CN113157899A - Big data portrait analysis method, server and readable storage medium - Google Patents
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Abstract
The application relates to the technical field of big data and portrait analysis, in particular to a big data portrait analysis method, a server and a readable storage medium, which can determine a topic comment portrait knowledge base of a target online comment user according to first comment emotion polarity information and second comment emotion polarity information, so that the purpose of determining comment emotion polarity information of the target online comment user in each group of topic comment texts in target topic comment contents according to associated viewpoint texts obtained by a text synchronous updating thread is achieved, the technical effect of completely determining the comment emotion polarity information of the target online comment user according to the synchronously updated associated viewpoint texts is achieved, and the technical problem that the topic comment portrait knowledge base of the target online comment user is lost in a complicated topic interaction environment in the related technology is solved.
Description
Technical Field
The embodiment of the application relates to the technical field of big data and portrait analysis, in particular to a big data portrait analysis method, a server and a readable storage medium.
Background
With the development of science and technology, the application of big data is more and more extensive. Topic comment processing and sentiment polarity analysis are used as one branch of big data application and are applied more in the current traffic age.
Generally speaking, a user can participate in topic discussion on a network through an intelligent terminal and release own opinions, the opinions may carry related emotional tendencies of the user, and through analyzing the opinion comments, the portrait of the related user can be sketched, so that a basis is provided for subsequent product service upgrading of business pushing. However, there are some drawbacks to the portrait analysis technique related to topic review users.
Disclosure of Invention
In view of the above, embodiments of the present application provide a big data portrait analysis method, a server and a readable storage medium.
The embodiment of the application provides a big data portrait analysis method, which is applied to a portrait analysis server and comprises the following steps:
acquiring first comment emotion polarity information of a target online comment user in a first group of topic viewpoint texts of target topic comment contents according to a target topic interaction client;
on the premise of the target online comment user existing in the second group of topic opinion texts of the target topic comment content, determining second comment emotion polarity information of the target online comment user according to the comment emotion polarity of the synchronously updated associated opinion text with the maximum relevance;
and determining a topic comment portrait knowledge base of the target online comment user according to the first comment emotion polarity information and the second comment emotion polarity information.
Preferably, after determining the topic comment portrait knowledge base of the target online comment user according to the first comment emotion polarity information and the second comment emotion polarity information, the method includes:
on the premise that the target online comment user does not have an interactive comment record in a third group to a fourth group of topic viewpoint texts, analyzing third comment sentiment polarity information of the target online comment user in the third group of topic viewpoint texts according to the topic comment portrait knowledge base, wherein n groups are arranged between the third group and the fourth group, and n is a positive integer which is greater than 1 and smaller than a first set threshold value;
determining first analysis strategy information of the target online comment user according to the third comment emotion polarity information;
acquiring the maximum association degree of the associated viewpoint text of the first analysis strategy information according to the text synchronous updating thread;
and on the premise that the maximum relevance is larger than a second set threshold, determining that the third comment emotion polarity information is information of a topic comment portrait knowledge base of the target online comment user.
Preferably, the determining the first analysis policy information of the target online comment user according to the third comment sentiment polarity information includes:
and determining the first analysis strategy information by taking a case map transmission path of first target strategy information in a second group of topic viewpoint texts of the target online comment user as a case map transmission path of the first analysis strategy information and taking a feature weighting result of emotion key description of the first target strategy information in the second group of topic viewpoint texts of the target online comment user as emotion key description of the first analysis strategy information.
Preferably, the obtaining of the maximum relevance degree of the relevance viewpoint text of the first parsing policy information according to the text synchronous update thread includes:
acquiring the strategy description characteristics of the first analysis strategy information;
determining m local policy description features from the policy description features of the first analysis policy information, wherein feature dimensions and feature distributions of the m local policy description features are the same, a feature value of a first local policy description feature of the m local policy description features is a first feature value of the policy description feature, a feature value of a last local policy description feature of the m local policy description features is a second feature value of the policy description feature, the first feature value and the second feature value are associated, common policy information of the same feature dimension exists between every two adjacent local policy description features of the m local policy description features, and m is a positive integer greater than 1;
acquiring m associated viewpoint texts corresponding to the m local policy description features according to the text synchronous updating thread;
determining a maximum degree of association of the m associated viewpoint texts as a maximum degree of association of the associated viewpoint texts of the first resolution policy information.
