A marketing value evaluation method and device based on principal component analysis algorithm

Abstract

The present invention discloses a marketing value evaluation method and device based on a principal component analysis algorithm, wherein the method comprises: obtaining first user information whose level meets preset requirements in a social platform; obtaining user portrait information based on the first user information; storing the user portrait information in a basic database; inputting the user portrait information into a first model, wherein the first model is a model established using a principal component analysis algorithm and training the model using data in the basic database as a sample; obtaining a first output result of the first model, wherein the first output result is a marketing value evaluation value of the first user; obtaining a first instruction based on the marketing value evaluation value, wherein the first instruction is used to store the marketing value evaluation value in the basic database. The method solves the technical problems in the prior art that the user marketing value evaluation dimension of a social platform is single and the evaluation result has low accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of component analysis, and in particular to a marketing value assessment method and device based on a principal component analysis algorithm. Background Art

[0002] At present, principal component analysis is widely used in many fields such as regional economic development evaluation, clothing standard formulation, satisfaction evaluation and pattern recognition. It is a commonly used multivariate analysis method. With the rapid development of social media today, using KOL to improve advertising effect has become the most practical and most commonly used marketing form for brand owners. KOL is a concept in marketing, usually defined as: a person who has more and more accurate product information, is received or informed by relevant groups, and has a greater influence on the purchasing behavior of the group. Driven by the Internet, mass entertainment has reached a new height, and the fans and eyeball effects brought by KOL are multiplied by the Internet. Almost all brands will choose KOL endorsements to increase their popularity. At the same time, the difficulty of KOL marketing has also changed. Not all KOL placements can reap the value that matches the investment. Therefore, it is difficult to judge the marketing value of social platform KOLs only from the number of fans of social platform KOLs.

[0003] In the process of implementing the technical solution of the invention in the embodiment of the present application, the inventors of the present application found that the above technology has at least the following technical problems:

[0004] The evaluation dimension of user marketing value of social platforms is single and the accuracy of the evaluation results is low. Summary of the invention

[0005] The embodiments of the present application provide a marketing value assessment method and device based on a principal component analysis algorithm, thereby solving the technical problems in the prior art of single-dimensional assessment of user marketing value on social platforms and low accuracy of assessment results, and achieving the technical purpose of accurately assessing the marketing value of users in multiple dimensions based on a principal component analysis algorithm.

[0006] The embodiment of the present application provides a marketing value assessment method based on a principal component analysis algorithm, wherein the method includes: obtaining first user information according to preset requirements, wherein the first user is a user whose level in a social platform meets the preset requirements; obtaining user portrait information according to the first user information; storing the user portrait information in a basic database; inputting the user portrait information into a first model, wherein the first model is a model established using a principal component analysis algorithm and training the model using data in the basic database as samples; obtaining a first output result of the first model, wherein the first output result is a marketing value assessment value of the first user; obtaining a first instruction according to the marketing value assessment value, wherein the first instruction is used to store the marketing value assessment value in the basic database.

[0007] On the other hand, the present application also provides a marketing value evaluation device based on the principal component analysis algorithm, wherein the device includes: a first acquisition unit, the first acquisition unit is used to obtain first user information according to preset requirements, and the first user is a user whose level in the social platform meets the preset requirements; a second acquisition unit, the second acquisition unit is used to obtain user portrait information based on the first user information; a first storage unit, the first storage unit is used to store the user portrait information in a basic database; a first input unit, the first input unit is used to input the user portrait information into a first model, and the first model is a model established using the principal component analysis algorithm by training the model with the data in the basic database as samples; a third acquisition unit, the third acquisition unit is used to obtain a first output result of the first model, and the first output result is the marketing value evaluation value of the first user; a fourth acquisition unit, the fourth acquisition unit is used to obtain a first instruction based on the marketing value evaluation value, and the first instruction is used to store the marketing value evaluation value in the basic database.

[0008] On the other hand, an embodiment of the present application also provides a marketing value assessment device based on a principal component analysis algorithm, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the program.

