CN116521962A

CN116521962A - Public opinion data mining method, system, electronic equipment and storage medium

Info

Publication number: CN116521962A
Application number: CN202310516114.0A
Authority: CN
Inventors: 李勇; 王勇; 顾沛峰
Original assignee: Guyuan Shanghai Culture Technology Co ltd
Current assignee: Guyuan Shanghai Culture Technology Co ltd
Priority date: 2023-05-09
Filing date: 2023-05-09
Publication date: 2023-08-01

Abstract

The disclosure relates to a public opinion data mining method, a public opinion data mining system, electronic equipment and a storage medium, and relates to the technical field of public opinion data mining. Wherein the method comprises the following steps: public opinion data are obtained, and attitude analysis is carried out on a plurality of users corresponding to the public opinion data respectively; and determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis. The embodiment of the disclosure can realize the determination of the perception sensitivity of a plurality of users corresponding to the public opinion data under the same cognition or different cognitions.

Description

Public opinion data mining method, system, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of public opinion data mining, in particular to a public opinion data mining method, a public opinion data mining system, electronic equipment and a storage medium.

Background

In the internet era, public opinion data is a general term for a collection of opinion information of the public. Through analysis of public opinion data users, the opinion of public opinion of the vast Internet citizens can be known. Especially commercially, consumer satisfaction with the merchant or merchant product is known.

Only people with susceptibility are wild people, only people with rationality are brute force people, and truly free and perfect people are people with rationality and susceptibility perfectly combined and harmonious. Big data, communities, value-viewing marketing have become a major factor in the 4.0 era of marketing revolution. For example, the back of the fire explosion of the eastern screening fire explosion event transmits the value views of the hierarchy, the naturalness, the nostalgia and the like, and is related to the current experience of most audience, thereby promoting the brand recognition, the sales and purchase behavior of the eastern screening fire explosion event. Based on the above, how to deeply mine the public opinion data, and further determine the perception sensitivity corresponding to a plurality of users of the public opinion data under the same cognition or different cognitions, so as to enable the brand power to increase and lead to new consumption cognition is a technical problem to be solved in the present day.

Disclosure of Invention

The disclosure provides a public opinion data mining method, a public opinion data mining system, electronic equipment and a storage medium technical scheme.

According to an aspect of the present disclosure, there is provided a public opinion data mining method, including:

public opinion data are obtained, and attitude analysis is carried out on a plurality of users corresponding to the public opinion data respectively;

and determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis.

Preferably, the method for performing attitude analysis on the plurality of users corresponding to the public opinion data respectively includes:

acquiring emotion expression key information;

and carrying out attitude analysis on a plurality of users corresponding to the public opinion data based on the emotion expression keywords and/or emotion expression packages or images in the emotion expression keyword information.

Preferably, the method for determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis comprises the following steps:

respectively determining cognition corresponding to the plurality of users;

classifying the plurality of users according to the cognition corresponding to the plurality of users;

and respectively determining the perception sensitivity corresponding to the classified users based on the classified users.

Preferably, before the determining the perceived sensitivities corresponding to the users under the same cognition or different cognitions, determining a cognitive method corresponding to the plurality of users includes:

determining a situation and/or a plot corresponding to the public opinion data;

determining a plurality of main stream value views corresponding to the plurality of users based on the situation and/or the plot corresponding to the public opinion data;

Configuring a plurality of main stream value views with highest ranking in sequence from the plurality of main stream value views as cognition corresponding to the plurality of users; and/or the number of the groups of groups,

the method for acquiring public opinion data comprises the following steps:

acquiring at least one public opinion keyword corresponding to public opinion data to be mined;

mining the public opinion data to be mined according to the public opinion keywords to obtain public opinion data; and/or the number of the groups of groups,

before carrying out attitude analysis on the plurality of users corresponding to the public opinion data respectively, the method for determining the plurality of users corresponding to the public opinion data comprises the following steps:

obtaining social network topology corresponding to public opinion data, and generating a public opinion propagation search tree based on the social network topology;

determining a public opinion initial user corresponding to a public opinion propagation search tree source node of the public opinion data propagation by using the public opinion propagation search tree;