Preferably, after determining the topic comment portrait knowledge base of the target online comment user according to the first comment emotion polarity information and the second comment emotion polarity information, the method includes:
on the premise that the target online comment users do not have interactive comment records in a third group to a fifth group of topic opinion texts, determining all target alternative strategy information of the target online comment users in the third group of topic opinion texts according to the target topic interactive client, wherein k groups are arranged between the third group and the fifth group, and k is a positive integer greater than a first set threshold;
optimizing the case atlas transmission path and the strategy priority of all the target alternative strategy information to be consistent with the case atlas transmission path and the strategy priority of the first target strategy information where the target online comment user is located in the second group of topic opinion texts;
extracting strategy description vectors of all optimized target candidate strategy information;
calculating the cosine distance between the strategy description vector of each target candidate strategy information and a target strategy description vector, wherein the target strategy description vector is obtained by extracting the target online comment user from the first target strategy information;
and on the premise that the maximum cosine distance is larger than a third set threshold value, determining fourth comment sentiment polarity information of a target online comment user in the target alternative strategy information corresponding to the maximum cosine distance as the information of the topic comment portrait knowledge base.
Preferably, before determining the second comment emotion polarity information of the target online comment user according to the comment emotion polarity of the synchronously updated associated viewpoint text with the maximum association degree, the method includes:
acquiring second target strategy information of the target online comment user in a first group of topic viewpoint texts according to the target topic interaction client;
obtaining emotion key description of the second target strategy information, and adjusting according to the emotion key description to obtain second analysis strategy information;
acquiring the strategy description characteristics of the second analysis strategy information;
obtaining a sample training sequence of the text synchronous updating thread by performing iterative processing on the strategy description characteristics of the second analysis strategy information;
and determining the text synchronous updating thread according to the sample training sequence and a linear regression model.
Preferably, before determining the second comment emotion polarity information of the target online comment user according to the comment emotion polarity of the synchronously updated associated viewpoint text with the maximum association degree, the method further includes:
acquiring the maximum relevance degree of the synchronously updated relevance viewpoint text;
acquiring the matching degree between the synchronously updated associated viewpoint text and the preset natural language text characteristic distribution;
determining the topic comment credibility coefficient of the target online comment user according to the maximum relevance degree and the matching degree;
on the premise that the topic comment credibility coefficient is larger than a fourth set threshold value, determining that the target online comment user exists in the second group of topic opinion texts.
Preferably, the method comprises:
acquiring first user binding information of the target online comment user in a first group of topic viewpoint texts of the target topic comment content according to the target topic interaction client;
on the premise that the target online comment user exists in a second group of topic opinion texts of the target topic comment content, acquiring a case atlas transmission path and a strategy priority of the target online comment user in the second group of topic opinion texts according to the maximum comment sentiment polarity of the case atlas transmission path and the strategy priority associated thread associated opinion texts;
determining second user binding information of the target online comment user in the second group of topic opinion texts according to the second comment emotion polarity information, the event map transmission path and the strategy priority;
continuously mining the portrait of the target online comment user according to the first user binding information and the second user binding information;
correspondingly, after outputting the second user binding information of the target online comment user in the second group of topic opinion texts according to the second comment emotion polarity information, the case atlas delivery path and the strategy priority, the method comprises:
determining a target policy description vector of the target online comment user in the second set of topic viewpoint texts according to the target policy description vector of the target online comment user in the first set of topic viewpoint texts, second user binding information data in the second set of topic viewpoint texts and a first global thread configuration parameter;
determining a text synchronous updating thread in the second group of topic opinion texts according to a text synchronous updating thread in the first group of topic opinion texts, second user binding information data in the second group of topic opinion texts and a second global thread configuration parameter;
determining a matter map transmission path and a strategy priority association thread in the second group of topic viewpoint texts according to the matter map transmission path and the strategy priority association thread in the first group of topic viewpoint texts, the second user binding information data in the second group of topic viewpoint texts and a third global thread configuration parameter;
according to the steps, the target strategy description vector, the text synchronous updating thread, the event graph transmission path and the strategy priority association thread of the target online comment user in the current group of topic viewpoint text are determined according to the target strategy description vector of the target online comment user in the previous group of topic viewpoint text, the user binding information data in the current group of topic viewpoint text and the global thread configuration parameter.
The embodiment of the application also provides an portrait analysis server, which comprises a processor, a communication bus and a memory; the processor and the memory communicate via the communication bus, and the processor reads the computer program from the memory and runs the computer program to perform the method described above.
The embodiment of the application also provides a readable storage medium for a computer, wherein the readable storage medium stores a computer program, and the computer program realizes the method when running.