[0009] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0010] By building user portraits from multiple dimensions and inputting the user portraits into the principal component analysis model for training, a more accurate evaluation of the user's marketing value is obtained based on the training model's ability to continuously optimize learning and gain "experience" to process data more accurately, and the evaluation value is stored in the basic database. Through the principal component analysis method, the factors affecting the user's marketing value can be reasonably judged, and the most important influencing factors can be analyzed, thereby providing reasonable suggestions for the marketing planning of the social platform. The technical purpose of accurately evaluating the user's marketing value in multiple dimensions based on the principal component analysis algorithm has been achieved.

[0011] The above description is an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of a process of a marketing value assessment method based on a principal component analysis algorithm according to an embodiment of the present application;

[0013] Figure 2 This is a schematic diagram of the structure of a marketing value assessment device based on a principal component analysis algorithm according to an embodiment of the present application;

[0014] Figure 3 It is a schematic diagram of the structure of an exemplary electronic device according to an embodiment of the present application.

[0015] Explanation of the reference numerals: first obtaining unit 11 , second obtaining unit 12 , first storage unit 13 , first input unit 14 , third obtaining unit 15 , fourth obtaining unit 16 , bus 300 , receiver 301 , processor 302 , transmitter 303 , memory 304 , bus interface 305 . DETAILED DESCRIPTION

[0016] The embodiments of the present application provide a marketing value assessment method and device based on a principal component analysis algorithm, which solves the technical problems in the prior art of single-dimensional assessment of user marketing value on social platforms and low accuracy of assessment results, and achieves the technical purpose of accurately assessing the marketing value of users in multiple dimensions based on a principal component analysis algorithm. Below, an example embodiment of the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

[0017] Application Overview

[0018] With the rapid development of social media today, using KOLs to improve advertising effects has become the most practical and most commonly used marketing form for brand owners. Driven by the Internet, mass entertainment has reached a new height, and the fans and eyeball effects brought by KOLs have been multiplied by the Internet. Almost all brands will choose KOLs to endorse their products to increase their popularity. At the same time, the difficulty of KOL marketing has also changed. Not all KOL investments can reap the value that matches the investment. It is difficult to judge the ability of social platform KOLs to bring goods only from the number of fans of social platform KOLs. There are also technical problems in the existing technology that the user marketing value evaluation dimension of social platforms is single and the evaluation results are low in accuracy.

[0019] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0020] The embodiment of the present application provides a marketing value assessment method based on a principal component analysis algorithm, wherein the method includes: obtaining first user information according to preset requirements, wherein the first user is a user whose level in a social platform meets the preset requirements; obtaining user portrait information according to the first user information; storing the user portrait information in a basic database; inputting the user portrait information into a first model, wherein the first model is a model established using a principal component analysis algorithm and training the model using data in the basic database as samples; obtaining a first output result of the first model, wherein the first output result is a marketing value assessment value of the first user; obtaining a first instruction according to the marketing value assessment value, wherein the first instruction is used to store the marketing value assessment value in the basic database.

[0021] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0022] Embodiment 1

[0023] like Figure 1 As shown, the embodiment of the present application provides a marketing value evaluation method based on a principal component analysis algorithm, wherein the method includes:

[0024] Step S100: obtaining first user information according to preset requirements, where the first user is a user whose level on the social platform meets the preset requirements;

[0025] Specifically, in the embodiment of the present application, the information acquisition platform of the social platform acquires the information of the first user, including the number of fans, regional information, identity information, interaction data and other related data of the first user. The preset requirement is the level threshold information of the first user preset by the platform for marketing value evaluation, such as setting the number of fans threshold, interaction data threshold, etc. as the preset requirement, and then performing marketing value evaluation on users who meet this requirement. For example, the social platform is Weibo, and the KOL information acquisition platform of Weibo is used to crawl the information data of Weibo KOL, that is, Weibo big V users, from Weibo, and store the obtained KOL data information into the basic database.

[0026] Step S200: Obtaining user portrait information according to the first user information;

[0027] Specifically, the first user information obtained by the information acquisition platform of the social platform is used to establish the portrait information of the first user by the user portrait platform. The user portrait information is obtained by the user portrait platform of the social platform, and is an effective tool for outlining customers, linking customer demands and design directions, and is used to classify all user information. For example, the Weibo KOL portrait platform conducts field portrait analysis and fan portrait analysis on the acquired Weibo KOL information, thereby further analyzing the marketing value of the user. By establishing the portrait information of each user, it is possible to accurately grasp customer information, thereby laying the foundation for subsequent marketing value evaluation.

