and after data collection is included. For example, data cleaning of data in web pages can exclude junk information in web pages, such as text in navigation columns, advertisements, and copyright information, so that the main text content in the web page is filtered out.
[0081] S103: Input the cleaned omni-channel data into an image recognition model, a public opinion volume model, a business environment model, or a risk prediction model to obtain public opinion text data.
[0082] The cleaned omni-channel data is input into the comprehensive model to output the public opinion text data, wherein the comprehensive model includes one or more of an image recognition model, a public opinion volume model, a business environment model, and a risk prediction model.
[0083] In some embodiments, the image recognition model may be verified and updated first to improve the accuracy of the image recognition model output. Specifically, the step of verifying and updating the image recognition model includes: first using the image recognition model to extract entities from image samples, then verifying the entity extraction effect, and obtaining a first verification result. If the first verification result is not up to standard, the image recognition model is trained using the image sample to update the image recognition model, and then the image recognition model is used again to extract image samples and verify the entity extraction effect. If the verification result is not up to standard, the verification and update steps are repeated until the verification result is up to standard.
[0084] In some implementations, the business environment model may be verified and updated first to improve the accuracy of the business environment model output. Specifically, the step of verifying and updating the business environment model includes: first inputting the business environment sample into the business environment model to obtain the business environment index and ranking, then verifying the business environment index and ranking to obtain a second verification result, if the second verification result is not up to standard, then using the business environment sample to train the business environment model to update the business environment model, then re-inputting the business environment sample into the business environment model to obtain the business environment index and ranking, and verifying the business environment index and ranking, if the verification result is not up to standard, then repeating the verification and updating steps until the verification result is up to standard.
[0085] In some embodiments, the risk prediction model can be verified and updated first to improve the accuracy of the risk prediction model output. Specifically, the step of verifying and updating the risk prediction model includes: first using the risk prediction model to perform risk prediction on the risk prediction sample, then testing the risk prediction effect to obtain a third test result, if the third test result is not up to standard, then using the risk prediction sample to train the risk prediction model to update the risk prediction model, then reusing the risk prediction model to perform risk prediction on the risk prediction sample, and testing the risk prediction effect, if the test result is not up to standard, then repeating the verification and updating steps until the test result is up to standard.
[0086] S104: Storing the public opinion text data in a database.
[0087] The generated public opinion text data is stored in a database. In some implementations, the public opinion text data can be stored in a full-text retrieval library. At the same time, the public opinion text data is analyzed to obtain analysis result data, and the analysis result data is stored in the analysis result library. The full-text retrieval library and the analysis result library are the same in that they can realize text retrieval of all fields of the data and statistical analysis result data. The data they store are different. The full-text retrieval library is a full database and can retrieve all data. The advantage is that the data volume is larger and more complete, and it supports data statistical analysis. The analysis result library is a result database after analysis, which can display better data on the platform. It also supports data statistical analysis, and its advantage is that the data is small and precise. For example, when the full-text retrieval library receives a search instruction, it can search the full-text field of the database and quickly return relevant data. Similarly, the result library will quickly return relevant data after receiving the search instruction.
[0088] S105: In response to the search instruction, search is performed in the database to obtain search result data.
[0089] In response to the search instruction, a search is performed in the database to obtain search result data. If the search instruction is a full-text search instruction, a search is performed in the full-text search library to obtain search result data; if the search instruction is a result search instruction, a search is performed in the analysis result library to obtain search result data. In some embodiments, after obtaining the search result data, the search result data can also be pushed.
[0090] In the embodiment provided by the present application, when executing the method, first obtain omni-channel data, then input the obtained omni-channel data into the image recognition model, or the public opinion volume model, or the business environment model, or the risk prediction model, and obtain the public opinion text data. Finally, the public opinion text data is stored in the database, and in response to the search instruction, the database is searched to obtain the search result data. In this way, the omni-channel data is processed by the image recognition model, or the public opinion volume model, or the business environment model, or the risk prediction model to obtain the public opinion text data, and the public opinion text data is used to provide accurate public opinion risk control services.
[0091] The above are some specific implementations of the public opinion data processing method provided in the embodiment of the present application. Based on this, the present application also provides a corresponding device. The device provided in the embodiment of the present application will be introduced from the perspective of functional modularization.
[0092] See also Figure 2 , Figure 2 A schematic diagram of the structure of a public opinion data processing device provided in an embodiment of the present application, the public opinion data processing device 200 includes:
[0093] An acquisition module 210 is used to acquire omni-channel data;
[0094] A data cleaning module 220, used for cleaning the omni-channel data;
[0095] The first input module 230 is used to input the cleaned omni-channel data into an image recognition model, a public opinion volume model, a business environment model, or a risk prediction model to obtain public opinion text data;
[0096] Storage module 240, used to store the public opinion text data in a database;
[0097] The retrieval module 250 is used to respond to the retrieval instruction and perform a search in the database to obtain retrieval result data.
[0098] Optionally, the storage module 240 includes:
[0099] A first storage unit, used to store the public opinion text data in a full-text retrieval library;
[0100] An analysis unit, used to analyze the public opinion text data to obtain analysis result data;
[0101] The second storage unit is used to store the analysis result data in an analysis result library.
[0102] Optionally, the retrieval module 250 includes:
[0103] A first retrieval unit, configured to perform a search in the full-text retrieval library in response to a full-text retrieval instruction to obtain retrieval result data;
[0104] The second retrieval unit is used to respond to the result retrieval instruction and perform a search in the analysis result library to obtain retrieval result data.