Compared with the prior art, the big data portrait analysis method, the server and the readable storage medium provided by the embodiment of the application have the following technical effects: acquiring first comment emotion polarity information of a target online comment user in a first set of topic viewpoint texts of target topic comment contents by a target topic interaction client; on the premise that a target online comment user exists in a second group of topic opinion texts of the target topic comment content, determining second comment emotion polarity information of the target online comment user according to the comment emotion polarity of the synchronously updated associated opinion text with the maximum relevance; the topic comment portrait knowledge base of the target online comment user is determined according to the first comment emotion polarity information and the second comment emotion polarity information, the purpose of determining the comment emotion polarity information of the target online comment user in each group of topic comment texts in the target topic comment content according to the associated viewpoint texts obtained by the text synchronous updating thread is achieved, the technical effect of completely determining the comment emotion polarity information of the target online comment user according to the synchronously updated associated viewpoint texts is achieved, and the technical problem that the topic comment portrait knowledge base of the target online comment user is lost in a complicated topic interaction environment in the related technology is solved.
In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a block diagram of a portrait analysis server according to an embodiment of the present disclosure.
FIG. 2 is a flowchart illustrating a big data image analysis method according to an embodiment of the present disclosure.
FIG. 3 is a block diagram of a big data image analysis device according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
FIG. 1 is a block diagram of a representation analysis server 10 according to an embodiment of the present disclosure. The image analysis server 10 in the embodiment of the present application may be a server with data storage, transmission, and processing functions, as shown in fig. 1, the image analysis server 10 includes: memory 11, processor 12, communication bus 13, and big data portrait analysis device 20.
The memory 11, processor 12 and communication bus 13 are electrically connected, directly or indirectly, to enable the transfer or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 11 stores a big data image analyzing apparatus 20, the big data image analyzing apparatus 20 includes at least one software functional module that can be stored in the memory 11 in the form of software or firmware (firmware), and the processor 12 executes various functional applications and data processing by running the software programs and modules stored in the memory 11, such as the big data image analyzing apparatus 20 in the embodiment of the present application, so as to implement the big data image analyzing method in the embodiment of the present application.
The Memory 11 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 11 is used for storing a program, and the processor 12 executes the program after receiving an execution instruction.
The processor 12 may be an integrated circuit chip having data processing capabilities. The Processor 12 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The communication bus 13 is used to establish communication connection between the image analysis server 10 and other communication terminal devices via a network, and to realize transmission and reception of network signals and data. The network signal may include a wireless signal or a wired signal.
It will be appreciated that the configuration shown in FIG. 1 is merely illustrative, and that representation analysis server 10 may include more or fewer components than shown in FIG. 1, or may have a different configuration than shown in FIG. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
The embodiment of the application also provides a readable storage medium for a computer, wherein the readable storage medium stores a computer program, and the computer program realizes the method when running.
FIG. 2 illustrates a flow diagram of big data portrait analysis provided by embodiments of the present application. The method steps defined by the flow associated with the method, as applied to representation analysis server 10, may be implemented by processor 12, and include the following S21-S23.
S21, the portrait analysis server obtains first comment sentiment polarity information of a target online comment user in a first group of topic viewpoint texts of the target topic comment content according to the target topic interaction client.
For example, the portrait analysis server communicates with the target topic interaction client, and acquires corresponding target topic comment content on the premise of obtaining authorization of the target topic interaction client. The target topic comment content may be textual content. The first set of topical opinion text may be a textual opinion comment entered by a user. The target online review user may be an internet user who logs in through an account. Correspondingly, the comment emotion polarity information can include three emotion polarities of positive, negative and neutral, and can also include other types of polarities, and the scheme is not listed.
In the embodiment of the application, the first set of topic viewpoint texts can be analyzed through the related neural network model to obtain the first comment sentiment polarity information, for example, the neural network model can be trained in advance, and then the neural network model is called to analyze the first set of topic viewpoint texts, and the specific implementation mode can refer to the related prior art.
S22, the portrait analysis server determines second comment sentiment polarity information of the target online comment user according to the comment sentiment polarity of the associated opinion text with the maximum relevance updated synchronously on the premise of the target online comment user existing in the second group of topic opinion texts of the target topic comment content.
For example, the second set of topic perspective text may be generated at a time prior to the first set of topic perspective text, and the target online comment user present in the second set of topic perspective text may be understood as having an online status identification of the target online comment user present in the second set of topic perspective text.
The associated viewpoint text can be topic viewpoint text of other users to assist in determining the second comment emotion polarity information of the target online comment user, for example, the second comment emotion polarity information of the target online comment user can be determined according to semantic association between the associated viewpoint text and the second group of topic viewpoint text, so that the integrity of the second comment emotion polarity information can be ensured as much as possible.
