CN111259057A - Data processing method and device for civil appeal analysis - Google Patents
Data processing method and device for civil appeal analysis Download PDFInfo
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Abstract
The application discloses a data processing method and device for civil appeal analysis, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring appeal information of a user and extracting appeal characteristic information of the user; matching the appeal characteristic information of the user with a preset appeal characteristic set; determining one or more appeal dimensions to which the appeal information of the user belongs according to the matching result; and performing clustering analysis on the users according to the appeal dimensions to obtain a user group corresponding to each appeal dimension. The method and the device solve the technical problem that in the related technology, due to the lack of multi-dimensional statistical analysis on the civil appeal information, related departments and personnel cannot effectively manage the civil appeal. Through the method and the device, the purpose of carrying out multi-dimensional statistical analysis on the civil appeal information is achieved, and the technical effect of helping relevant departments and personnel to effectively manage the civil appeal is achieved.
Description
Technical Field
The application relates to the technical field of statistical analysis of civil appetitions, in particular to a data processing method and device, an electronic device and a readable storage medium for the analysis of the civil appetitions.
Background
In recent years, the field of civil appeal is becoming more and more extensive, and the range and the number of the civil appeal are on a continuous increasing trend. The specific content is rapidly expanded from the previous demand of only the full-bodied income to various aspects of social life, such as price of things, room price, education, employment, endowment, medical health, social security, infrastructure construction, public security, traffic, culture, ecological environment protection and the like, and the method has the advantages of wide range, large quantity and high speed increase. From the important point of appeal, according to relevant research, the most urgent problem to be solved by the current masses is that 72.5% of people select 'poor and rich disparities', 42% of people select 'guarantee employment', 40.1% of people select 'medical care', 31.4% of people select 'education fairness', 31.4% of people select 'house price is too high', 26.5% of people select 'general goods expansion', 21.6% of people select 'care guarantee', and 10.8% of people select 'benefit compensation'.
At present, for the civil appeal information of each dimension in each field, the relevant departments are usually obtained by means of questionnaire survey, network personnel inquiry and offline voting boxes, so that real and dynamic civil problems and actual demands are difficult to receive, the psychological stability state of a social group, the occurrence probability of a group crisis event, the main demands, ideas and willingness of the people are difficult to dynamically know and control, the psychological state, emotional state and the like of the group are difficult to achieve, and the possibility of performing subsequent data management on the civil appeal information is also unavailable. In addition, the related departments have difficulty in classifying and searching the civil appeal according to the crowd characteristics such as different regions, professions, sexes, ages and the like.
Aiming at the problem that in the related technology, due to the lack of multi-dimensional statistical analysis on the civil appeal information, related departments and personnel cannot effectively manage the civil appeal, an effective solution is not provided at present.
Disclosure of Invention
The application mainly aims to provide a data processing method and device for civil appeal analysis, an electronic device and a readable storage medium, so as to solve the problem that in the related technology, due to the lack of multi-dimensional statistical analysis on the civil appeal information, related departments and personnel cannot effectively manage the civil appeal.
To achieve the above object, according to a first aspect of the present application, there is provided a data processing method for civil appeal analysis.
The data processing method for the civil appeal analysis comprises the following steps: acquiring appeal information of a user and extracting appeal characteristic information of the user, wherein the appeal information of the user comprises any one or more of user personal information, psychological information, physiological information and user behavior information; matching the appeal characteristic information of the user with a preset appeal characteristic set; determining one or more appeal dimensions to which the appeal information of the user belongs according to the matching result; and performing clustering analysis on the users according to the appeal dimensions to obtain a user group corresponding to each appeal dimension.
Further, the obtaining appeal information of the user and extracting appeal feature information of the user includes: acquiring human-computer interaction information, wherein the human-computer interaction information comprises appeal information of the user; and performing semantic analysis on the human-computer interaction information to extract appeal characteristic information of the user.
Further, after the obtaining the appeal information of the user and extracting the appeal feature information of the user, the method includes: matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result; comparing the feature matching result with a preset appeal dimension threshold value to determine one or more appeal dimensions to which the appeal information of the user belongs.
Further, the user behavior information includes chat statement information, and performing cluster analysis on the user according to the appeal dimensions to obtain a user group corresponding to each appeal dimension includes: performing word segmentation processing and intention analysis on the chat statement information to extract appeal characteristic information of the user; matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result; comparing the feature matching result with a preset appeal dimension threshold value to determine one or more appeal dimensions to which the appeal information of the user belongs.
