WO2018205459A1

WO2018205459A1 - Target user acquisition method and apparatus, electronic device and medium

Info

Publication number: WO2018205459A1
Application number: PCT/CN2017/099701
Authority: WO
Inventors: 王健宗; 黄章成; 吴天博; 肖京
Original assignee: 平安科技（深圳）有限公司
Priority date: 2017-05-10
Filing date: 2017-08-30
Publication date: 2018-11-15
Also published as: CN107665442B; CN107665442A

Abstract

A target user acquisition method and apparatus, an electronic device and a medium, applied to the technical field of information processing. The target user acquisition method comprises: acquiring category list information via a social platform, and generating target feature information according to the category list information (S101); acquiring public information published by a social account of a user, and according to the target feature information and the public information, determining public information related to the target feature information (S102); according to the determined public information related to the target feature information, determining whether the user is a target user (S103). The present solution locates potential target users in a rapid and accurate manner, and thereby increases product sales.

Description

Method, device, electronic device and medium for acquiring target user

Technical field

The present application belongs to the field of information processing technologies, and in particular, to a method, an apparatus, an electronic device, and a medium for acquiring a target user.

Background technique

Before the business promotes the business, it is first necessary to determine the target customer. The traditional acquisition of the target customer is completed through questionnaires and the like, and it is difficult to obtain accurate user feedback information in this way. With the development of Internet technology, there is a way to analyze user's concerns through user behavior in the network, but the analysis of user behavior is relatively fixed, and only the specific identifiers in the user information can be analyzed, so it has certain limitations. Analysis is based on a small amount of user data that conforms to the data format.

technical problem

In view of this, the embodiments of the present invention provide a method, an apparatus, an electronic device, and a medium for acquiring a target user, so as to solve the problem that only a specific identifier in the user information can be analyzed in the prior art, and thus has certain limitations, and The problem of analysis based on a small amount of user data that conforms to the data format.

Technical solution

A first aspect of the embodiments of the present invention provides a method for acquiring a target user, including:

Obtaining classification list information through a social platform, and generating target feature information according to the classification list information;

Obtaining public information published by the user's social account, and determining public information related to the target feature information according to the target feature information and each piece of the public information;

Determining whether the user is a target user according to the determined pieces of public information related to the target feature information.

A second aspect of the embodiments of the present invention provides an apparatus for acquiring a target user, including:

a feature generation module, configured to acquire classification list information by using a social platform, and generate target feature information according to the classification list information;

An information obtaining module, configured to obtain public information published by a user's social account;

a determining module, configured to determine, according to the target feature information and each piece of the public information, public information related to the target feature information;

And a processing module, configured to determine, according to each piece of public information related to the target feature information determined by the determining module, whether the user is a target user.

A third aspect of the embodiments of the present invention provides a target user electronic device, including a memory, a processor, and a computer program executable on the processor, where the processor executes the The computer sequence implements the following steps:

According to a fourth aspect of the embodiments of the present invention, a computer readable storage medium storing a computer program, the computer program being executed by at least one processor, implements the following steps:

Beneficial effect

In the embodiment of the present invention, the classification list information is obtained through the social platform, and the target feature information is generated according to the classification list information; then the public information published by the user's social account is obtained, and the public information is obtained according to the target feature information and each piece of the public information. Determining the public information related to the target feature information; determining, according to the determined pieces of public information related to the target feature information, whether the user is a target user, and the method for acquiring the target user can pass the social platform Obtaining various classification list information to generate a plurality of target feature information, and then determining whether the user is a target user corresponding to each target feature information according to the public information published by the user, thereby being able to quickly and accurately locate the potential target user, thereby improving the product. Sales.

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments or the description of the prior art will be briefly described below. It is obvious that the drawings in the following description are only the present invention. For some embodiments, other drawings may be obtained from those of ordinary skill in the art in light of the inventive workability.

1 is a flowchart of a method for acquiring a target user according to an embodiment of the present invention;

FIG. 2 is a flowchart of an implementation of step S101 in FIG. 1;

FIG. 3 is a flowchart of an implementation of step S102 in FIG. 1;

4 is a specific flowchart of a method for acquiring a target user according to an embodiment of the present invention;

FIG. 5 is a flowchart of an implementation of step S404 in FIG. 4;

6 is a schematic diagram of an operating environment for acquiring a target user program according to an embodiment of the present invention;

FIG. 7 is a block diagram of a program for acquiring a target user program according to an embodiment of the present invention.

