CN113988915A - Method and device for positioning product passenger group, electronic equipment and storage medium - Google Patents
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Abstract
The application relates to the technical field of computers, and discloses a method for positioning a product customer group, which comprises the following steps: acquiring text information corresponding to a target product, and acquiring a user tag of a user corresponding to the text information; the user tags include one or more of the user's age, gender, occupation, and hobby; inputting the text information and the user label into a preset emotion classification model to obtain a target product and the user's preference degree for the target product; acquiring the correlation between the input features and the output features of the emotion classification model; determining alternative user tags according to the correlation; and determining the passenger group location according to the alternative user tags. Therefore, by analyzing the correlation between the input features and the output features of the emotion classification model, the customer base of the target product can be positioned according to the correlation performance, and the automation degree of customer base positioning is improved. The application also discloses a device for positioning the product passenger group, electronic equipment and a storage medium.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for locating a product customer group, an electronic device, and a storage medium.
Background
How to locate customers for a product is an important issue in the marketing field. At present, in order to clearly perform product definition and audience demand analysis, and to clarify the direction of product operation and effectively utilize operation resources, enterprises need to clarify the customer group location of products.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art:
in the prior art, the customer group positioning of the product is determined by manual investigation, discussion and analysis, and the automation degree is low.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
The embodiment of the disclosure provides a method and a device for positioning a product passenger group, electronic equipment and a storage medium, so that the automation degree of passenger group positioning can be improved.
In some embodiments, the method for locating product customers comprises: acquiring text information corresponding to a target product, and acquiring a user tag of a user corresponding to the text information; the user tag comprises one or more of an age, gender, occupation, and hobby of the user; inputting the text information and the user label into a preset emotion classification model to obtain the target product and the user's love degree on the target product; acquiring the correlation between the input features and the output features of the emotion classification model; determining alternative user tags according to the correlation; and determining the passenger group location according to the alternative user tag.
In some embodiments, the apparatus for locating product customers comprises: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire text information corresponding to a target product and acquire a user tag of a user corresponding to the text information; the user tag comprises one or more of an age, gender, occupation, and hobby of the user; the classification module is configured to input the text information and the user tags into a preset emotion classification model, and obtain the target product and the user's preference degree on the target product; a second obtaining module configured to obtain a correlation between input features and output features of the emotion classification model; a first determination module configured to determine an alternative user tag from the correlation; a second determination module configured to determine a guest group location from the alternative user tag.
In some embodiments, the apparatus for locating product customers comprises a processor and a memory storing program instructions, the processor being configured to, when executing the program instructions, perform the method for locating product customers as described above.
In some embodiments, the electronic device comprises the above-mentioned means for locating a product customer base.
In some embodiments, the storage medium stores program instructions that, when executed, perform the method for locating product customers described above.
The method and the device for positioning the product passenger group, the electronic device and the storage medium provided by the embodiment of the disclosure can realize the following technical effects: acquiring text information corresponding to a target product and acquiring a user tag of a user corresponding to the text information; the user tags include one or more of the user's age, gender, occupation, and hobby; inputting the text information and the user label into a preset emotion classification model to obtain a target product and the user's preference degree for the target product; acquiring the correlation between the input features and the output features of the emotion classification model; determining alternative user tags according to the correlation; and determining the passenger group location according to the alternative user tags. Therefore, the love degree of the user on the target product can be determined through the emotion classification model, and the passenger group of the target product can be positioned according to the correlation performance by analyzing the correlation between the input characteristic and the output characteristic of the emotion classification model, so that the automation degree of passenger group positioning is improved.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:
FIG. 1 is a schematic diagram of a method for locating product customers provided by an embodiment of the disclosure;
FIG. 2 is a schematic diagram of a method for obtaining an emotion classification model according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of another method for locating product customers provided by an embodiment of the disclosure;
FIG. 4 is a schematic diagram of another method for locating product customers provided by an embodiment of the disclosure;
FIG. 5 is a schematic diagram of an apparatus for locating product customers provided by an embodiment of the present disclosure;
fig. 6 is a schematic diagram of another apparatus for locating a product customer group according to an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure described herein may be made. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.
The term "plurality" means two or more unless otherwise specified.
In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
The term "correspond" may refer to an association or binding relationship, and a corresponds to B refers to an association or binding relationship between a and B.