[0028] Step S300: storing the user portrait information in a basic database;

[0029] Specifically, the basic database provides the basic information and fans of the first user of the social platform required for the user marketing value assessment, and saves all data generated during the assessment. After the user portrait information is obtained by the user portrait platform of the social platform, the user portrait information is stored in the basic database for reading data from the basic database when the marketing value assessment of the first user is subsequently performed.

[0030] Step S400: inputting the user portrait information into a first model, wherein the first model is a model established by using the data in the basic database as samples for model training and a principal component analysis algorithm;

[0031] Step S500: Obtaining a first output result of the first model, where the first output result is a marketing value evaluation value of the first user;

[0032] Specifically, the principal component analysis algorithm is a commonly used mathematical statistical method, which converts a set of variables that may be correlated into a set of linearly unrelated variables through orthogonal transformation. The converted set of variables is called principal components. The user portrait information includes multiple variables, which will increase the complexity of the evaluation when conducting marketing value assessment. Therefore, the principal component analysis method is used to delete the redundant repeated variables in the user portrait and establish as few new variables as possible. After the social platform user marketing value assessment analysis platform reads the first user portrait information from the basic database, the user portrait information is input into the first model to obtain the marketing value assessment value information of the first user. Among them, the first model is a model established by using the data in the basic database as a sample for model training using the principal component analysis algorithm. The first model is a machine learning model. After the user portrait information is input into the first model, the first model will output the marketing value evaluation value identification information of the first user to verify the marketing value evaluation value of the first user output by the first model. If the marketing value evaluation value of the first user output is consistent with the marketing value evaluation value of the first user identified, the supervised learning of this data is completed, and the supervised learning of the next set of data is carried out; if the marketing value evaluation value of the first user output is inconsistent with the marketing value evaluation value of the first user identified, the first model itself is adjusted until the first model reaches the expected accuracy, and then the supervised learning of the next set of data is carried out. For example, the Weibo KOL marketing value evaluation and analysis platform is used to read the information of Weibo KOLs in various fields from the basic database, and then analyze the marketing value of each Weibo KOL in various fields through the first model. Based on the characteristics that the first model itself can continuously optimize learning and obtain "experience" to process data more accurately, the acquisition of the marketing value evaluation value of the first user is more accurate.

[0033] Step S600: Obtain a first instruction according to the marketing value evaluation value, wherein the first instruction is used to store the marketing value evaluation value in the basic database.

[0034] Specifically, after the social platform user marketing value evaluation and analysis platform obtains the marketing value evaluation value of the first user through the first model, the obtained marketing value evaluation value information of the first user is stored in the basic database, so as to facilitate the subsequent data management and analysis by the social platform.

[0035] Furthermore, before inputting the user portrait information into the first model, the embodiment S400 of the present application further includes:

[0036] Step S401: Obtain model configuration parameters;

[0037] Step S402: Obtain model training data according to the model configuration parameters and the basic database;

[0038] Step S403: obtaining first modeling information according to the model training data, wherein the first modeling information is used to perform the first model training using the model training data as a sample.

[0039] Specifically, the user marketing value evaluation system establishes the first model, uses the data in the existing basic database as samples for model training, and uses the principal component analysis algorithm for modeling. The model configuration parameters of the first model are obtained by the data management center of the social platform. According to the model configuration parameters and the basic database, model training data is obtained, and according to the model training data, first modeling information is obtained, and then the first model is obtained, and the model training data is used as a sample for the first model training. The accuracy of data acquisition of the user marketing value evaluation system is enhanced.

[0040] Furthermore, step S200 in the embodiment of the present application further includes:

[0041] Step S201: obtaining first text information according to the first user information;

[0042] Step S202: obtaining a first user domain category according to the first text information;

[0043] Step S203: storing the first user domain category in the basic database.