Embodiments of the invention

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

In order to explain the technical solution described in the present invention, the following description will be made by way of specific embodiments.

Embodiment 1

FIG. 1 is a flowchart showing an implementation process of a method for acquiring a target user according to an embodiment of the present invention, which is described in detail as follows:

Step S101: Acquire classification list information through a social platform, and generate target feature information according to the classification list information.

The "entity dictionary" can be constructed by using the classified list information acquired in the social platform, and then the constructed "entity dictionary" is subjected to certain expansion and the like to generate target feature information. For example, the crawler software can be used to crawl the classified list information in the social platform. The social platform can be a website platform, such as a public comment, but not limited to this.

The target feature information is used to determine a target user among users, for example, target feature information includes, but is not limited to, fields such as finance, sports, and entertainment.

Specifically, referring to FIG. 2, in an embodiment, generating the target feature information according to the classification list information in step S101 may be implemented by using the following process:

Step S201, extracting words and phrases in the classification list information.

Specifically, the classification list may include multiple aspects of information, such as hotels, travel, catering, etc. The following is a description of the hotel, but is not limited thereto. For example, if the classification list information is the Hilton Wangfujing Hotel, the words extracted from the classified list information may be "Wangfujing Hilton Hotel".

Step S202, expanding the extracted words according to the vector space model to generate the target feature information.

Understandably, according to the common social account usage habits of the public, the published public information including the “Wangfujing Hilton Hotel” does not have much public information, which results in a low recall rate, so the word vector space can be utilized. The model calculates the distance (similarity) between the words and the words to expand the extracted words to generate the target feature information.

Specifically, the word distance can be extended by using the distance of the path, for example, based on the shortest path between the concepts connected to each other in the superordinate word hierarchy. The synonym set will return the maximum value compared to itself.

Optionally, after the extracted word segment is expanded according to the vector space model in step S202, the method further includes: Step S203: expanding the expanded word sentence by the text depth representation model to generate the target feature information.

Specifically, the word vector has good semantic characteristics and is a common way of expressing word features. Therefore, Word2Vector (text depth representation model) can be used to generate more related words to expand the entity dictionary to produce target feature information. For example, the Hilton Wangfujing Hotel can be expanded to Hilton, Wangfujing, Four Seasons, Hilton and more. That is, the purpose The characteristic information can be Hilton, Wangfujing, Four Seasons Hotel, Hilton, etc.

Among them, the vector space model of words relies on the combination of words with similar semantics to improve the performance of natural language processing. For example, the training set might have sentence 1 "dog is walking" and sentence 2 "cat is walking". Because the probability distribution of the context of dogs and cats is very similar, in a dog's sentence, it is very likely that a dog will be replaced by a cat and a dog will be replaced with a word that is not similar to the dog's context probability distribution. May get an illegal sentence.

Step S102: Acquire public information published by the social account of the user, and determine public information related to the target feature information according to the target feature information and each piece of the public information.

The social account includes, but is not limited to, a Weibo account and an instant messaging platform account. The public information published by the user's social account may be public information related to the hobby, life, work, etc. posted by the user, and can represent various aspects of the user's concern.

The target feature information is used to determine a target user among users, such as target feature information including, but not limited to, finance, sports, entertainment, and the like. Specifically, if the target feature information is financial, and the public information published by the user's social account includes financial information, the user may be the target account.

Referring to FIG. 3, in an embodiment, step S102 can be implemented by the following process:

Step S301, segmenting the text content of each piece of the public information to form a plurality of phrases.

The text content of the public information published by the user can reflect the user's interest to a certain extent, and thus can be used to extract the topic of interest to the user. In this step, the text content of the public information is segmented, so that the influence of the ambiguous words on the dictionary can be smoothed.

Step S302, classifying each phrase of each piece of the public information according to the target feature information.

In this step, according to the target feature information, each phrase in each of the public information texts may be classified by using an intelligent quick troubleshooting method. Here, only one text W is taken as an example. After classification, n words W(l)={w ₁ , w ₂ ,...,w _n } are obtained, and W(l) is searched by the troubleshooting method to determine whether the text is Related to the label l, in this loop, the texts of all the public information of all users are classified, and all the public information sets U={T _l1 , T _l2 , . . . , T _ln } of each user U for the label set L are obtained. Where l∈L. The tag 1 represents characteristic information corresponding to the public information published by the user, such as finance, sports, entertainment, and the like.