With reference to fig. 1, an embodiment of the present disclosure provides a method for locating a product customer group, including:
step S101, acquiring text information corresponding to a target product, and acquiring a user tag of a user corresponding to the text information; the user tags include one or more of the user's age, gender, occupation, and hobbies.
And S102, inputting the text information and the user label into a preset emotion classification model to obtain the target product and the user' S favorite degree of the target product.
Step S103, acquiring the correlation between the input features and the output features of the emotion classification model.
And step S104, determining alternative user tags according to the correlation.
And step S105, determining the passenger group location according to the alternative user tags.
By adopting the method for positioning the product customer group provided by the embodiment of the disclosure, the text information corresponding to the target product is obtained, and the user label of the user corresponding to the text information is obtained; the user tags include one or more of the user's age, gender, occupation, and hobby; inputting the text information and the user label into a preset emotion classification model to obtain a target product and the user's preference degree for the target product; acquiring the correlation between the input features and the output features of the emotion classification model; determining alternative user tags according to the correlation; and determining the passenger group location according to the alternative user tags. Therefore, the love degree of the user on the target product can be determined through the emotion classification model, and the passenger group of the target product can be positioned according to the correlation performance by analyzing the correlation between the input characteristic and the output characteristic of the emotion classification model, so that the automation degree of passenger group positioning is improved.
Optionally, the target product includes a product to be positioned and/or a competitive product corresponding to the product to be positioned.
Optionally, the text information is article or comment information published on a social platform or a shopping platform by the user.
In some embodiments, the text information and the user tags are input into a preset emotion classification model, and in the case that the text information is article or comment information related to the target product, the target product and the user's liking degree of the target product are obtained.
In some embodiments, the text information and the user tag are input into a preset emotion classification model, and in the case that the text information is article or comment information irrelevant to the target product, the text information is obtained as invalid text information.
Optionally, the emotion classification model is obtained by the following means, including: acquiring sample text information and a sample user label corresponding to a sample product; acquiring a training sample label; the training sample label is used for representing the user's preference degree on the sample product; determining sample text information and a sample user label as a training sample; and inputting the training sample with the training sample label into a preset machine learning model for training to obtain an emotion classification model.
Optionally, obtaining a training sample label comprises: and carrying out natural language processing on the sample text information to obtain a training sample label.
Optionally, the performing natural language processing on the sample text information to obtain a training sample label includes: processing sample text information by using an NER (Named Entity Recognition) technology to obtain a training sample label; or processing the sample text information by an ABSA (Aspect-Based Sentiment Analysis) technology to obtain a training sample label.
Optionally, the training sample label comprises a first label and a second label.
Optionally, the first tag includes a reference to the item, a reference to the product to be located and its item, and no reference to the product to be located and its item. Optionally, the contest is a competing product of the product to be located.
Optionally, the second label comprises a positive emotion, a negative emotion and a neutral emotion; positive emotions include very likes and general likes; negative emotions include general and very unpleasant.
In some embodiments, the sample text information is "sound quality of the recording device a is very good, i.e. i like very much", and entity identification is performed on the sample text information by the NER technique to obtain the first tag, that is, the product to be located is: a recording device A; the second label is: positive emotions. Thus, the training sample label can be more accurately determined by performing natural language processing on the sample text information.
With reference to fig. 2, an embodiment of the present disclosure provides a method for obtaining an emotion classification model, including:
step S201, obtaining sample text information and a sample user label corresponding to a sample product;
step S202, acquiring a training sample label; the training sample label is used for representing the user's preference degree on the sample product; determining sample text information and a sample user label as a training sample; and inputting the training sample with the training sample label into a preset machine learning model for training to obtain an emotion classification model.
By adopting the method for acquiring the emotion classification model provided by the embodiment of the disclosure, the machine learning model is trained by utilizing the acquired sample text information and sample user labels corresponding to the sample products and the user's liking degree on the sample products through the deep learning technology, so that the emotion classification model is acquired, and the user can conveniently determine the possible emotion state, namely the liking degree, of the user on the target product through the text information, thereby improving the automation degree of positioning a target product customer group.
Optionally, obtaining a correlation between the input features and the output features of the emotion classification model includes: determining the text information and the user label as input characteristics of the model, and determining the user preference degree of the target product as output characteristics of the model; acquiring a first feature vector corresponding to the input feature and a second feature vector corresponding to the output feature; and acquiring the correlation between the first feature vector and the second feature vector.