Further, the determining, according to the matching result, one or more appeal dimensions to which the appeal information of the user belongs includes: determining attribute information of a user group corresponding to each appeal dimension, wherein the attribute information comprises any one or more of region, occupation, gender and age; and classifying and displaying the appeal dimensionality of the user group according to the attribute information of the user group.
In order to achieve the above object, according to a second aspect of the present application, there is provided a data processing apparatus for consumer appeal analysis.
The data processing device for civil appeal analysis according to the application comprises: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring appeal information of a user and extracting appeal characteristic information of the user, and the appeal information of the user comprises any one or more of personal information, psychological information, physiological information and user behavior information of the user; the first matching module is used for matching the appeal characteristic information of the user with a preset appeal characteristic set; the first determination module is used for determining one or more appeal dimensions to which the appeal information of the user belongs according to the matching result; and the clustering module is used for carrying out clustering analysis on the users according to the appeal dimensions so as to obtain a user group corresponding to each appeal dimension.
Further, the obtaining module comprises: the acquisition unit is used for acquiring human-computer interaction information, and the human-computer interaction information comprises appeal information of the user; and the extraction unit is used for carrying out semantic analysis on the human-computer interaction information so as to extract appeal characteristic information of the user.
Further, the apparatus further comprises: the second matching module is used for matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result; the first comparison module is used for comparing the feature matching result with a preset appeal dimension threshold value so as to determine one or more appeal dimensions to which the appeal information of the user belongs.
In order to achieve the above object, according to a third aspect of the present application, there is provided an electronic apparatus comprising: one or more processors; storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method as in any preceding item
To achieve the above object, according to a fourth aspect of the present application, there is provided a non-transitory readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method according to any of the preceding claims.
In the embodiment of the application, the appeal information of a user is obtained and the appeal characteristic information of the user is extracted, wherein the appeal information of the user comprises any one or more of user personal information, psychological information, physiological information and user behavior information; matching the appeal characteristic information of the user with a preset appeal characteristic set; the method comprises the steps of determining one or more appeal dimensions to which appeal information of a user belongs according to a matching result, performing cluster analysis on the user according to the appeal dimensions to obtain a user group corresponding to each appeal dimension, achieving the purpose of performing multi-dimensional statistical analysis on the civil appeal information, achieving the technical effect of helping related departments and personnel to effectively manage the civil appeal, and further solving the technical problem that the related departments and personnel cannot effectively manage the civil appeal due to the lack of multi-dimensional statistical analysis on the civil appeal information in the related technology.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
fig. 1 is a schematic flow chart of a data processing method for civil appeal analysis according to a first embodiment of the present application;
fig. 2 is a schematic flow chart of a data processing method for civil appeal analysis according to a second embodiment of the present application;
fig. 3 is a schematic flow chart of a data processing method for civil appeal analysis according to a third embodiment of the present application;
fig. 4 is a schematic flow chart of a data processing method for civil appeal analysis according to a fourth embodiment of the present application;
fig. 5 is a schematic flow chart of a data processing method for civil appeal analysis according to a fifth embodiment of the present application;
fig. 6 is a schematic diagram of a data processing apparatus for civil appeal analysis according to an embodiment of the present application; and
fig. 7 is a schematic diagram of a composition structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
According to an embodiment of the present invention, a data processing method for consumer appeal analysis is provided, as shown in fig. 1, the method includes the following steps S101 to S104:
step S101, obtaining appeal information of a user and extracting appeal characteristic information of the user, wherein the appeal information of the user comprises any one or more of user personal information, psychological information, physiological information and user behavior information.
In specific implementation, firstly, the people's appeal information of a certain number of users needs to be acquired, specifically, the people can perform human-computer interaction with the users through an artificial intelligent psychological robot, and important information is collected in the process, wherein the important information includes user personal information (age, sex, residence, occupation, academic calendar, income level, family condition, marital condition and the like), psychological information (psychological disturbance, psychological state, psychological crisis condition, psychological trait, psychological stability, psychological ability, psychological tendency, psychological disease history and the like), physiological information (height, weight, blood type, blood pressure, blood sugar, blood fat, physiological disease history and the like), user behavior information (behavior track, action mode, operation mode, chat statement and the like) and the like.
And then, carrying out feature extraction on the acquired civil appeal information, wherein the features in the aspect of the civil appeal mainly comprise: psychological distress, psychological state, psychological traits, psychological stability, psychological disposition, and physiological and behavioral characteristics. In addition, feature extraction is carried out on the text of the user actually speaking with the robot.