determining a plurality of users corresponding to the public opinion data based on the social network topology corresponding to the public opinion initial user; and/or the number of the groups of groups,

the perception sensitivity is configured as one or more of emotion sensitivity, emotion sensitivity and desire sensitivity; and/or the number of the groups of groups,

when the types of the plurality of users are configured as mother or the context corresponding to the public opinion data and/or the plot is configured as feeding alone, the cognition or the plurality of mainstream value views are configured as one or more of sense of security, female care, age stress, job site stress, social observation, marital observation, traditional aesthetic, anti-traditional aesthetic, superdescense; and/or the number of the groups of groups,

Before the public opinion data is obtained and attitude analysis is carried out on a plurality of users corresponding to the public opinion data, the method for determining the public opinion analysis vector corresponding to the public opinion data comprises the following steps:

converting the public opinion data into text data and converting the text data into a word list;

converting each word in the word list into a word vector by using a preset word vector model;

respectively converting the word vectors into public opinion analysis text vectors;

and analyzing attitudes of a plurality of users corresponding to the public opinion data based on the public opinion analysis text vector.

Preferably, the method for generating the public opinion propagation search tree based on the social network topology comprises the following steps:

generating a public opinion propagation topological graph based on the social network topology;

constructing a public opinion propagation search tree based on the public opinion propagation topological graph; and/or the number of the groups of groups,

the method for determining the public opinion initial user corresponding to the public opinion propagation search tree source node of the public opinion data propagation by utilizing the public opinion propagation search tree comprises the following steps:

calculating a propagation center value and a priori estimated value corresponding to the public opinion propagation search tree node by using the public opinion propagation search tree;

Tracing the public opinion data according to the propagation center value and the prior estimated value, and determining a public opinion initial user corresponding to a public opinion propagation search tree source node of the public opinion data propagation; and/or the number of the groups of groups,

the emotion sensitivity is configured as one or more of a group consisting of a hydrophobic, a anger, a probability, an anxiety, a disappointing, a sadness, a calm, a happiness, an agitation and an excitation; and/or the number of the groups of groups,

the emotional sensitivity is configured to be one or more of worry, pain, confusion, surprise, happiness, low fall, anxiety, battle, counseling; and/or the number of the groups of groups,

the libido sensitivity is configured as one or more of health, safety, nutrition, strength, emaciation, weakness, disease; and/or the number of the groups of groups,

before the public opinion data is converted into text data, the method further comprises the following steps: if the public opinion data is video data, converting a voice signal corresponding to the video data into text data; and/or the number of the groups of groups,

the method for converting the voice signal corresponding to the video data into text data comprises the following steps:

determining a number of speakers of the speech signal;

if the number of the speakers is greater than 1, respectively determining the identities corresponding to the speakers; determining the speaking start time and the speaking end time of each identity respectively; the audio signals corresponding to the speaking start time and the speaking end time of each identity are converted into text data;

Otherwise, directly converting the voice signal into text data; and/or the number of the groups of groups,

if the identity corresponding to the speaker is configured as a questioning identity and a response identity;

processing the text data corresponding to the questioning identity and the response identity to obtain text data conforming to a preset rule; and/or the number of the groups of groups,

the method for processing the text data corresponding to the questioning identity and the response identity to obtain the text data conforming to the preset rule comprises the following steps:

respectively extracting first text data of the questioning identity and first text features and second text features corresponding to second text data of the answering identity;

calculating the similarity of the first text feature and the second text feature;

based on the similarity and the preset similarity, processing the text data corresponding to the questioning identity and the response identity to obtain text data conforming to a preset rule; and/or the number of the groups of groups,

the method for processing the text data corresponding to the questioning identity and the response identity based on the similarity and the preset similarity to obtain the text data conforming to the preset rule comprises the following steps:

if the similarity is greater than or equal to the preset similarity, configuring the corresponding first text data and the second text data into text data conforming to a preset rule; and/or the number of the groups of groups,