[0044] Specifically, the purpose of text segmentation is to segment the test text according to certain rules through mechanical segmentation methods, artificial intelligence segmentation methods, etc., so as to extract the feature values ​​of the text and provide phrases for feature value comparison for the text. After obtaining the data information of the first user, the social platform user information acquisition platform performs text segmentation based on the content published by the first user on the social platform, and obtains the first user field category through analysis. The obtained first user field categories are stored in the basic database respectively. For example, the Weibo KOL involves a field portrait unit, which is used to analyze the promotion fields involved by the Weibo KOL. It mainly performs text segmentation based on the Weibo KOL’s published Weibo, and classifies the fields involved by each Weibo KOL.

[0045] Furthermore, step S201 of the embodiment of the present application also includes:

[0046] Step S2011: obtaining first fan information according to the first user information;

[0047] Step S2012: Obtaining first fan attribute information according to the first fan information;

[0048] Step S2013: inputting the first fan attribute information into a second model, where the second model is a model trained using a large amount of fan attribute information as samples;

[0049] Step S2014: obtaining a second output result of the second model, where the second output result is fan portrait information of the first user;

[0050] Step S2015: storing the fan portrait information into the basic database, wherein the user portrait information includes the first user domain category and the first user fan portrait information.

[0051] Specifically, the user portrait information is divided into a domain portrait unit and a fan portrait unit. The fan portrait unit is obtained by inputting the first fan attribute information into the second training model, and the second model is used to analyze the first user's fans' social attributes, age level, consumption behavior and other related attribute information, and the second model is used to analyze and obtain, and the obtained first user fan portrait information is stored in the basic database. The second model is a clustering model, such as a random forest and a neural network. Cluster analysis refers to the analysis process of grouping a collection of physical or abstract objects into multiple classes composed of similar objects, the random forest refers to a classifier that uses multiple trees to train and predict samples, and the neural network model is a mathematical model or calculation model that imitates the structure and function of a biological neural network. For example, the Weibo KOL fan portrait unit is used to analyze the Weibo KOL's fans' social attributes, age level, consumption behavior and other related attribute information, and use clustering models (such as random forests and neural networks) to store the final fan portrait data in the basic database.

[0052] Furthermore, before obtaining the first execution instruction, step S203 of the embodiment of the present application further includes:

[0053] Step S2031: Obtaining a first marketing value evaluation value, a second marketing value evaluation value, and an Nth marketing value evaluation value from the basic database, wherein N is a natural number greater than 1;

[0054] Step S2032: Obtain corresponding user domain categories according to the first marketing value evaluation value, the second marketing value evaluation value, and so on until the Nth marketing value evaluation value;

[0055] Step S2033: obtaining first classification information according to the user domain category, wherein the first classification information is used to classify the first marketing value evaluation value, the second marketing value evaluation value, and the Nth marketing value evaluation value according to the user domain, and obtaining a plurality of domain marketing value evaluation value sets;

[0056] Step S2034: sorting the marketing value evaluation values ​​in the field marketing value evaluation value set to obtain a marketing value ranking list for each field;

[0057] Step S2035: According to the marketing value rankings in each field, first display information is obtained, where the first display information is used to display the marketing value rankings in each field and store them in the basic database.

[0058] Specifically, after storing the obtained user marketing value evaluation value and the user portrait information in the basic database, the marketing value evaluation values ​​of the N users are matched one by one with the user field categories, and the marketing value evaluation values ​​are classified according to the user field categories to obtain the marketing value evaluation value sets of the multiple fields. And sort the marketing value evaluation values ​​in the field marketing value evaluation value set to obtain the marketing value rankings of each field, store the rankings in the basic database, and display them to users through the social platform. For example, establishing a Weibo KOL marketing value scoring system and generating a marketing value ranking list of Weibo KOLs in various fields can help brand owners greatly increase the probability of discovering high-value Weibo KOLs. Compared with methods such as brand owners selecting only by the number of fans, the present invention provides a more efficient and accurate method to provide Weibo KOLs with a comprehensive marketing value evaluation.

[0059] Furthermore, step S202 of the embodiment of the present application also includes:

[0060] Step S2021: Obtaining first text segmentation information according to the first text information;

[0061] Step S2022: Obtaining a first text vocabulary level and a first text vocabulary frequency according to the first text segmentation information;

[0062] Step S2023: Obtain the first user domain category according to the first text vocabulary level and the first text vocabulary frequency.