Optionally, obtaining a first feature vector corresponding to the input feature includes: respectively acquiring a feature vector corresponding to the text information and a feature vector corresponding to the user tag; and fusing and splicing the feature vector corresponding to the text information and the feature vector corresponding to the user label to obtain a first feature vector.
Optionally, obtaining a correlation between the first feature vector and the second feature vector includes: and calculating by using the first feature vector and the second feature vector according to a preset algorithm to obtain the correlation between the first feature vector and the second feature vector.
In some embodiments, the predetermined algorithm is a corrcoef correlation coefficient function based on Matlab.
In some embodiments, whether the input features are important can be determined by an emotion classification model, including: adjusting the input characteristics of the emotion classification model by a control variable method, and inputting the adjusted input characteristics into the emotion classification model to obtain output characteristics; determining whether the adjusted input features have an influence on the output features; in the case where the adjusted input features have an effect on the output features, then the adjusted ones of the input features are determined to be significant. Therefore, the input characteristics are adjusted by controlling the variable method, the importance of the input characteristics can be analyzed more accurately, and therefore the passenger group of the target product is convenient to locate.
Optionally, determining an alternative user tag according to the correlation comprises: and determining the user label corresponding to the correlation within the first preset range as an alternative user label.
Optionally, the first preset range is 0.5-1.
In some embodiments, the greater the magnitude of the correlation, the more important the input features corresponding to the first feature vector.
Optionally, the user tag corresponding to the correlation in the second preset range is determined to be in the swing state. Optionally, the second preset range is-0.5 to 0.5.
Therefore, by determining which user tags are in the swing state and analyzing the users corresponding to the user tags in the swing state, the enterprise can conveniently improve the product to be positioned, so that key breakthrough is performed on the product to be positioned, and the product to be positioned meets the users corresponding to the user tags in the swing state.
With reference to fig. 3, an embodiment of the present disclosure provides a method for locating a product customer group, including:
step S301, acquiring text information corresponding to a target product, and acquiring a user tag of a user corresponding to the text information; the user tags include one or more of the user's age, gender, occupation, and hobbies.
Step S302, inputting the text information and the user label into a preset emotion classification model, and obtaining the target product and the user' S favorite degree of the target product.
Step S303, obtaining the correlation between the input features and the output features of the emotion classification model.
Step S304, determining the user tag corresponding to the correlation within the first preset range as an alternative user tag.
And S305, determining the passenger group location according to the alternative user tags.
By adopting the method for positioning the product customer base provided by the embodiment of the disclosure, the love degree of the user to the target product can be determined through the emotion classification model, and the user label corresponding to the correlation within the first preset range is determined as the alternative user label by analyzing the correlation between the input feature and the output feature of the emotion classification model, so that the customer base of the target product can be positioned according to the alternative user label, and the automation degree of customer base positioning is improved.
Optionally, determining the guest group location according to the alternative user tag includes: and determining all users meeting the alternative user tags as the guest group location corresponding to the target product.
As shown in fig. 4, an embodiment of the present disclosure provides a method for locating a product customer group, including:
step S401, acquiring text information corresponding to a target product, and acquiring a user tag of a user corresponding to the text information; the user tags include one or more of the user's age, gender, occupation, and hobbies.
Step S402, inputting the text information and the user label into a preset emotion classification model to obtain the target product and the user' S favorite degree of the target product.
Step S403, acquiring the correlation between the input features and the output features of the emotion classification model.
Step S404, determining the user tag corresponding to the correlation within the first preset range as an alternative user tag.
And S405, determining all users meeting the alternative user tags as the guest group positioning corresponding to the target product.
By adopting the method for positioning the product customer base provided by the embodiment of the disclosure, the love degree of the user to the target product can be determined through the emotion classification model, the user label corresponding to the correlation within the first preset range is determined as the alternative user label by analyzing the correlation between the input feature and the output feature of the emotion classification model, the user with the alternative user label is determined as the customer base of the target product, and the automation degree of customer base positioning is improved.
In some embodiments, after the target product is subjected to the customer group positioning, the profit condition corresponding to the target product in a preset time period is obtained, under the condition that the profit condition does not reach a preset expected value, the text information and the user label corresponding to the target product are obtained again, the user's liking degree of the target product is obtained according to the emotion classification model, the target product positioning is adjusted according to the relevance, repeated iteration is performed, the target product positioning is adjusted continuously, and the influence of the product is enlarged.