Step S303, determining public information related to the target feature information according to the classification result.

In this step, the public information related to the target feature information may be determined according to the classification result in step S302. Specifically, if the target feature information includes Hilton, Wangfujing, Four Seasons Hotel, and Hilton, and the public information posted by the user's social account includes at least one of Hilton, Wangfujing, Four Seasons, and Hilton, the user May be the target user.

In addition, by extracting the first classification feature information of each piece of the public information, determining, according to the first classification feature information and the target feature information of each piece of the public information, each piece of the public information and the Whether the target feature information is relevant. The first classification feature information includes a keyword and/or an identifier.

It can be understood that the public information published by the user through the social account may include the classification feature information of the user's hobbies, life, work, etc., so the first category including the keyword and/or identifier may be extracted from the public information published by the user. Feature information to classify each piece of public information. The keywords include, but are not limited to, words related to the user's hobbies, life, work, etc., and the identifiers include, but are not limited to, identifiers such as pictures, expressions, and the like related to the user's hobbies, life, work, and the like.

The target feature information may include at least one keyword and at least one identifier. Specifically, after the first classification feature information is extracted, the first classification feature information may be matched with the target feature information. If the matching degree of the first classification feature information and the target feature information is greater than the first threshold, determining the public information and The target feature information is related. Otherwise, it is determined that the public information is not related to the target feature information.

For example, when the first classification feature information is a keyword, the first classification feature information may be matched with each keyword in the target feature information, and if the matching is successful, the public information is determined to be related to the target feature information; otherwise, the determination is performed. The public information is not related to the target feature information.

For example, when the first classification feature information is an identifier, the first classification feature information may be matched with the identifier in the target feature information, and if the matching degree is greater than the first threshold, determining that the public information is related to the target feature information, Otherwise, it is determined that the public information is not related to the target feature information.

For another example, when the first classification feature information includes both the keyword and the identifier, the keyword or the identifier may be prioritized, and the first classification feature information and the target feature information are matched according to the priority.

Step S103: Determine, according to the determined pieces of public information related to the target feature information, whether the user is a target user.

Wherein, the determined degree of relevance of each piece of public information related to the target feature information and the target feature information may be determined whether the user is a target user. Specifically, the correlation degree of each piece of public information and the target feature information related to the target feature information may be averaged, and then the user is determined to be the target user according to the size relationship between the average value and the second threshold.

Referring to FIG. 4, a specific flowchart of the method for acquiring a target user is shown, and the repeated description is not repeated.

Step S401: Acquire classification list information through a social platform, and generate target feature information according to the classification list information.

Step S402: Acquire public information published by the social account of the user, where the public information includes the information content and the publishing time, and determine the public information related to the target feature information according to the target feature information and each piece of the public information.

Step S403: Obtain target account information of the user's social account, the target account information includes classification information of the target account and ranking information of the target account, and determine the location according to the target feature information and each of the target account information. Each piece of target account information related to the target feature information.

The target account information that the user's social account pays attention to may be account information related to the user's hobbies, life, work, and the like, and can represent various aspects of the user's concern. It can be understood that if the target feature information is financial, and the classified information of the target account in the target account information of the user's social account is included in the financial account, the user may be the target account.

Step S404: Determine, according to the determined pieces of public information related to the target feature information and each piece of target account information, whether the user is a target user.

The size of the confidence value of each piece of public information and target feature information related to the target feature information, and the confidence value of each piece of target account information and target feature information may be comprehensively considered to determine the user. Whether it is a target user.

Referring to FIG. 5, step S404 in one embodiment may be implemented by the following process:

Step S501: Establish a confidence value model of the user according to the determined public information and target account information related to the target feature information.

Specifically, taking the single target feature information 1 and the user u _i as an example, the number of public information of all the public information of the user u _i belongs to the target feature information 1 is TN(l), and the target account of the user u _i is comprehensively considered. The number of accounts belonging to the target feature information 1 is GN(l), and the confidence value model of the user u _i for the target feature information 1 is:

S(u _i ,l)=α*∑TN(l)+(1-α)*∑GN(l)

Where α∈[0,1]. The weights of the two views from the public information published by the user u _i and the target account information of interest can be adjusted to reflect different priorities. For example, the value of α is 0.5, and the balance considers the role of the public information published by the user u _i and the target account information of interest. Repeatedly, the confidence value of each target feature information of all users can be obtained.