And S102, matching the appeal characteristic information of the user with a preset appeal characteristic set.
In specific implementation, an appeal feature set is constructed in advance according to historical appeal information of the civil appeal, and the feature set mainly comprises but is not limited to social psychological stability, crisis events and occurrence probability thereof, main psychological troubles, civil contradictions, reasonable civil appeal expression behaviors such as visiting or right-of-maintenance appeal and the like, unreasonable or illegal civil appeal expression behaviors, interest, moral and value orientation deviation, distribution relations of interests of different groups, unfairness of appeal among different groups, unfairness and unfairness of expression among different civil subjects, expression of civil appeal of vulnerable groups and the like.
And combining the extracted user appeal feature information with the preset appeal feature for matching so as to judge the preset appeal feature dimension in the user appeal feature information.
Step S103, determining one or more appeal dimensions to which the appeal information of the user belongs according to the matching result.
In specific implementation, if the appeal feature information of the user is matched with one or more specific appeal dimensions in the preset appeal feature set, that is, the appeal information of one user may include appeal features of multiple dimensions, it is considered that the appeal information of the user includes the one or more appeal dimensions matched with the appeal information of the user, that is, the association relationship between the user and the one or more appeal dimensions matched with the appeal information is established.
And S104, performing clustering analysis on the users according to the appeal dimensions to obtain a user group corresponding to each appeal dimension.
In specific implementation, after the appeal dimensions of each user are divided and judged according to the above process, the users need to be subjected to cluster analysis according to different appeal dimensions, and then dynamic data of user groups with different appeal dimensions are obtained. In addition, clustering classification can be performed according to personal attribute information of the users, such as age, gender, regions and the like, so that the appealing dimension of user groups with different genders, different regions and different age groups can be obtained. Through the process, the system statistical analysis can be carried out on the appeal information of different dimensions based on human-computer interaction and big data, so that the relevant departments and personnel can be helped to effectively manage the civil appeal, and the processing efficiency of the civil appeal of the relevant departments and personnel is improved.
As a preferred implementation manner of the embodiment of the present application, as shown in fig. 2, the acquiring the appeal information of the user and extracting the appeal feature information of the user includes steps S201 to S202 as follows:
step S201, human-computer interaction information is obtained, wherein the human-computer interaction information comprises appeal information of the user.
When the artificial intelligence psychological robot is specifically implemented, the civil appeal information can be in man-machine interaction with the user through the artificial intelligence psychological robot, and important information in the interaction process is collected, so that the appeal information of the user is obtained.
Step S202, semantic analysis is carried out on the human-computer interaction information so as to extract appeal characteristic information of the user.
In specific implementation, semantic analysis processing needs to be performed on the human-computer interaction information to obtain the appeal characteristic information of the user, including but not limited to the social and psychological stability of the user, crisis events and occurrence probability thereof, main psychological troubles, civil contradiction, reasonable civil appeal expression behaviors such as visiting or right-of-maintenance appeal and the like, unreasonable or illegal civil appeal expression behaviors, interest, moral and value orientation deviation, distribution relations of interests of different groups, unfairness of appeal among different groups, unfairness and unfairness of expression among different civil subjects, expression of civil appeal of vulnerable groups, psychological state of the user, emotional state and the like.
As a preferred implementation manner of the embodiment of the present application, as shown in fig. 3, after obtaining the appeal information of the user and extracting the appeal feature information of the user, the method includes steps S301 to S302 as follows:
step S301, matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result.
In specific implementation, after the appeal feature information of the user is obtained, the appeal feature information of the user needs to be matched with each appeal feature in a preset appeal feature set, and then a plurality of matching results are obtained.
Step S302, comparing the feature matching result with a preset appeal dimension threshold to determine one or more appeal dimensions to which the appeal information of the user belongs.
In specific implementation, the obtained multiple matching results are respectively compared with a preset matching threshold, if the matching results exceed the threshold, the appeal information of the user is considered to contain the appeal dimension, and if the matching results do not exceed the threshold, the appeal information of the user is considered not to contain the appeal dimension.
As a preferred implementation manner of the embodiment of the present application, as shown in fig. 4, the user behavior information includes chat statement information, and performing cluster analysis on the user according to the appeal dimensions to obtain a user group corresponding to each appeal dimension includes steps S401 to S403:
step S401, performing word segmentation processing and intention analysis on the chat statement information to extract appeal feature information of the user.