In some alternative embodiments, before the step of determining the second comment emotion polarity information of the target online comment user according to the comment emotion polarity of the synchronously updated associated viewpoint text with the maximum association degree described in S22, the method may further include the following technical solutions: acquiring second target strategy information of the target online comment user in a first group of topic viewpoint texts according to the target topic interaction client; obtaining emotion key description of the second target strategy information, and adjusting according to the emotion key description to obtain second analysis strategy information; acquiring the strategy description characteristics of the second analysis strategy information; obtaining a sample training sequence of the text synchronous updating thread by performing iterative processing on the strategy description characteristics of the second analysis strategy information; and determining the text synchronous updating thread according to the sample training sequence and a linear regression model.
In this way, the text synchronous updating thread can be configured and optimized, so that the stability of the text synchronous updating thread in subsequent operation is ensured.
In some alternative embodiments, before determining the second comment emotion polarity information of the target online comment user according to the comment emotion polarity of the synchronously updated association viewpoint text with the maximum association degree described in S22, the method may further include the following technical solutions: acquiring the maximum relevance degree of the synchronously updated relevance viewpoint text; acquiring the matching degree between the synchronously updated associated viewpoint text and the preset natural language text characteristic distribution; determining the topic comment credibility coefficient of the target online comment user according to the maximum relevance degree and the matching degree; on the premise that the topic comment credibility coefficient is larger than a fourth set threshold value, determining that the target online comment user exists in the second group of topic opinion texts.
By the design, whether the target online comment user exists in the second group of topic opinion texts can be accurately judged based on the topic comment credibility coefficient, so that an accurate and reliable data basis is provided for subsequent knowledge base construction.
S23, the portrait analysis server determines the topic comment portrait knowledge base of the target online comment user according to the first comment emotion polarity information and the second comment emotion polarity information.
In the embodiment of the application, the topic review portrait Knowledge base can be understood as a Knowledge map of the topic review portrait, a Knowledge map (Knowledge Graph), which is called Knowledge domain visualization or Knowledge domain mapping map in the book intelligence field, and is a series of different graphs displaying the relationship between the Knowledge development process and the structure, and the Knowledge resources and the carriers thereof are described by using visualization technology, and Knowledge and the mutual relation between the Knowledge resources, the Knowledge construction Knowledge domain mapping map and the Knowledge domain mapping map are mined, analyzed, drawn and displayed.
The knowledge graph is a modern theory which achieves the aim of multi-discipline fusion by combining theories and methods of applying subjects such as mathematics, graphics, information visualization technology, information science and the like with methods such as metrology introduction analysis, co-occurrence analysis and the like and utilizing a visualized graph to vividly display the core structure, development history, frontier field and overall knowledge framework of the subjects.
The topic comment portrait knowledge base can be guaranteed to be complete in the building process through different comment emotion polarity information, and the problem that the topic comment portrait knowledge base is lost due to the fact that part of comment emotion polarities are omitted is avoided.
In some alternative embodiments, the determining the topic comment portrait knowledge base of the target online comment user according to the first comment emotion polarity information and the second comment emotion polarity information described in the above S23 may include the following S231-S236.
S231, obtaining a comment semantic emotion content set according to the first comment emotion polarity information and the second comment emotion polarity information, wherein the comment semantic emotion content set comprises x groups of related comment semantic emotion contents, and x is an integer greater than 1.
S232, obtaining an emotion content noise set according to the comment semantic emotion content set, wherein the emotion content noise set comprises x groups of related emotion content noises.
For example, emotional content noise is emotional content in which there is interference.
S233, based on the comment semantic emotion content set, obtaining a comment semantic key description set through a first description recognition unit included in a comment semantic recognition network, wherein the comment semantic key description set includes x comment semantic key descriptions.
S234, based on the emotional content noise set, acquiring a noise key description set through a second description identification unit included in the comment semantic identification network, wherein the noise key description set comprises x noise key descriptions.
S235, obtaining emotion portrait clustering results corresponding to the comment semantic emotion content set through emotion polarity clustering units included in the comment semantic recognition network based on the comment semantic emotion key description set and the noise key description set; and determining the emotion portrait splicing strategy of the comment semantic emotion content set according to the emotion portrait clustering result.
For example, the emotion figure splicing policy includes connection relationships between tag nodes, attribute configuration information of the tag nodes, and the like.
S236, performing graph data processing on the emotion portrait label of the first comment emotion polarity information and the emotion portrait label of the second comment emotion polarity information by using the emotion portrait splicing strategy to obtain the topic comment portrait knowledge base;
for example, the graph data processing may be understood as a process of nodularizing different emotion figure labels to form a topic comment figure knowledge base (knowledge graph).