The method for carrying out attitude analysis on a plurality of users corresponding to the public opinion data based on the public opinion analysis text vector comprises the following steps:

based on a preset multi-classification model, carrying out attitude classification on a plurality of users corresponding to the public opinion data by utilizing the public opinion analysis text vector to obtain a plurality of analysis results corresponding to the attitude analysis; and/or the number of the groups of groups,

the method for carrying out attitude analysis on a plurality of users corresponding to the public opinion data based on the public opinion analysis text vector further comprises the following steps:

respectively acquiring a plurality of public opinion analysis text vectors corresponding to the same user on a plurality of public opinion platforms or the public opinion data of the same public opinion platform;

respectively splicing and fusing the plurality of public opinion analysis text vectors;

based on a preset multi-classification model, carrying out attitude classification on each user by utilizing the fusion public opinion analysis text vector after the fusion, so as to obtain a plurality of analysis results corresponding to the attitude analysis of each user.

According to an aspect of the present disclosure, there is provided a public opinion data mining method applied to a target user, including: the method described above; the method comprises the steps of,

and recommending the target user based on the perception sensitivity corresponding to the plurality of users of the public opinion data under the same cognition or different cognitions.

According to an aspect of the present disclosure, there is provided a public opinion data mining method applied to a speaker, including: the method described above; the method comprises the steps of,

determining target users based on the perception sensitivity corresponding to a plurality of users of the public opinion data under the same cognition or different cognitions; and recommending the speaker corresponding to the public opinion data based on the target user.

According to an aspect of the present disclosure, there is provided a public opinion data mining system including:

the analysis unit is used for acquiring public opinion data and respectively carrying out attitude analysis on a plurality of users corresponding to the public opinion data;

the determining unit is used for determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis; and/or a first recommending unit, configured to recommend a target user based on perception sensitivities corresponding to a plurality of users of the public opinion data under the same cognition or different cognitions; and/or a second recommending unit, configured to recommend a speaker corresponding to the public opinion data based on the target user.

According to an aspect of the present disclosure, there is provided an electronic apparatus including:

a processor;

A memory for storing processor-executable instructions;

wherein the processor is configured to: the above public opinion data mining method is performed.

According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described public opinion data mining method.

In the embodiment of the disclosure, the public opinion data mining method, the system, the electronic equipment and the storage medium are provided to solve the problem that the perception sensitivity of the corresponding user cannot be determined based on the public opinion data at present, so that brand power growth cannot be enabled fundamentally to lead new consumption cognition.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the technical aspects of the disclosure.

FIG. 1 illustrates a flow chart of a public opinion data mining method according to an embodiment of the present disclosure;

FIG. 2 illustrates a perceived sensitivity map corresponding to the user under the same or different cognition in accordance with an embodiment of the present disclosure;

FIG. 3 illustrates a plurality of mainstream value graphs corresponding to different public opinion data users according to an embodiment of the present disclosure;

FIG. 4 illustrates a content distribution diagram for emotion, liberty, context, and plot correspondence in accordance with an embodiment of the present disclosure;

FIG. 5 illustrates a perceived sensitivity map corresponding to the user under the same or different cognition in accordance with another embodiment of the present disclosure;

FIG. 6 shows a block diagram of a public opinion data mining system according to an embodiment of the present disclosure;

FIG. 7 is a block diagram of an electronic device 800, shown in accordance with an exemplary embodiment;

fig. 8 is a block diagram illustrating an electronic device 1900 according to an example embodiment.

Detailed Description

Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, numerous specific details are set forth in the following detailed description in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order not to obscure the present disclosure.

It will be appreciated that the above-mentioned method embodiments of the present disclosure may be combined with each other to form a combined embodiment without departing from the principle logic, and are limited to the description of the present disclosure.

In addition, the disclosure further provides a public opinion data mining system, an electronic device, a computer readable storage medium and a program, which can be used for implementing any public opinion data mining method provided by the disclosure, and corresponding technical schemes and descriptions and corresponding descriptions of method parts are omitted.