Step S502, determining whether the user is a target user according to the confidence value model of the user.

Specifically, through the above process, all users can be tagged with very fine features and the confidence values of these tags can be obtained. Through these user portraits, financial business personnel can help financial professionals to provide personalized services to each customer quickly and accurately, and promote financial products suitable for each customer, thereby greatly improving the efficiency of the enterprise and thus helping the enterprise. Business analysis and development strategy.

The method for obtaining a target user is to first obtain the category list information through the social platform, and generate target feature information according to the category list information; and then obtain the public information posted by the user's social account, and according to the target feature information and each article Declaring public information to determine public information related to the target feature information; Each piece of public information related to the feature information determines whether the user is a target user, and the method for acquiring the target user is capable of acquiring various classification list information through the social platform to generate a plurality of target feature information, and then publishing according to the user The information and the target account information of interest determine whether the user is a target user corresponding to multiple target feature information, and thus can quickly and accurately locate potential target users, thereby increasing product sales.

It should be understood that the size of the sequence of the steps in the above embodiments does not imply a sequence of executions, and the order of execution of the processes should be determined by its function and internal logic, and should not be construed as limiting the implementation of the embodiments of the present invention.

Corresponding to the method for obtaining a target user according to the foregoing embodiment, FIG. 6 is a schematic diagram of an operating environment for acquiring a target user program according to an embodiment of the present invention. For the convenience of explanation, only the parts related to the present embodiment are shown.

In the embodiment, the acquisition target user program 600 is installed and runs in the electronic device 60. The electronic device 60 can be a mobile terminal, a palmtop computer, a server, or the like. The electronic device 60 can include, but is not limited to, a memory 603, a processor 601, and a display 602. Figure 6 shows only electronic device 60 having components 601-603, but it should be understood that not all illustrated components may be implemented and that more or fewer components may be implemented instead.

The memory 603 may be an internal storage unit of the electronic device 60, such as a hard disk or memory of the electronic device 60, in some embodiments. The memory 603 may also be an external storage device of the electronic device 60 in other embodiments, such as a plug-in hard disk equipped on the electronic device 60, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc. Further, the memory 603 may also include both an internal storage unit of the electronic device 60 and an external storage device. The memory 603 is configured to store application software and various types of data installed in the electronic device 60, such as the program code of the acquisition target user program 600, and the like. The memory 603 can also be used to temporarily store data that has been output or is about to be output.

The processor 601, in some embodiments, may be a Central Processing Unit (CPU), a microprocessor or other data processing chip for running program code or processing data stored in the memory 603, such as The acquisition target user program 600 or the like is executed.

The display 602 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like in some embodiments. The display 602 is used to display information processed in the electronic device 60 and a user interface for displaying visualizations, such as an application menu interface, an application icon interface, and the like. The components 601-603 of the electronic device 60 communicate with one another via a system bus.

FIG. 7 is a block diagram of a program for acquiring a target user program 600 according to an embodiment of the present invention. In this embodiment, the acquisition target user program 600 may be divided into one or more modules, the one or more modules being stored in the memory 603 and being processed by one or more processors (this Embodiments are performed by the processor 601) to complete the present invention. For example, in FIG. 7, the acquisition target user program 600 can be divided into a generation module 701, an information acquisition module 702, an information acquisition module 703, and a processing module 704. The module referred to in the present invention refers to being able to complete a specific A series of computer program instruction segments of functionality are more suitable than programs to describe the execution of the acquisition target user program 600 in the electronic device 60. The following description will specifically describe the functions of the modules 701-704.

The feature generation module 701 is configured to acquire the classification list information through the social platform, and generate target feature information according to the classification list information.

The information obtaining module 702 is configured to obtain public information published by the user's social account.

The determining module 703 is configured to determine public information related to the target feature information according to the target feature information and each piece of the public information.

The processing module 704 is configured to determine, according to the pieces of public information related to the target feature information determined by the determining module, whether the user is a target user.