It can be understood that by implementing the above S231-S236, the emotion portrait splicing policy can be obtained by taking into account the comment semantic emotion content and emotion content noise, and the emotion portrait tags of the first comment emotion polarity information and the emotion portrait tags of the second comment emotion polarity information are subjected to image data processing by the emotion portrait splicing policy to obtain the topic comment portrait knowledge base, so that the integrity of the topic comment portrait knowledge base can be ensured, and the lack of connection between partial portrait nodes or among portrait nodes can be avoided, so as to improve the quality of the topic comment portrait knowledge base.
In some optional embodiments, the obtaining, by the emotion polarity clustering unit included in the comment semantic emotion recognition network, emotion figure clustering results corresponding to the comment semantic emotion content set based on the comment semantic emotion key description set and the noise key description set, which is described in S235, may include the following S2351-S2355.
S2351, based on the comment semantic key description set, x first semantic feature maps are obtained through a first global feature recognition layer included in the comment semantic recognition network, wherein each first semantic feature map corresponds to a comment semantic key description.
S2352, based on the noise key description set, acquiring x second semantic feature maps through a second global feature recognition layer included in the comment semantic recognition network, wherein each second semantic feature map corresponds to a noise key description.
S2353, integrating the x first semantic feature maps and the x second semantic feature maps to obtain x target semantic feature maps, wherein each target semantic feature map comprises a first semantic feature map and a second semantic feature map.
S2354, acquiring a fused semantic feature map through a time domain attention unit included in the comment semantic recognition network based on the x target semantic feature maps, wherein the fused semantic feature map is determined according to the x target semantic feature maps and x time sequence weights, and each target semantic feature map corresponds to one time sequence weight.
S2355, based on the fusion semantic feature map, obtaining emotion portrait clustering results corresponding to the comment semantic emotion content set through emotion polarity clustering units included in the comment semantic identification network.
In some possible embodiments, after the step of determining the topic comment portrait knowledge base of the target online comment user from the first comment sentiment polarity information and the second comment sentiment polarity information described at S23, the method includes the following S241-S244.
S241, on the premise that the target online comment user does not have interactive comment records in a third group to a fourth group of topic viewpoint texts, analyzing third comment sentiment polarity information of the target online comment user in the third group of topic viewpoint texts according to the topic comment portrait knowledge base, wherein n groups are arranged between the third group and the fourth group, and n is a positive integer which is greater than 1 and smaller than a first set threshold value.
For example, the interactive review record may be a review record corresponding to a target online review user.
And S242, determining first analysis strategy information of the target online comment user according to the third comment emotion polarity information.
For example, the first parsing policy information is used to determine an associated point of view text.
Accordingly, in some examples, determining the first parsing strategy information of the target online comment user according to the third comment sentiment polarity information as described in S242 includes: and determining the first analysis strategy information by taking a case map transmission path of first target strategy information in a second group of topic viewpoint texts of the target online comment user as a case map transmission path of the first analysis strategy information and taking a feature weighting result of emotion key description of the first target strategy information in the second group of topic viewpoint texts of the target online comment user as emotion key description of the first analysis strategy information.
For example, a case atlas is a case logic knowledge base that describes the evolution rules and patterns between events. Structurally, the event graph is a directed cyclic graph, wherein nodes represent events, and directed edges represent event logical relations such as sequential, causal, conditional and upper and lower positions between the events. Therefore, in order to accurately and efficiently present the event content of the news event corresponding to different classification identifications to the user so as to achieve the purpose of news early warning, the event content is mapped to the node corresponding to the event map in a logical relationship mode. Further, the emotion key description may be a feature vector.
And S243, acquiring the maximum association degree of the associated viewpoint text of the first analysis strategy information according to the text synchronous updating thread.
For example, the text synchronization update thread may be pre-configured to perform comment text synchronization to avoid loss of relevant text information.
In some possible embodiments, the step of obtaining the maximum relevance of the associated viewpoint text of the first parsing strategy information according to the text synchronization update thread described in S243 may include the following S2431-S2434.
And S2431, obtaining the strategy description characteristics of the first analysis strategy information.
For example, policy description features are used to express resolution policy information from different dimensions.
S2432, determining m local strategy description characteristics in the strategy description characteristics of the first analysis strategy information.
In this embodiment of the present application, feature dimensions and feature distributions of the m local policy description features are the same, one feature value of a first local policy description feature of the m local policy description features is a first feature value of the policy description feature, one feature value of a last local policy description feature of the m local policy description features is a second feature value of the policy description feature, the first feature value and the second feature value are associated with each other, common policy information of the same feature dimension exists between every two adjacent local policy description features of the m local policy description features, and m is a positive integer greater than 1.
And S2433, acquiring m associated viewpoint texts corresponding to the m local policy description features according to the text synchronous updating thread.