Fig. 1 shows a flowchart of a public opinion data mining method according to an embodiment of the present disclosure, as shown in fig. 1, including: step S101: public opinion data are obtained, and attitude analysis is carried out on a plurality of users corresponding to the public opinion data respectively; step S102: and determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis. And further determining the perception sensitivity corresponding to a plurality of users of the public opinion data under the same cognition or different cognitions so as to enable brand power to increase and lead to new consumption cognition.

Step S101: and obtaining public opinion data, and respectively carrying out attitude analysis on a plurality of users corresponding to the public opinion data.

In an embodiment of the present disclosure, the method for performing attitude analysis on a plurality of users corresponding to the public opinion data includes: acquiring emotion expression key information; and carrying out attitude analysis on a plurality of users corresponding to the public opinion data based on the emotion expression keywords and/or emotion expression packages or images in the emotion expression keyword information.

In the embodiments of the present disclosure and other possible embodiments, those skilled in the art may configure the emotion expression key words in the emotion expression key information according to actual needs. For example, the emotional expression keywords may be configured as corresponding similar words of smile, dislike, pyrolusite, dementia, single-eye vomiting of tongue, photophobia, and/or smile; or the emotion expression package or image is configured into a corresponding expression package or image such as smile, happiness, pyrolusite, idiom, monocular tongue spit, photophobia and/or smile.

In an embodiment of the present disclosure, the method for obtaining public opinion data includes: acquiring at least one public opinion keyword corresponding to public opinion data to be mined; and mining the public opinion data to be mined according to the public opinion keywords to obtain the public opinion data.

In the embodiments of the present disclosure and other possible embodiments, the public opinion keywords may be configured by those skilled in the art according to actual needs. For example, the name of the person or time corresponding to the public opinion or other possible public opinion keywords.

In the embodiments and other possible embodiments of the present disclosure, mining at least 1 set public opinion platforms according to the public opinion keywords to obtain public opinion data. Wherein, the public opinion platform can be configured according to actual needs by a person skilled in the art. For example, the public opinion platform may be configured as one or more of official microblogs, weChat public numbers, portals, government networks, weChat, microblogs, forums, bar-attached and news media, etc., head numbers, hundred family numbers, weChat public numbers, etc., self-media numbers, tremble sounds, fast hands, knowledge, etc.

In an embodiment of the disclosure, before the determining the perceived sensitivities corresponding to the users under the same cognition or different cognitions, determining a cognitive method corresponding to the plurality of users includes: determining a situation and/or a plot corresponding to the public opinion data; determining a plurality of main stream value views corresponding to the plurality of users based on the situation and/or the plot corresponding to the public opinion data; and configuring a plurality of main stream value views with highest ranking in turn from the plurality of main stream value views as cognition corresponding to the plurality of users. For example, the plurality of mainstream value views may be configured to rank up to 3 mainstream values in turn.

In the embodiments of the present disclosure and other possible embodiments, those skilled in the art may configure the context and/or scenario corresponding to the public opinion data according to actual needs. For example, the scenario may be configured to feel one or more of itself is needed, marital is suspected to be authentic, living is disfiguring (spells in life, chicken feather in home), looking for oneself; the emotion mirror can be one or more of knowledge learning, independent feeding, waking, incapacitation, movement, insomnia, sigh (bellying by feeling), psychological treatment, work or learning all the time (rotating by connecting shafts), inexperience, staying up night, drinking water, vernix child-care contradiction, breathing regulation, massage and body regulation.

Fig. 2 illustrates a perceived sensitivity map corresponding to the user under the same or different cognition according to an embodiment of the present disclosure. As shown in fig. 2, for different public opinion data, the corresponding perceived sensitivities of the users under the same or different cognitions are given. For example, public opinion data is configured such that Xie Ankun in "rural love" sells grandchildren, and cognition (underlying awareness) shows a hydrophobic effect on perceived sensitivity corresponding to finance. Where n/a represents null (no public opinion data). Under the same underlying knowledge, consumers (a plurality of users corresponding to the public opinion data) can have great variability in the expression of different events (the public opinion data). By disassembling the underlying awareness, and locating in FIG. 2, the brand can intuitively see the high/low perceived sensitivity of the consumer (the plurality of users to whom the public opinion data corresponds). The synchronization may be used as a content/session/scene extension library for new media operations.