As shown in fig. 5, an embodiment of the present disclosure provides an apparatus for locating a product customer group, including: a first obtaining module 501, a classifying module 502, a second obtaining module 503, a first determining module 504 and a second determining module 505; the first obtaining module 501 is configured to obtain text information corresponding to a target product, and obtain a user tag of a user corresponding to the text information; the user tags include one or more of the user's age, gender, occupation, and hobby; the classification module 502 is configured to input the text information and the user tags into a preset emotion classification model, and obtain a target product and a user's preference degree for the target product; the second obtaining module 503 is configured to obtain the correlation between the input features and the output features of the emotion classification model; the first determination module 504 is configured to determine an alternative user tag from the correlation; the second determination module 505 is configured to determine the guest group location from the alternative user tags.
By adopting the device for positioning the product passenger group provided by the embodiment of the disclosure, the text information corresponding to the target product is obtained through the first obtaining module, and the user label of the user corresponding to the text information is obtained; the classification module inputs the text information and the user labels into a preset emotion classification model to obtain target products and the user preference degrees of the target products; the second acquisition module acquires the correlation between the input features and the output features of the emotion classification model; the first determining module determines alternative user tags according to the correlation; the second determining module determines the guest group location based on the alternative user tag. Therefore, the love degree of the user on the target product can be determined through the emotion classification model, and the passenger group of the target product can be positioned according to the correlation performance by analyzing the correlation between the input characteristic and the output characteristic of the emotion classification model, so that the automation degree of passenger group positioning is improved.
Optionally, the classification module is further configured to obtain an emotion classification model, and obtain sample text information and a sample user tag corresponding to the sample product; acquiring a training sample label; the training sample label is used for representing the user's preference degree on the sample product; determining sample text information and a sample user label as a training sample; and inputting the training sample with the training sample label into a preset machine learning model for training to obtain an emotion classification model.
Optionally, obtaining a training sample label comprises: and carrying out natural language processing on the sample text information to obtain a training sample label.
Optionally, the second obtaining module is configured to obtain a correlation between the input features and the output features of the emotion classification model, determine the text information and the user tag as the input features of the model, and determine the user's preference degree for the target product as the output features of the model; acquiring a first feature vector corresponding to the input feature and a second feature vector corresponding to the output feature; and acquiring the correlation between the first feature vector and the second feature vector.
Optionally, the first determining module is configured to determine the alternative user tag according to the correlation in the following manner, and determine the user tag corresponding to the correlation within the first preset range as the alternative user tag.
Optionally, the second determining module is configured to determine the guest group location according to the alternative user tag by determining all users meeting the alternative user tag as the guest group location corresponding to the target product.
In this way, the sample text information is processed through a natural language processing technology to obtain a training sample label, and a machine learning model is trained through a deep learning technology by utilizing the training sample and the training sample label to obtain an emotion classification model; the acquired text information and the user labels are determined as the input characteristics of the model and input into the emotion classification model, the possible emotion state, namely the love degree, of the user on the target product is obtained, the automatic mining of the passenger group of the product to be positioned from a large amount of network information is facilitated, and the automation degree of passenger group positioning is improved.
As shown in fig. 6, an apparatus for locating a product client includes a processor (processor)600 and a memory (memory) 601. Optionally, the apparatus may also include a Communication Interface 602 and a bus 603. The processor 600, the communication interface 602, and the memory 601 may communicate with each other via a bus 603. The communication interface 602 may be used for information transfer. The processor 600 may invoke logic instructions in the memory 601 to perform the method for locating product customers of the above-described embodiments.
By adopting the device for positioning the product passenger group provided by the embodiment of the disclosure, the text information corresponding to the target product is obtained, and the user label of the user corresponding to the text information is obtained; the user tags include one or more of the user's age, gender, occupation, and hobby; inputting the text information and the user label into a preset emotion classification model to obtain a target product and the user's preference degree for the target product; acquiring the correlation between the input features and the output features of the emotion classification model; determining alternative user tags according to the correlation; and determining the passenger group location according to the alternative user tags. Therefore, the love degree of the user on the target product can be determined through the emotion classification model, and the passenger group of the target product can be positioned according to the correlation performance by analyzing the correlation between the input characteristic and the output characteristic of the emotion classification model, so that the automation degree of passenger group positioning is improved.
In addition, the logic instructions in the memory 601 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products.