[0105] Optionally, the device 200 further includes:
[0106] An entity extraction module is used to extract entities from image samples using an image recognition model;
[0107] A first inspection module is used to inspect the entity extraction effect and obtain a first inspection result;
[0108] A first updating module, configured to train the image recognition model using the image sample to update the image recognition model if the first inspection result is not up to standard;
[0109] The first execution module is used to re-execute the step of extracting entities from image samples using the image recognition model until the first inspection result meets the standard.
[0110] Optionally, the device 200 further includes:
[0111] The second input module is used to input the business environment sample into the business environment model to obtain the business environment index and ranking;
[0112] A second testing module is used to test the business environment index and ranking to obtain a second testing result;
[0113] A second updating module, configured to train the business environment model using the business environment sample to update the business environment model if the second inspection result is not up to standard;
[0114] The second execution module is used to re-execute the step of inputting the business environment sample into the business environment model to obtain the business environment index and ranking until the second test result meets the standard.
[0115] Optionally, the device 200 further includes:
[0116] A risk prediction module is used to perform risk prediction on risk prediction samples using a risk prediction model;
[0117] The third testing module is used to test the risk prediction effect and obtain a third testing result;
[0118] A third updating module, configured to train the risk prediction model using the risk prediction sample to update the risk prediction model if the third test result is not up to standard;
[0119] The third execution module is used to re-execute the step of using the risk prediction model to perform risk prediction on the risk prediction sample until the third test result meets the standard.
[0120] Optionally, the device 200 further includes:
[0121] The push module is used to push the search result data.
[0122] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.
[0123] like Figure 3 As shown, the computer device 01 is in the form of a general computing device. The components of the computer device 01 may include but are not limited to: one or more processors or processing units 03, a system memory 08, and a bus 04 connecting different system components (including the system memory 08 and the processing unit 03).
[0124] Bus 04 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.
[0125] The computer device 01 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 01, including volatile and non-volatile media, removable and non-removable media.
[0126] The system memory 08 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 09 and / or cache memory 10. The computer device 01 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 11 may be used to read and write non-removable, non-volatile magnetic media ( Figure 3 not shown, usually called a "hard drive"). Although Figure 3Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 04 via one or more data medium interfaces. The memory 08 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.
[0127] A program / utility 12 having a set (at least one) of program modules 13 may be stored, for example, in the memory 08, such program modules 13 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. The program modules 13 generally perform the functions and / or methods of the embodiments described herein.
[0128] The computer device 01 may also communicate with one or more external devices 02 (e.g., keyboard, pointing device, display 07, etc.), one or more devices that enable a user to interact with the computer device 01, and / or any device that enables the computer device 01 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via input / output (I / O) interface 06. Furthermore, the computer device 01 may also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet) via network adapter 05. Figure 3 As shown, the network adapter 05 communicates with other modules of the computer device 01 via the bus 04. It should be understood that although Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with computer device 01, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0129] The processor unit 03 executes various functional applications and public opinion data processing by running the programs stored in the system memory 08, for example, implementing a public opinion data processing method provided in an embodiment of the present application.
[0130] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.
[0131] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.
[0132] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.
[0133] The above description is merely an exemplary embodiment of the present application and is not intended to limit the protection scope of the present application.
Claims
1. A method for processing public opinion data, characterized in that: include: Access omnichannel data; Performing data cleansing on the omni-channel data; Input the cleaned omni-channel data into the image recognition model, or the public opinion volume model, or the business environment model, or the risk prediction model to obtain the public opinion text data; Storing the public opinion text data in a database; In response to the search instruction, a search is performed in the database to obtain search result data.
2. The method according to claim 1, characterized in that The storing of the public opinion text data into a database comprises: Storing the public opinion text data in a full-text retrieval library; Analyze the public opinion text data to obtain analysis result data; The analysis result data is stored in an analysis result library.
3. The method according to claim 2, characterized in that The step of searching the database in response to the search instruction to obtain search data includes: In response to the full-text search instruction, searching in the full-text search library to obtain search result data; In response to the result retrieval instruction, a search is performed in the analysis result library to obtain retrieval result data.
4. The method according to claim 1, characterized in that: The method further comprises: Use image recognition models to extract entities from image samples; Test the entity extraction effect and obtain the first test result; If the first inspection result is unsatisfactory, training the image recognition model using the image sample to update the image recognition model; The step of extracting entities from image samples using the image recognition model is re-executed until the first inspection result meets the standard.
5. The method according to claim 1, characterized in that The method further comprises: Input the business environment samples into the business environment model to obtain the business environment index and ranking; Testing the business environment index and ranking to obtain a second test result; If the second inspection result is unsatisfactory, training the business environment model using the business environment sample to update the business environment model; Re-execute the step of inputting the business environment sample into the business environment model to obtain the business environment index and ranking until the second test result meets the standard.
6. The method according to claim 1, characterized in that The method further comprises: Using the risk prediction model to predict the risk of risk prediction samples; Testing the risk prediction effect to obtain a third test result; If the third test result is not up to standard, the risk prediction model is trained using the risk prediction sample to update the risk prediction model; The step of performing risk prediction on the risk prediction sample using the risk prediction model is re-executed until the third test result meets the standard.
7. The method according to claim 1, characterized in that After the search is performed in the database in response to the search instruction and the search result data is obtained, the method further comprises: The search result data is pushed.
8. A public opinion data processing device, characterized in that: include: Acquisition module, used to obtain omni-channel data; A data cleaning module, used for cleaning the omni-channel data; The first input module is used to input the cleaned omni-channel data into an image recognition model, a public opinion volume model, a business environment model, or a risk prediction model to obtain public opinion text data; A storage module, used to store the public opinion text data in a database; The retrieval module is used to respond to the retrieval instruction, search in the database, and obtain retrieval result data.
9. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for processing public opinion data as described in any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the public opinion data processing method according to any one of claims 1 to 7.