Fig. 3 illustrates a plurality of mainstream value perspective views corresponding to different public opinion data users according to an embodiment of the present disclosure. As shown in fig. 3, a plurality of mainstream value views corresponding to public opinion data users in the intelligent electronic industry, the cosmetic industry and the video beverage industry are respectively given. And the data corresponding to the plurality of main stream value views with the highest rank in sequence are respectively 12.

FIG. 4 illustrates a content distribution diagram for emotion, liberty, context and plot correspondence according to an embodiment of the present disclosure. As shown in fig. 4, in an embodiment of the present disclosure, the perceived sensitivity is configured as one or more of emotional sensitivity, and emotional sensitivity. Wherein, the liquid crystal display device comprises a liquid crystal display device, the emotion sensitivity is configured as one or more of a group consisting of a hydrophobic, a anger, a probability, an anxiety, a disappointing, a sadness, a calm, a happiness, an agitation and an excitation; and/or the emotional sensitivity is configured to be one or more of worry, pain, confusion, surprise, happiness, low, restlessness, battle, counseling, passive; and/or the libido sensitivity is configured as one or more of health, safety, nutrition, strength, emaciation, weakness, illness. Wherein the scenario may be configured to feel one or more of self-desire, suspected marital origin, dislived (spells in life, chicken feather in home), seek oneself; the emotion mirror can be one or more of knowledge learning, independent feeding, waking, incapacitation, movement, insomnia, sigh (bellying by feeling), psychological treatment, work or learning all the time (rotating by connecting shafts), inexperience, staying up night, drinking water, vernix child-care contradiction, breathing regulation, massage and body regulation.

In embodiments of the present disclosure and other possible embodiments, the emotional sensitivity is configured as one or more of disgust, anger, anxiety, disappointment, sadness, calm, pleasure, happiness, agitation, and excitement; wherein the emotional sensitivity corresponding to the aversion, the angry, the anxiety, the disappointing, the sadness, the calm, the pleasure, the happiness, the excitement and the contrasty is sequentially increased or sequentially decreased.

In embodiments of the present disclosure, and other possible embodiments, those skilled in the art can configure the emotion, desire, context, and scenario as desired. For example, the emotional sensitivity may also be configured to be one or more of honor, overrun, co-emotion, self-luxury, non-boring, solitary, dropoff, frustration, lost, etc.; the emotion sensitivity can also be configured as one or more of happiness, anger, sadness, convulsion and the like; the libido sensitivity can be configured as one or more of survival, work assurance, friendship, confidence and the like; the plot can also be configured into one or more of ordinary family and child knowledge with goods and adverse attack, teacher living broadcast and flame explosion, adverse attack of some middle-aged, recall rural areas in living broadcast rooms, parents and relatives and friends of parents, overrising after stock price is stopped, and the like; the scenario may also be configured as one or more of a forced anechoic direct broadcast room, listening to a certain storytelling tear break, being in the same experience stamp, ordering for a once-used plot, etc.

In an embodiment of the disclosure, before the converting the public opinion data into text data, the method further includes: and if the public opinion data is video data, converting a voice signal corresponding to the video data into text data.

In an embodiment of the present disclosure, the method for converting a voice signal corresponding to the video data into text data includes: determining a number of speakers of the speech signal; if the number of the speakers is greater than 1, respectively determining the identities corresponding to the speakers; determining the speaking start time and the speaking end time of each identity respectively; the audio signals corresponding to the speaking start time and the speaking end time of each identity are converted into text data; otherwise, the voice signal is directly converted into text data.

In embodiments of the present disclosure and other possible embodiments, the method for determining the number of speakers of the speech signal includes: identifying a speaker of the speech signal using a speaker identification technique; and determining the number of speakers of the speech signal based on the speaker.

In an embodiment of the present disclosure and other possible embodiments, the method for determining the speaking start time and the speaking end time of each identity respectively includes: and respectively determining the speaking start time and the speaking end time of each identity by using a voice endpoint detection technology.