[0063] Specifically, the social platform user information acquisition platform performs word segmentation processing on the first text according to certain rules through mechanical word segmentation methods, artificial intelligence word segmentation methods, etc., extracts the feature value of the first text, including the vocabulary level and vocabulary frequency information of the first text, and obtains the field category information of the first user by analyzing and counting the vocabulary level and vocabulary frequency. For example, the field portrait unit of Weibo is used to analyze the fields involved in the promotion of Weibo KOLs, mainly based on the text segmentation of the Weibo KOLs, and based on the vocabulary level and frequency, statistics are performed to classify the fields involved by each Weibo KOL.

[0064] In summary, the marketing value assessment method based on the principal component analysis algorithm provided in the embodiment of the present application has the following technical effects:

[0065] By building user portraits from multiple dimensions and inputting the user portraits into the principal component analysis model for training, a more accurate evaluation of the user's marketing value is obtained based on the training model's ability to continuously optimize learning and gain "experience" to process data more accurately, and the evaluation value is stored in the basic database. Through the principal component analysis method, the factors affecting the user's marketing value can be reasonably judged, and the most important influencing factors can be analyzed, thereby providing reasonable suggestions for the marketing planning of the social platform. The technical purpose of accurately evaluating the user's marketing value in multiple dimensions based on the principal component analysis algorithm has been achieved.

[0066] Embodiment 2

[0067] Based on the same inventive concept as the marketing value evaluation method based on the principal component analysis algorithm in the aforementioned embodiment, the present invention also provides a marketing value evaluation device based on the principal component analysis algorithm, such as Figure 2 As shown, the device comprises:

[0068] A first obtaining unit 11, the first obtaining unit 11 is used to obtain first user information according to preset requirements, the first user is a user whose level in the social platform meets the preset requirements;

[0069] A second obtaining unit 12, wherein the second obtaining unit 12 is used to obtain user portrait information according to the first user information;

[0070] A first storage unit 13, wherein the first storage unit 13 is used to store the user portrait information in a basic database;

[0071] A first input unit 14, the first input unit 14 is used to input the user portrait information into a first model, the first model is a model established by using the data in the basic database as a sample for model training and using a principal component analysis algorithm;

[0072] A third obtaining unit 15, wherein the third obtaining unit 15 is used to obtain a first output result of the first model, wherein the first output result is a marketing value evaluation value of the first user;

[0073] The fourth obtaining unit 16 is used to obtain a first instruction according to the marketing value evaluation value, wherein the first instruction is used to store the marketing value evaluation value in the basic database.

[0074] Furthermore, the device also includes:

[0075] a fifth obtaining unit, the fifth obtaining unit being used to obtain model configuration parameters;

[0076] A sixth obtaining unit, the sixth obtaining unit is used to obtain model training data according to the model configuration parameters and the basic database;

[0077] A seventh obtaining unit, wherein the seventh obtaining unit is used to obtain first modeling information according to the model training data, wherein the first modeling information is used to perform the first model training using the model training data as a sample.

[0078] Furthermore, the device also includes:

[0079] an eighth obtaining unit, the eighth obtaining unit being configured to obtain first text information according to the first user information;

[0080] a ninth obtaining unit, configured to obtain a first user domain category according to the first text information;

[0081] A second storage unit, wherein the second storage unit is used to store the first user domain category in the basic database.

[0082] Furthermore, the device also includes:

[0083] a tenth obtaining unit, the tenth obtaining unit being configured to obtain first fan information according to the first user information;

[0084] an eleventh obtaining unit, the eleventh obtaining unit being configured to obtain first fan attribute information according to the first fan information;

[0085] a second input unit, the second input unit being used to input the first fan attribute information into a second model, the second model being a model trained with a large amount of fan attribute information as samples;

[0086] a twelfth obtaining unit, the twelfth obtaining unit being used to obtain a second output result of the second model, the second output result being fan portrait information of the first user;

[0087] A third storage unit, wherein the third storage unit is used to store the fan portrait information into the basic database, wherein the user portrait information includes the first user domain category and the first user fan portrait information.