And S2434, determining the maximum relevance degree in the m associated viewpoint texts as the maximum relevance degree of the associated viewpoint text of the first analysis strategy information.
With this configuration, the associated viewpoint texts can be acquired as completely as possible by the above-described S2431 to S2434, and the reliability of the maximum association degree of the associated viewpoint texts of the first analysis policy information can be ensured.
And S244, on the premise that the maximum relevance is larger than a second set threshold, determining that the third comment emotion polarity information is information of the topic comment portrait knowledge base of the target online comment user.
It can be understood that the topic comment portrait knowledge base of the target online comment user can be further optimized and perfected by determining the third comment emotion polarity information as the information of the topic comment portrait knowledge base, so that the topic comment portrait knowledge base is ensured to be in a continuous updating state.
In still other embodiments, after determining the topic comment portrait knowledge base of the target online comment user from the first comment sentiment polarity information and the second comment sentiment polarity information as described in S23, the method may further include the following S251-S255.
S251, on the premise that the target online comment users do not have interactive comment records in a third group to a fifth group of topic opinion texts, determining all target alternative strategy information of the target online comment users in the third group of topic opinion texts according to the target topic interactive client, wherein k groups are arranged between the third group and the fifth group, and k is a positive integer greater than a first set threshold;
and S252, optimizing the event atlas transmission paths and the strategy priorities of all the target candidate strategy information to be consistent with the event atlas transmission paths and the strategy priorities of the first target strategy information where the target online comment users are located in the second group of topic viewpoint texts.
And S253, extracting all optimized strategy description vectors of the target candidate strategy information.
And S254, calculating the cosine distance between the strategy description vector of each target candidate strategy information and a target strategy description vector, wherein the target strategy description vector is obtained by extracting the target online comment user from the first target strategy information.
And S255, on the premise that the maximum cosine distance is larger than a third set threshold value, determining fourth comment sentiment polarity information of the target online comment user in the target alternative strategy information corresponding to the maximum cosine distance as the information of the topic comment portrait knowledge base.
It can be understood that the topic comment portrait knowledge base of the target online comment user can be further optimized and perfected by determining the fourth comment emotion polarity information as the information of the topic comment portrait knowledge base, so that the topic comment portrait knowledge base is ensured to be in a continuous updating state.
In some related embodiments, the method may further include the following S31-S34.
S31, acquiring first user binding information of the target online comment user in a first group of topic viewpoint texts of the target topic comment content according to the target topic interaction client.
For example, the user binding information is used to detect a target online review user.
S32, on the premise that the target online comment user exists in the second group of topic opinion texts of the target topic comment contents, acquiring a matter map transmission path and a strategy priority of the target online comment user in the second group of topic opinion texts according to the maximum comment sentiment polarity of the matter map transmission path and the strategy priority association thread association opinion texts.
For example, comment sentiment polarity may be quantitatively expressed, such as negative polarity of 0 and positive polarity of 1.
S33, determining second user binding information of the target online comment user in the second group of topic opinion texts according to the second comment emotion polarity information, the event map transmission path and the strategy priority.
In some possible examples, after outputting the second user binding information of the target online comment user in the second set of topic opinion texts according to the second comment emotion polarity information, the case map delivery path and the policy priority, which are described in S33, the method may further include the following steps a1-a 4.
Step a1, determining a target policy description vector of the target online comment user in the second set of topic viewpoint texts according to the target policy description vector of the target online comment user in the first set of topic viewpoint texts, the second user binding information data in the second set of topic viewpoint texts and the first global thread configuration parameter.
Step a2, determining a text synchronization update thread in the second set of topic perspective texts according to a text synchronization update thread in the first set of topic perspective texts, second user binding information data in the second set of topic perspective texts and a second global thread configuration parameter.
Step a3, determining the event map transmission path and the strategy priority associated thread in the second group of topic viewpoint texts according to the event map transmission path and the strategy priority associated thread in the first group of topic viewpoint texts, the second user binding information data in the second group of topic viewpoint texts and the third global thread configuration parameter.
Step a4, sequentially according to the steps a1-a3, determining a target strategy description vector, a text synchronous updating thread, a case graph transmission path and a strategy priority association thread of the target online comment user in the current group of topic view texts according to the target strategy description vector of the target online comment user in the previous group of topic view texts, user binding information data in the current group of topic view texts and a global thread configuration parameter.
S34, continuously mining the portrait of the target online comment user through the first user binding information and the second user binding information.
In the embodiment of the application, the continuous portrait mining can be used for carrying out real-time and uninterrupted portrait analysis on related comment texts or user operations of target online comment users, so that as much material as possible is provided for updating of a knowledge base.