In an embodiment of the present disclosure and other possible embodiments, the method for converting an audio signal corresponding to the speaking start time and the speaking end time of each of the identities into text data includes: and converting the audio signals corresponding to the speaking start time and the speaking end time of each identity into text data by utilizing a voice recognition technology.

In the embodiment of the disclosure, the identities corresponding to the speaker are configured as a questioning identity and a response identity; and processing the text data corresponding to the questioning identity and the response identity to obtain text data conforming to a preset rule.

In an embodiment of the present disclosure, the method for processing text data corresponding to the question identity and the answer identity to obtain text data conforming to a preset rule includes: respectively extracting first text data of the questioning identity and first text features and second text features corresponding to second text data of the answering identity; calculating the similarity of the first text feature and the second text feature; and processing the text data corresponding to the questioning identity and the response identity based on the similarity and the preset similarity to obtain the text data conforming to the preset rule.

In an embodiment of the present disclosure, the method for processing text data corresponding to the question identity and the response identity based on the similarity and the preset similarity to obtain text data conforming to a preset rule includes: and if the similarity is greater than or equal to the preset similarity, configuring the corresponding first text data and the second text data into text data conforming to a preset rule. The person skilled in the art can configure the numerical value corresponding to the preset similarity according to actual needs, and the similarity can be configured as cosine similarity.

In an embodiment of the present disclosure and other possible embodiments, the method for extracting a first text feature and a second text feature corresponding to the first text data of the challenge identity and the second text data of the response identity respectively includes: based on a preset word vector model, using the first text data and the second text data to obtain a first keyword feature word vector and a second keyword feature word vector corresponding to the first text data and the second text data; respectively calculating the sum of the first word vectors and the sum of the second word vectors corresponding to the first keyword vectors and the second keyword vectors; respectively calculating a first length and a second length of the first text data and the second text data; determining a first text feature corresponding to the first text data of the questioning identity based on the sum of the first word vectors and the first length; and determining a second text feature corresponding to the second text data of the questioning identity based on the sum of the second word vectors and the second length.

In an embodiment of the present disclosure and other possible embodiments, the method for determining a first text feature corresponding to first text data of the questioning identity based on the sum of the first word vectors and the first length includes: dividing the sum of the first word vectors by the first length to obtain a first text feature corresponding to the first text data of the questioning identity.

In an embodiment of the present disclosure and other possible embodiments, the method for determining a second text feature corresponding to second text data of the questioning identity based on the sum of the second word vectors and the second length includes: and dividing the sum of the second word vectors by the second length to obtain a second text feature corresponding to the second text data of the questioning identity.

Step S102: and determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis.

In an embodiment of the disclosure, the method for determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis includes: respectively determining cognition corresponding to the plurality of users; classifying the plurality of users according to the cognition corresponding to the plurality of users; and respectively determining the perception sensitivity corresponding to the classified users based on the classified users.

In embodiments of the present disclosure, when the type of the plurality of users is configured as mother (e.g., new mother) or the context and/or scenario corresponding to the public opinion data is configured as feeding alone, the cognitive or multiple mainstream value views are configured as one or more of a sense of security, female care, age stress, job site stress, social view, marital view, traditional aesthetic, anti-traditional aesthetic, superdeviant.

Fig. 5 illustrates a perceived sensitivity map corresponding to the user under the same or different cognition according to another embodiment of the present disclosure. As shown in fig. 5, the public opinion data is configured such that when a new mother corresponds to a post or is hired, the perceived sensitivity of the user under the cognition corresponding to job site pressure is anger. For example, the plurality of users are configured as new moms, and the cognition or the plurality of mainstream value views corresponding to the plurality of users (new moms) are respectively determined. For example, the cognitive or mainstream value views corresponding to the plurality of users (new mom) include: one or more of security, female care, age stress, job site stress, vigilance, marital, traditional aesthetic, anti-traditional aesthetic, and superdestinationsense. And classifying the plurality of users (new mom) according to the cognitive or mainstream value views corresponding to the plurality of users to obtain classified users (new mom) under different cognitive or cognitive mainstream value views, and respectively determining the perception sensitivity corresponding to the classified users (new mom).