[0088] Furthermore, the device also includes:

[0089] A thirteenth obtaining unit, the thirteenth obtaining unit is used to obtain a first marketing value evaluation value, a second marketing value evaluation value, and an Nth marketing value evaluation value from the basic database, wherein N is a natural number greater than 1;

[0090] A fourteenth obtaining unit, the fourteenth obtaining unit is used to obtain corresponding user field categories according to the first marketing value evaluation value, the second marketing value evaluation value, and the Nth marketing value evaluation value respectively;

[0091] a fifteenth obtaining unit, the fifteenth obtaining unit being used to obtain first classification information according to the user domain category, the first classification information being used to classify the first marketing value evaluation value, the second marketing value evaluation value, and up to the Nth marketing value evaluation value according to the user domain, to obtain a plurality of domain marketing value evaluation value sets;

[0092] A sixteenth obtaining unit, the sixteenth obtaining unit is used to sort the marketing value evaluation values ​​in the field marketing value evaluation value set to obtain a marketing value ranking list of each field;

[0093] A seventeenth obtaining unit is used to obtain first display information according to the marketing value ranking list in each field, and the first display information is used to display the marketing value ranking list in each field and store it in the basic database.

[0094] Furthermore, the device also includes:

[0095] an eighteenth obtaining unit, the eighteenth obtaining unit being configured to obtain first text segmentation information according to the first text information;

[0096] a nineteenth obtaining unit, the nineteenth obtaining unit being configured to obtain a first text vocabulary level and a first text vocabulary frequency according to the first text segmentation information;

[0097] The twentieth obtaining unit is used to obtain the first user domain category according to the first text vocabulary level and the first text vocabulary frequency.

[0098] Furthermore, the device also includes:

[0099] A twenty-first obtaining unit, the twenty-first obtaining unit is used to obtain the second model, wherein the second model is a clustering model.

[0100] The foregoing Figure 1 The various variations and specific examples of a marketing value assessment method based on a principal component analysis algorithm in Example 1 are also applicable to a marketing value assessment device based on a principal component analysis algorithm in this embodiment. Through the above detailed description of a marketing value assessment method based on a principal component analysis algorithm, those skilled in the art can clearly know a marketing value assessment device based on a principal component analysis algorithm in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0101] Exemplary Electronic Devices

[0102] Reference below Figure 3 To describe the electronic device of the embodiment of the present application.

[0103] Figure 3 The figure shows a schematic structural diagram of an electronic device according to an embodiment of the present application.

[0104] Based on the inventive concept of a marketing value evaluation method based on a principal component analysis algorithm in the aforementioned embodiment, the present invention also provides a marketing value evaluation device based on a principal component analysis algorithm, on which a computer program is stored, and when the program is executed by a processor, the steps of any method of the marketing value evaluation method based on a principal component analysis algorithm described above are implemented.

[0105] Among them, Figure 3 In the embodiment of the present invention, a bus architecture (represented by bus 300) is shown, which may include any number of interconnected buses and bridges, and bus 300 links various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are not further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, namely a transceiver, which provides a unit for communicating with various other devices over a transmission medium.

[0106] The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 may be used to store data used by the processor 302 when performing operations.

[0107] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0111] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A marketing value assessment method based on principal component analysis algorithm, wherein: The method comprises: Acquire first user information according to preset requirements, where the first user is a user whose level on the social platform meets the preset requirements; Obtaining user portrait information according to the first user information; Storing the user portrait information in a basic database; Inputting the user portrait information into a first model, wherein the first model is a model established by using the principal component analysis algorithm and performing model training using the data in the basic database as samples; Obtaining a first output result of the first model, where the first output result is a marketing value evaluation value of the first user; According to the marketing value evaluation value, a first instruction is obtained, wherein the first instruction is used to store the marketing value evaluation value in the basic database, Wherein, obtaining user portrait information according to the first user information includes: obtaining first text information according to the first user information; obtaining a first user domain category according to the first text information; storing the first user domain category in the basic database, Wherein, obtaining user portrait information according to the first user information further includes: obtaining first fan information according to the first user information; obtaining first fan attribute information according to the first fan information; inputting the first fan attribute information into a second model, wherein the second model is a model trained with a large amount of fan attribute information as samples; obtaining a second output result of the second model, wherein the second output result is the first user fan portrait information; storing the fan portrait information in the basic database, wherein the user portrait information includes the first user domain category, the first user fan portrait information, Among them, the user portrait information is divided into a domain portrait unit and a fan portrait unit, wherein the fan portrait unit is obtained by inputting the first fan attribute information into the second model, and the second model is used to analyze the relevant attribute information of the first user's fans, and the relevant attribute information includes fan social attributes, age level and consumption behavior. The second model is used to perform analysis to obtain the first user's fan portrait information, and the obtained first user fan portrait information is stored in the basic database.