In specific implementation, the obtained user behavior information comprises chat information of the user and the machine, and the chat text of the user is subjected to word segmentation, intention analysis and word slot analysis, so that the appeal intention in the user chat sentence is mined out and serves as appeal characteristic information of the user.
And S402, matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result.
In specific implementation, after the appeal feature information of the user is obtained, the appeal feature information of the user needs to be matched with each appeal feature in a preset appeal feature set, and then a plurality of matching results are obtained.
Step S403, comparing the feature matching result with a preset appeal dimension threshold to determine one or more appeal dimensions to which the appeal information of the user belongs.
In specific implementation, the obtained multiple matching results are respectively compared with a preset matching threshold, if the matching results exceed the threshold, the appeal information of the user is considered to contain the appeal dimension, and if the matching results do not exceed the threshold, the appeal information of the user is considered not to contain the appeal dimension.
As a preferred implementation manner of the embodiment of the present application, as shown in fig. 5, after determining one or more appeal dimensions to which the appeal information of the user belongs according to the matching result, the method includes steps S501 to S502:
step S501, determining attribute information of the user group corresponding to each appeal dimension, where the attribute information includes any one or more of a region, an occupation, a gender, and an age.
In specific implementation, after one or more appeal dimensions corresponding to each user are obtained, statistical analysis needs to be performed on attribute information of a user group corresponding to each appeal dimension, including information of regions, professions, sexes, ages and the like, so that appeal dimensions corresponding to different regions, different professions, different sexes and different ages are obtained.
Step S502, classifying and displaying the appeal dimensionality of the user group according to the attribute information of the user group.
In specific implementation, based on the obtained attribute information of the user group corresponding to each appeal dimension, the appeal dimensions of different user groups are displayed in a classified mode, and related departments and personnel are supported to perform classified retrieval according to different appeal dimensions or different user attribute information. For example, the user may search for appetitive information of a female population in the age range of 40 to 50 years in beijing, or may search for user population information in the appetitive dimension of "right" or the like.
From the above description, it can be seen that the present invention achieves the following technical effects: acquiring appeal information of a user and extracting appeal characteristic information of the user, wherein the appeal information of the user comprises any one or more of user personal information, psychological information, physiological information and user behavior information; matching the appeal characteristic information of the user with a preset appeal characteristic set; the method comprises the steps of determining one or more appeal dimensions to which the appeal information of the user belongs according to a matching result, performing cluster analysis on the user according to the appeal dimensions to obtain a user group corresponding to each appeal dimension, achieving the purpose of performing multi-dimensional statistical analysis on the civil appeal information, receiving real and dynamic civil problems and actual appeal through interaction between the user and an artificial intelligent robot, performing data management on the civil appeal, helping relevant departments to know and control the psychological stability state of social groups, the occurrence probability of group crisis events, main appeal, ideas and willingness of the people, the psychological state, emotional state and other information of the groups, performing classification retrieval on the civil appeal according to the characteristics of the groups such as different regions, occupations, sexes, ages and the like, and acquiring the civil appeal information more intuitively and quickly, thereby improving the processing efficiency of related departments and personnel.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
According to an embodiment of the present invention, there is also provided an apparatus for implementing the data processing method for civil appeal analysis, as shown in fig. 6, the apparatus includes: the device comprises an acquisition module 1, a first matching module 2, a first determination module 3 and a clustering module 4. The obtaining module 1 of the embodiment of the application is configured to obtain appeal information of a user and extract appeal feature information of the user, where the appeal information of the user includes any one or more of user personal information, psychological information, physiological information, and user behavior information; the first matching module 2 is configured to match appeal feature information of the user with a preset appeal feature set; the first determining module 3 in the embodiment of the present application is configured to determine, according to a matching result, one or more appeal dimensions to which appeal information of the user belongs; the clustering module 4 of the embodiment of the application is configured to perform clustering analysis on the users according to the appeal dimensions to obtain a user group corresponding to each appeal dimension.
As a preferred implementation manner of the embodiment of the present application, the obtaining module includes: the acquisition unit is used for acquiring human-computer interaction information, and the human-computer interaction information comprises appeal information of the user; and the extraction unit is used for carrying out semantic analysis on the human-computer interaction information so as to extract appeal characteristic information of the user.
As a preferred implementation of the embodiment of the present application, the apparatus further includes: the second matching module is used for matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result; the first comparison module is used for comparing the feature matching result with a preset appeal dimension threshold value so as to determine one or more appeal dimensions to which the appeal information of the user belongs.