Optionally, the feature generation module 701 can be divided into an obtaining unit 801, an extracting unit 802, and an expanding unit 803. The obtaining unit 801 is configured to obtain the category list information through the social platform. The extracting unit 802 is configured to extract words and phrases in the classified list information. The expansion unit 803 is configured to expand the extracted words according to the vector space model to generate the target feature information.

Optionally, after expanding the extracted words according to the vector space model, the expansion unit 803 is further configured to expand the extended words by the text depth representation model to generate the target feature information.

Optionally, the determining module 703 can be divided into a word segmentation unit 901, a classification unit 902, and a determination unit 903. The word segmentation unit 901 is configured to segment the text content of each piece of the public information to form a plurality of phrases. The classification unit 902 is configured to classify each phrase of each piece of the public information according to the target feature information. The determining unit 903 is configured to determine, according to the classification result of the classification unit, the public information related to the target feature information.

Optionally, the information obtaining module 702 is further configured to acquire target account information that is related to the social account of the user. The determining module 703 is further configured to determine, according to the target feature information and each of the target account information, pieces of target account information related to the target feature information. The processing module 704 is specifically configured to: determine, according to the determined pieces of public information related to the target feature information, and each piece of target account information, whether the user is a target user.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the division of each functional unit and module described above is exemplified. In practical applications, the above functions may be assigned to different functional units as needed. The module is completed by dividing the internal structure of the device into different functional units or modules to perform all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the integrated unit may be hardware. Formal implementation can also be implemented in the form of software functional units. In addition, the specific names of the respective functional units and modules are only for the purpose of facilitating mutual differentiation, and are not intended to limit the scope of protection of the present application. For the specific working process of the unit and the module in the foregoing system, reference may be made to the corresponding process in the foregoing method embodiment, and details are not described herein again.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the solution. A person skilled in the art can use different methods for implementing the described functions for each particular application, but such implementation should not be considered to be beyond the scope of the present invention.

In the embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the system embodiment described above is merely illustrative. For example, the division of the module or unit is only a logical function division. In actual implementation, there may be another division manner, for example, multiple units or components may be used. Combinations can be integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in electrical, mechanical or other form.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the embodiments of the present invention may contribute to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage. The medium includes a plurality of instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the methods described in various embodiments of the embodiments of the present invention. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

The embodiments described above are only for explaining the technical solutions of the present invention, and are not intended to be limiting; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that The technical solutions described in the examples are modified, or some of the technical features are equivalently replaced; and the modifications or substitutions do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in Within the scope of protection of the present invention.

Claims

A method for obtaining a target user, comprising:

Obtaining classification list information through a social platform, and generating target feature information according to the classification list information;

Obtaining public information published by the user's social account, and determining public information related to the target feature information according to the target feature information and each piece of the public information;

Determining whether the user is a target user according to the determined pieces of public information related to the target feature information.
The method for acquiring a target user according to claim 1, wherein the generating the target feature information according to the classification list information is specifically:

Extracting words and phrases in the category list information;

The extracted words and phrases are expanded according to a vector space model to generate the target feature information.
The method for acquiring a target user according to claim 2, further comprising: after expanding the extracted words and phrases according to the vector space model, further comprising:

The expanded words are expanded again by the text depth representation model to generate the target feature information.
The method for obtaining a target user according to claim 1, wherein the determining the public information related to the target feature information according to the target feature information and each piece of the public information comprises:

Segmenting the text content of each piece of the public information to form a plurality of phrases;

Sorting each phrase of each piece of public information according to the target feature information;

The public information related to the target feature information is determined based on the classification result.
The method for obtaining a target user according to claim 1, further comprising:

Obtaining target account information of the user's social account, and determining each piece of target account information related to the target feature information according to the target feature information and each of the target account information;

Determining, according to the determined pieces of public information related to the target feature information, whether the user is a target user is:

Determining whether the user is a target user according to the determined pieces of public information and each piece of target account information related to the target feature information.
A device for acquiring a target user, comprising:

a feature generation module, configured to acquire classification list information by using a social platform, and generate target feature information according to the classification list information;

An information obtaining module, configured to obtain public information published by a user's social account;

a determining module, configured to determine, according to the target feature information and each piece of the public information, public information related to the target feature information;