2. The method of claim 1, wherein: Before inputting the user portrait information into the first model, the method includes: Get model configuration parameters; Obtaining model training data according to the model configuration parameters and the basic database; According to the model training data, first modeling information is obtained, where the first modeling information is used to perform the first model training using the model training data as a sample.

3. The method of claim 1, wherein: The method comprises: Obtaining a first marketing value evaluation value, a second marketing value evaluation value, and an Nth marketing value evaluation value from the basic database, wherein N is a natural number greater than 1; Obtain corresponding user domain categories according to the first marketing value evaluation value, the second marketing value evaluation value, and the Nth marketing value evaluation value; According to the user domain category, first classification information is obtained, wherein the first classification information is used to classify the first marketing value evaluation value, the second marketing value evaluation value, and up to the Nth marketing value evaluation value according to the user domain, to obtain a set of marketing value evaluation values ​​in multiple domains; Sort the marketing value evaluation values ​​in the field marketing value evaluation value set to obtain a marketing value ranking list for each field; According to the marketing value rankings in each field, first display information is obtained, and the first display information is used to display the marketing value rankings in each field and store them in the basic database.

4. The method of claim 1, wherein: The obtaining, according to the first text information, a first user domain category includes: Obtaining first text segmentation information according to the first text information; Obtaining a first text vocabulary level and a first text vocabulary frequency according to the first text segmentation information; The first user domain category is obtained according to the first text vocabulary level and the first text vocabulary frequency.

5. The method of claim 1, wherein: The second model is a clustering model.

6. A marketing value assessment device based on principal component analysis algorithm, wherein: The device comprises: A first obtaining unit, the first obtaining unit is used to obtain first user information according to preset requirements, the first user being a user whose level on the social platform meets the preset requirements; a second obtaining unit, the second obtaining unit being configured to obtain user portrait information according to the first user information; A first storage unit, the first storage unit is used to store the user portrait information in a basic database; A first input unit, the first input unit is used to input the user portrait information into a first model, the first model is a model established by using the data in the basic database as samples for model training and using a principal component analysis algorithm; a third obtaining unit, the third obtaining unit being used to obtain a first output result of the first model, the first output result being a marketing value evaluation value of the first user; a fourth obtaining unit, the fourth obtaining unit being used to obtain a first instruction according to the marketing value evaluation value, the first instruction being used to store the marketing value evaluation value in the basic database, Wherein, obtaining user portrait information according to the first user information includes: obtaining first text information according to the first user information; obtaining a first user domain category according to the first text information; storing the first user domain category in the basic database, Wherein, obtaining user portrait information according to the first user information further includes: obtaining first fan information according to the first user information; obtaining first fan attribute information according to the first fan information; inputting the first fan attribute information into a second model, wherein the second model is a model trained with a large amount of fan attribute information as samples; obtaining a second output result of the second model, wherein the second output result is the fan portrait information of the first user; storing the fan portrait information in the basic database, wherein the user portrait information includes the first user domain category, the first user fan portrait information, Among them, the user portrait information is divided into a domain portrait unit and a fan portrait unit, wherein the fan portrait unit is obtained by inputting the first fan attribute information into the second model, and the second model is used to analyze the relevant attribute information of the first user fans, and the relevant attribute information includes fan social attributes, age level and consumption behavior.

7. A marketing value assessment device based on a principal component analysis algorithm, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 5 are implemented.

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