As a preferred implementation manner of the embodiment of the present application, the user behavior information includes chat statement information, and the apparatus further includes: the extraction module is used for carrying out word segmentation processing and intention analysis on the chat statement information so as to extract appeal characteristic information of the user; the third matching module is used for matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result; the second comparison module is used for comparing the feature matching result with a preset appeal dimension threshold value so as to determine one or more appeal dimensions to which the appeal information of the user belongs.
As a preferred implementation of the embodiment of the present application, the apparatus further includes: the second determination module is used for determining attribute information of the user group corresponding to each appeal dimension, wherein the attribute information comprises any one or more of region, occupation, gender and age; and the display module is used for carrying out classified display on the appeal dimensionality of the user group according to the attribute information of the user group.
For the specific connection relationship between the modules and the units and the functions performed, please refer to the detailed description of the method, which is not repeated herein.
According to an embodiment of the present invention, there is also provided a computer apparatus including: one or more processors; storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as previously described.
There is also provided, in accordance with an embodiment of the present invention, a computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the steps of the method as previously described.
As shown in fig. 7, the electronic device includes one or more processors 31 and a memory 32, and one processor 31 is taken as an example in fig. 7.
The control unit may further include: an input device 33 and an output device 34.
The processor 31, the memory 32, the input device 33 and the output device 34 may be connected by a bus or other means, and fig. 7 illustrates the connection by a bus as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 32, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 31 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 32, namely, implements the data processing method for civil appeal analysis of the above method embodiment.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of a processing device operated by the server, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, which may be connected to a network connection device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 33 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the processing device of the server. The output device 34 may include a display device such as a display screen.
One or more modules are stored in the memory 32, which when executed by the one or more processors 31 perform the methods as previously described.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. Computer instructions for causing the computer to perform a data processing method for consumer appeal analysis.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, the principle and the implementation of the present invention are explained by applying the specific embodiments in the present invention, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A data processing method for consumer appeal analysis, comprising:
acquiring appeal information of a user and extracting appeal characteristic information of the user, wherein the appeal information of the user comprises any one or more of user personal information, psychological information, physiological information and user behavior information;
matching the appeal characteristic information of the user with a preset appeal characteristic set;
determining one or more appeal dimensions to which the appeal information of the user belongs according to the matching result;
and performing clustering analysis on the users according to the appeal dimensions to obtain a user group corresponding to each appeal dimension.
2. The data processing method for civil appeal analysis of claim 1, wherein the obtaining of user appeal information and extracting of user appeal feature information comprises:
acquiring human-computer interaction information, wherein the human-computer interaction information comprises appeal information of the user;
and performing semantic analysis on the human-computer interaction information to extract appeal characteristic information of the user.
3. The data processing method for civil appeal analysis of claim 1, wherein the obtaining of the appeal information of the user and the extracting of the appeal feature information of the user comprises:
matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result;
comparing the feature matching result with a preset appeal dimension threshold value to determine one or more appeal dimensions to which the appeal information of the user belongs.
4. The data processing method for civil appeal analysis of claim 1, wherein the user behavior information includes chat statement information, and the performing cluster analysis on the user according to the appeal dimensions to obtain the user group corresponding to each appeal dimension previously includes:
performing word segmentation processing and intention analysis on the chat statement information to extract appeal characteristic information of the user;
matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result;
comparing the feature matching result with a preset appeal dimension threshold value to determine one or more appeal dimensions to which the appeal information of the user belongs.
5. The data processing method for civil appeal analysis of claim 1, wherein said determining one or more appeal dimensions to which the user's appeal information belongs from the matching result comprises:
determining attribute information of a user group corresponding to each appeal dimension, wherein the attribute information comprises any one or more of region, occupation, gender and age;
and classifying and displaying the appeal dimensionality of the user group according to the attribute information of the user group.
6. A data processing apparatus for consumer appeal analysis, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring appeal information of a user and extracting appeal characteristic information of the user, and the appeal information of the user comprises any one or more of personal information, psychological information, physiological information and user behavior information of the user;
the first matching module is used for matching the appeal characteristic information of the user with a preset appeal characteristic set;
the first determination module is used for determining one or more appeal dimensions to which the appeal information of the user belongs according to the matching result;
and the clustering module is used for carrying out clustering analysis on the users according to the appeal dimensions so as to obtain a user group corresponding to each appeal dimension.
7. The data processing apparatus for civil appeal analysis of claim 6, wherein the obtaining module comprises:
the acquisition unit is used for acquiring human-computer interaction information, and the human-computer interaction information comprises appeal information of the user;
and the extraction unit is used for carrying out semantic analysis on the human-computer interaction information so as to extract appeal characteristic information of the user.