And a processing module, configured to determine, according to each piece of public information related to the target feature information determined by the determining module, whether the user is a target user.
The device for acquiring a target user according to claim 6, wherein the feature generating module comprises:

An obtaining unit, configured to obtain classification list information through a social platform;

An extracting unit, configured to extract words and phrases in the classified list information;

And an expansion unit, configured to expand the extracted words according to the vector space model to generate the target feature information.
The apparatus for acquiring a target user according to claim 7, wherein the extension unit further expands the expanded words by using a text depth representation model after expanding the extracted words according to the vector space model. And generating the target feature information.
The device for acquiring a target user according to claim 6, wherein the determining module comprises:

a word segmentation unit, configured to segment the text content of each piece of the public information to form a plurality of phrases;

a classifying unit, configured to classify each phrase of each piece of the public information according to the target feature information;

a determining unit, configured to determine public information related to the target feature information according to the classification result of the classification unit.
The device for acquiring a target user according to claim 6, wherein the information acquiring module is further configured to acquire target account information that the social account of the user pays attention to;

The determining module is further configured to determine, according to the target feature information and each of the target account information, each piece of target account information related to the target feature information;

The processing module is specifically configured to: determine, according to the determined pieces of public information related to the target feature information, and each piece of target account information, whether the user is a target user.
An acquisition target user electronic device, comprising: a memory, a processor, wherein the memory stores a computer program executable on the processor, and the processor executes the computer sequence to implement the following steps:

Obtaining classification list information through a social platform, and generating target feature information according to the classification list information;

Obtaining public information published by the user's social account, and determining public information related to the target feature information according to the target feature information and each piece of the public information;

Determining whether the user is a target user according to the determined pieces of public information related to the target feature information.
The acquiring target user electronic device according to claim 11, wherein the generating the target feature information according to the classification list information is specifically:

Extracting words and phrases in the category list information;

The extracted words and phrases are expanded according to a vector space model to generate the target feature information.
The acquisition target user electronic device according to claim 12, wherein in the basis vector space After the model expands the extracted words, it also includes:

The expanded words are expanded again by the text depth representation model to generate the target feature information.
The acquiring target user electronic device according to claim 11, wherein the determining the public information related to the target feature information according to the target feature information and each piece of the public information comprises:

Segmenting the text content of each piece of the public information to form a plurality of phrases;

Sorting each phrase of each piece of public information according to the target feature information;

The public information related to the target feature information is determined based on the classification result.
The acquiring target user electronic device according to claim 11, wherein when the processor executes the computer program, the following steps are further implemented:

Obtaining target account information of the user's social account, and determining each piece of target account information related to the target feature information according to the target feature information and each of the target account information;

Determining, according to the determined pieces of public information related to the target feature information, whether the user is a target user is:

Determining whether the user is a target user according to the determined pieces of public information and each piece of target account information related to the target feature information.
A computer readable storage medium storing a computer program, wherein the computer program, when executed by at least one processor, implements the following steps:

Obtaining classification list information through a social platform, and generating target feature information according to the classification list information;

Obtaining public information published by the user's social account, and determining public information related to the target feature information according to the target feature information and each piece of the public information;

Determining whether the user is a target user according to the determined pieces of public information related to the target feature information.
The computer readable storage medium according to claim 16, wherein the generating the target feature information according to the classification list information is specifically:

Extracting words and phrases in the category list information;

The extracted words and phrases are expanded according to a vector space model to generate the target feature information.
The computer readable storage medium according to claim 17, wherein after the expanding the extracted words and phrases according to the vector space model, the method further comprises:

The expanded words are expanded again by the text depth representation model to generate the target feature information.
The computer readable storage medium according to claim 16, wherein the determining the public information related to the target feature information according to the target feature information and each piece of the public information comprises:

Segmenting the text content of each piece of the public information to form a plurality of phrases;

Sorting each phrase of each piece of public information according to the target feature information;

The public information related to the target feature information is determined based on the classification result.
The computer readable storage medium of claim 16, wherein when the computer program is executed by at least one processor, the following steps are further implemented:

Obtaining target account information of the user's social account, and determining each piece of target account information related to the target feature information according to the target feature information and each of the target account information;

Determining, according to the determined pieces of public information related to the target feature information, whether the user is a target user is:

Determining whether the user is a target user according to the determined pieces of public information and each piece of target account information related to the target feature information.