The memory 601 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 600 executes the functional applications and data processing by executing the program instructions/modules stored in the memory 601, namely, implements the method for locating product customers in the above embodiments.
The memory 601 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. In addition, the memory 601 may include a high speed random access memory, and may also include a non-volatile memory.
Optionally, an embodiment of the present disclosure provides an electronic device, which includes the above apparatus for locating a product customer base.
Optionally, the electronic device includes a server, a computer, a tablet computer, and the like.
By adopting the electronic equipment provided by the embodiment of the disclosure, the text information corresponding to the target product is obtained, and the user label of the user corresponding to the text information is obtained; the user tags include one or more of the user's age, gender, occupation, and hobby; inputting the text information and the user label into a preset emotion classification model to obtain a target product and the user's preference degree for the target product; acquiring the correlation between the input features and the output features of the emotion classification model; determining alternative user tags according to the correlation; and determining the passenger group location according to the alternative user tags. Therefore, the love degree of the user on the target product can be determined through the emotion classification model, and the passenger group of the target product can be positioned according to the correlation performance by analyzing the correlation between the input characteristic and the output characteristic of the emotion classification model, so that the automation degree of passenger group positioning is improved.
The embodiment of the disclosure provides a storage medium, which stores program instructions, and when the program instructions are executed, the method for positioning product customers is executed.
The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described method for locating product customers.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method for locating a product customer comprising:
acquiring text information corresponding to a target product, and acquiring a user tag of a user corresponding to the text information; the user tag comprises one or more of an age, gender, occupation, and hobby of the user;
inputting the text information and the user label into a preset emotion classification model to obtain the target product and the user's love degree on the target product;
acquiring the correlation between the input features and the output features of the emotion classification model;
determining alternative user tags according to the correlation;
and determining the passenger group location according to the alternative user tag.
2. The method of claim 1, wherein obtaining the emotion classification model comprises:
acquiring sample text information and a sample user label corresponding to a sample product;
acquiring a training sample label; the training sample label is used for representing the user's preference degree on the sample product; determining the sample text information and the sample user label as a training sample; and inputting the training sample with the training sample label into a preset machine learning model for training to obtain an emotion classification model.
3. The method of claim 2, wherein the obtaining a training sample label comprises:
and carrying out natural language processing on the sample text information to obtain the training sample label.
4. The method of claim 1, wherein obtaining the correlation between the input features and the output features of the emotion classification model comprises:
determining the text information and the user label as input features of the emotion classification model, and determining the user's love degree on the target product as output features of the emotion classification model;
acquiring a first feature vector corresponding to the input feature and a second feature vector corresponding to the output feature;
and acquiring the correlation between the first feature vector and the second feature vector.
5. The method of claim 1, wherein determining alternative user tags from the correlations comprises:
and determining the user label corresponding to the correlation within the first preset range as an alternative user label.
6. The method of claim 1, wherein determining a guest group location from the alternative user tag comprises:
and determining all users meeting the alternative user tags to be positioned as the guest group corresponding to the target product.
7. An apparatus for locating a product customer base, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire text information corresponding to a target product and acquire a user tag of a user corresponding to the text information; the user tag comprises one or more of an age, gender, occupation, and hobby of the user;
the classification module is configured to input the text information and the user tags into a preset emotion classification model, and obtain the target product and the user's preference degree on the target product;
a second obtaining module configured to obtain a correlation between input features and output features of the emotion classification model;
a first determination module configured to determine an alternative user tag from the correlation;
a second determination module configured to determine a guest group location from the alternative user tag.
8. An apparatus for locating a product group, comprising a processor and a memory storing program instructions, wherein the processor is configured to perform the method of any one of claims 1 to 6 when executing the program instructions.
9. An electronic device comprising the apparatus for locating product customers of claim 8.
10. A storage medium storing program instructions which, when executed, perform the method for locating product customers of any one of claims 1 to 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116010693A (en) * | 2022-12-28 | 2023-04-25 | 广州市玄武无线科技股份有限公司 | Information pushing method, device and equipment based on guest group and computer storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116010693A (en) * | 2022-12-28 | 2023-04-25 | 广州市玄武无线科技股份有限公司 | Information pushing method, device and equipment based on guest group and computer storage medium |
| CN116010693B (en) * | 2022-12-28 | 2023-11-07 | 广州市玄武无线科技股份有限公司 | Information pushing method, device and equipment based on guest group and computer storage medium |
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