8. The data processing device for civil complaint analysis of claim 6, characterized in that it further comprises:
the second matching module is used for matching the appeal characteristic information of the user with the preset appeal characteristic set to obtain a characteristic matching result;
the first comparison module is used for comparing the feature matching result with a preset appeal dimension threshold value so as to determine one or more appeal dimensions to which the appeal information of the user belongs.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-5.
10. A non-transitory readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112418792A (en) * | 2020-11-19 | 2021-02-26 | 魏垠 | City refined information processing method, device and system |
| CN112712270A (en) * | 2020-12-31 | 2021-04-27 | 深圳前海微众银行股份有限公司 | Information processing method, device, equipment and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050197547A1 (en) * | 2001-05-15 | 2005-09-08 | Uwe Trinks | Spontaneous adverse events reporting |
| CN105740339A (en) * | 2016-01-25 | 2016-07-06 | 河北中科恒运软件科技股份有限公司 | Civil administration big data fusion and management system |
| CN107301529A (en) * | 2017-06-22 | 2017-10-27 | 四川智美高科科技有限公司 | A kind of government affairs demand accepts centring system |
| CN107977917A (en) * | 2017-12-15 | 2018-05-01 | 安徽长泰信息安全服务有限公司 | A kind of E-Government integrated application platform and method |
| CN108108352A (en) * | 2017-12-18 | 2018-06-01 | 广东广业开元科技有限公司 | A kind of enterprise's complaint risk method for early warning based on machine learning Text Mining Technology |
| CN109271481A (en) * | 2018-08-31 | 2019-01-25 | 国网河北省电力有限公司沧州供电分公司 | Method, system and terminal device for classifying electric power appeal information |
| CN109447579A (en) * | 2018-10-12 | 2019-03-08 | 成都北科维拓科技有限公司 | The dedicated method of calling of government system |
| CN110111084A (en) * | 2019-05-16 | 2019-08-09 | 上饶市中科院云计算中心大数据研究院 | A kind of government affairs service hotline analysis method and system |
| CN110222180A (en) * | 2019-06-04 | 2019-09-10 | 江南大学 | A kind of classification of text data and information mining method |
-
2020
- 2020-01-15 CN CN202010045381.0A patent/CN111259057A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050197547A1 (en) * | 2001-05-15 | 2005-09-08 | Uwe Trinks | Spontaneous adverse events reporting |
| CN105740339A (en) * | 2016-01-25 | 2016-07-06 | 河北中科恒运软件科技股份有限公司 | Civil administration big data fusion and management system |
| CN107301529A (en) * | 2017-06-22 | 2017-10-27 | 四川智美高科科技有限公司 | A kind of government affairs demand accepts centring system |
| CN107977917A (en) * | 2017-12-15 | 2018-05-01 | 安徽长泰信息安全服务有限公司 | A kind of E-Government integrated application platform and method |
| CN108108352A (en) * | 2017-12-18 | 2018-06-01 | 广东广业开元科技有限公司 | A kind of enterprise's complaint risk method for early warning based on machine learning Text Mining Technology |
| CN109271481A (en) * | 2018-08-31 | 2019-01-25 | 国网河北省电力有限公司沧州供电分公司 | Method, system and terminal device for classifying electric power appeal information |
| CN109447579A (en) * | 2018-10-12 | 2019-03-08 | 成都北科维拓科技有限公司 | The dedicated method of calling of government system |
| CN110111084A (en) * | 2019-05-16 | 2019-08-09 | 上饶市中科院云计算中心大数据研究院 | A kind of government affairs service hotline analysis method and system |
| CN110222180A (en) * | 2019-06-04 | 2019-09-10 | 江南大学 | A kind of classification of text data and information mining method |
Non-Patent Citations (1)
| Title |
|---|
| 薛彬: "针对民生热线文本的热点挖掘系统设计" * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112418792A (en) * | 2020-11-19 | 2021-02-26 | 魏垠 | City refined information processing method, device and system |
| CN112418792B (en) * | 2020-11-19 | 2022-12-20 | 魏垠 | City refinement information processing method, device and system |
| CN112712270A (en) * | 2020-12-31 | 2021-04-27 | 深圳前海微众银行股份有限公司 | Information processing method, device, equipment and storage medium |
| CN112712270B (en) * | 2020-12-31 | 2024-04-26 | 深圳前海微众银行股份有限公司 | Information processing method, device, equipment and storage medium |
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