Based on the same inventive concept, there is also provided a big data portrait analysis device 20 applied to a portrait analysis server 10, the device including:
the first obtaining module 21 is configured to obtain, according to the target topic interaction client, first comment sentiment polarity information of a target online comment user in a first group of topic opinion texts of the target topic comment content;
a second obtaining module 22, configured to, on the premise that the target online comment user exists in the second group of topic opinion texts of the target topic comment content, determine second comment emotion polarity information of the target online comment user according to the comment emotion polarity of the synchronously updated associated opinion text with the maximum relevance;
and the knowledge determining module 23 is configured to determine a topic comment portrait knowledge base of the target online comment user according to the first comment emotion polarity information and the second comment emotion polarity information.
For the description of the above functional modules, refer to the description of the method shown in fig. 2.
In conclusion, when the scheme is applied, the first comment sentiment polarity information of the target online comment user in the first group of topic viewpoint texts of the target topic comment content can be acquired according to the target topic interaction client; on the premise that a target online comment user exists in a second group of topic opinion texts of the target topic comment content, determining second comment emotion polarity information of the target online comment user according to the comment emotion polarity of the synchronously updated associated opinion text with the maximum relevance; the topic comment portrait knowledge base of the target online comment user is determined according to the first comment emotion polarity information and the second comment emotion polarity information, the purpose of determining the comment emotion polarity information of the target online comment user in each group of topic comment texts in the target topic comment content according to the associated viewpoint texts obtained by the text synchronous updating thread is achieved, the technical effect of completely determining the comment emotion polarity information of the target online comment user according to the synchronously updated associated viewpoint texts is achieved, and the technical problem that the topic comment portrait knowledge base of the target online comment user is lost in a complicated topic interaction environment in the related technology is solved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. 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). It should also be noted that, 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. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a representation analysis server 10, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: 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. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A big data image analysis method is applied to an image analysis server, and the method comprises the following steps:
acquiring first comment emotion polarity information of a target online comment user in a first group of topic viewpoint texts of target topic comment contents according to a target topic interaction client;
on the premise of the target online comment user existing in the second group of topic opinion texts of the target topic comment content, determining second comment emotion polarity information of the target online comment user according to the comment emotion polarity of the synchronously updated associated opinion text with the maximum relevance;
and determining a topic comment portrait knowledge base of the target online comment user according to the first comment emotion polarity information and the second comment emotion polarity information.
2. The method of claim 1, wherein after determining the topic comment portrait knowledge base of the target online comment user from the first comment sentiment polarity information and the second comment sentiment polarity information, the method comprises:
on the premise that the target online comment user does not have an interactive comment record in a third group to a fourth group of topic viewpoint texts, analyzing third comment sentiment polarity information of the target online comment user in the third group of topic viewpoint texts according to the topic comment portrait knowledge base, wherein n groups are arranged between the third group and the fourth group, and n is a positive integer which is greater than 1 and smaller than a first set threshold value;
determining first analysis strategy information of the target online comment user according to the third comment emotion polarity information;
acquiring the maximum association degree of the associated viewpoint text of the first analysis strategy information according to the text synchronous updating thread;
and on the premise that the maximum relevance is larger than a second set threshold, determining that the third comment emotion polarity information is information of a topic comment portrait knowledge base of the target online comment user.
3. The method of claim 2, wherein the determining the first resolution strategy information of the target online comment user according to the third comment sentiment polarity information comprises:
and determining the first analysis strategy information by taking a case map transmission path of first target strategy information in a second group of topic viewpoint texts of the target online comment user as a case map transmission path of the first analysis strategy information and taking a feature weighting result of emotion key description of the first target strategy information in the second group of topic viewpoint texts of the target online comment user as emotion key description of the first analysis strategy information.
4. The method according to claim 2, wherein the obtaining of the maximum relevance of the associated viewpoint text of the first parsing policy information according to the text synchronization update thread comprises:
acquiring the strategy description characteristics of the first analysis strategy information;
determining m local policy description features from the policy description features of the first analysis policy information, wherein feature dimensions and feature distributions of the m local policy description features are the same, a feature value of a first local policy description feature of the m local policy description features is a first feature value of the policy description feature, a feature value of a last local policy description feature of the m local policy description features is a second feature value of the policy description feature, the first feature value and the second feature value are associated, common policy information of the same feature dimension exists between every two adjacent local policy description features of the m local policy description features, and m is a positive integer greater than 1;
acquiring m associated viewpoint texts corresponding to the m local policy description features according to the text synchronous updating thread;
determining a maximum degree of association of the m associated viewpoint texts as a maximum degree of association of the associated viewpoint texts of the first resolution policy information.
5. The method of claim 1, wherein after determining the topic comment portrait knowledge base of the target online comment user from the first comment sentiment polarity information and the second comment sentiment polarity information, the method comprises:
on the premise that the target online comment users do not have interactive comment records in a third group to a fifth group of topic opinion texts, determining all target alternative strategy information of the target online comment users in the third group of topic opinion texts according to the target topic interactive client, wherein k groups are arranged between the third group and the fifth group, and k is a positive integer greater than a first set threshold;
optimizing the case atlas transmission path and the strategy priority of all the target alternative strategy information to be consistent with the case atlas transmission path and the strategy priority of the first target strategy information where the target online comment user is located in the second group of topic opinion texts;
extracting strategy description vectors of all optimized target candidate strategy information;
calculating the cosine distance between the strategy description vector of each target candidate strategy information and a target strategy description vector, wherein the target strategy description vector is obtained by extracting the target online comment user from the first target strategy information;
and on the premise that the maximum cosine distance is larger than a third set threshold value, determining fourth comment sentiment polarity information of a target online comment user in the target alternative strategy information corresponding to the maximum cosine distance as the information of the topic comment portrait knowledge base.
6. The method of claim 1, wherein before determining the second comment sentiment polarity information of the target online comment user according to the comment sentiment polarity of the synchronously updated associated viewpoint text with the largest relevance, the method comprises:
acquiring second target strategy information of the target online comment user in a first group of topic viewpoint texts according to the target topic interaction client;
obtaining emotion key description of the second target strategy information, and adjusting according to the emotion key description to obtain second analysis strategy information;
acquiring the strategy description characteristics of the second analysis strategy information;
obtaining a sample training sequence of the text synchronous updating thread by performing iterative processing on the strategy description characteristics of the second analysis strategy information;
and determining the text synchronous updating thread according to the sample training sequence and a linear regression model.
7. The method of claim 1, wherein before determining the second comment sentiment polarity information of the target online comment user according to the comment sentiment polarity of the synchronously updated associated viewpoint text with the largest relevance, the method further comprises:
acquiring the maximum relevance degree of the synchronously updated relevance viewpoint text;
acquiring the matching degree between the synchronously updated associated viewpoint text and the preset natural language text characteristic distribution;
determining the topic comment credibility coefficient of the target online comment user according to the maximum relevance degree and the matching degree;
on the premise that the topic comment credibility coefficient is larger than a fourth set threshold value, determining that the target online comment user exists in the second group of topic opinion texts.
8. The method according to claim 1, characterized in that it comprises:
acquiring first user binding information of the target online comment user in a first group of topic viewpoint texts of the target topic comment content according to the target topic interaction client;
on the premise that the target online comment user exists in a second group of topic opinion texts of the target topic comment content, acquiring a case atlas transmission path and a strategy priority of the target online comment user in the second group of topic opinion texts according to the maximum comment sentiment polarity of the case atlas transmission path and the strategy priority associated thread associated opinion texts;
determining second user binding information of the target online comment user in the second group of topic opinion texts according to the second comment emotion polarity information, the event map transmission path and the strategy priority;
continuously mining the portrait of the target online comment user according to the first user binding information and the second user binding information;
correspondingly, after outputting the second user binding information of the target online comment user in the second group of topic opinion texts according to the second comment emotion polarity information, the case atlas delivery path and the strategy priority, the method comprises:
determining a target policy description vector of the target online comment user in the second set of topic viewpoint texts according to the target policy description vector of the target online comment user in the first set of topic viewpoint texts, second user binding information data in the second set of topic viewpoint texts and a first global thread configuration parameter;
determining a text synchronous updating thread in the second group of topic opinion texts according to a text synchronous updating thread in the first group of topic opinion texts, second user binding information data in the second group of topic opinion texts and a second global thread configuration parameter;
determining a matter map transmission path and a strategy priority association thread in the second group of topic viewpoint texts according to the matter map transmission path and the strategy priority association thread in the first group of topic viewpoint texts, the second user binding information data in the second group of topic viewpoint texts and a third global thread configuration parameter;
according to the steps, the target strategy description vector, the text synchronous updating thread, the event graph transmission path and the strategy priority association thread of the target online comment user in the current group of topic viewpoint text are determined according to the target strategy description vector of the target online comment user in the previous group of topic viewpoint text, the user binding information data in the current group of topic viewpoint text and the global thread configuration parameter.
9. An image analysis server, comprising a processor, a communication bus and a memory; the processor and the memory communicate via the communication bus, the processor reading a computer program from the memory and operating to perform the method of any of claims 1-8.
10. A computer-readable storage medium, characterized in that the readable storage medium stores a computer program which, when executed, implements the method of any one of claims 1-8.
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