In an embodiment of the disclosure, before the obtaining public opinion data and respectively performing attitude analysis on a plurality of users corresponding to the public opinion data, a method for determining a public opinion analysis vector corresponding to the public opinion data includes: converting the public opinion data into text data and converting the text data into a word list; converting each word in the word list into a word vector by using a preset word vector model; respectively converting the word vectors into public opinion analysis text vectors; and analyzing attitudes of a plurality of users corresponding to the public opinion data based on the public opinion analysis text vector.

In embodiments of the present disclosure and other possible embodiments, the preset word vector model may be configured as one or more of a word2vec model or a GloVe model. The training method of the preset word vector model comprises the following steps: obtaining training corpus of a preset word vector model according to the text data; and inputting the training corpus into a preset vector model, and training the preset word quantity model. And converting each word in the word list into a word vector by using the trained preset word vector model.

In an embodiment of the present disclosure and other possible embodiments, a method for obtaining a training corpus of a text data word vector model according to the text data includes: and segmenting the text data, and converting the text data into a word list to obtain training corpus of a word vector model.

In embodiments of the present disclosure and other possible embodiments, the method for converting text data into a word list includes: and performing word segmentation by using a word segmentation tool (such as a jieba word segmentation tool) and a custom dictionary in the word segmentation tool, setting a stop word list, filtering the stop words of the text data by using the stop word list, and converting the text data into a word list. The stop words can be added into the existing stop word list according to specific scenes, and the words to be removed are set. Wherein, the stop words can be configured as one or more of the mood words, the auxiliary words and/or punctuation marks.

In an embodiment of the present disclosure and other possible embodiments, the method for converting the word vectors into text vectors for public opinion analysis, respectively, includes: and splicing the word vectors to obtain the public opinion analysis text vector. The method for splicing the word vectors to obtain the text vector for public opinion analysis comprises the following steps: respectively determining a mean word vector, a maximum word vector and a minimum word vector corresponding to the word vector; for the mean word vector, the maximum word vector, the minimum word vector, the mean word vector, the maximum word vector and the minimum word vector

In an embodiment of the disclosure, the method for performing attitude analysis on the plurality of users corresponding to the public opinion data based on the public opinion analysis text vector includes performing attitude classification on the plurality of users corresponding to the public opinion data by using the public opinion analysis text vector based on a preset multi-classification model to obtain a plurality of analysis results corresponding to the attitude analysis.

In embodiments of the present disclosure and other possible embodiments, the preset multi-classification model may be configured as a Machine Learning (ML) classification model. For example, one or more of support vector machines, decision trees, random forests, K-nearest neighbors, logistic regression, adaptive enhancement, linear discriminant analysis, and multi-layer perceptrons.

In embodiments of the present disclosure and other possible embodiments, the preset multi-classification model is configured as a Random Forest (RF). And the RF is used for constructing a plurality of decision trees, comprehensively evaluating the predicted results of the decision trees and obtaining a plurality of analysis results corresponding to the attitude analysis.

For another example, in the embodiments of the present disclosure and other possible embodiments, the preset multi-classification model is configured as a model corresponding to a K-nearest neighbor (KNN), so as to perform attitude classification on a plurality of users corresponding to the public opinion data, and obtain a plurality of analysis results corresponding to the attitude analysis.

Core idea of KNN: if a sample most of the k nearest samples in the feature space belong to a certain class, then the sample also belongs to that class and has the characteristics of the samples on that class. In particular, since the KNN model is a non-parametric, inert algorithm model, this means that the KNN model does not make any assumptions on the data characteristics; that is, the model structure established by the KNN model is determined based on the data characteristics. Therefore, the KNN model does not need to be trained by using a large amount of data, and has the advantages of high training speed and insensitivity to abnormal values, and the disadvantages of high memory requirement and insensitivity to data characteristic scale of the non-existent backbone.

In addition, the KNN model is also affected by factors, wherein the factors affecting the KNN model are a point distance calculation mode, a K value selection and a decision rule respectively.

(a) Point distance calculation

Common ways to measure the spatial midpoint distance include manhattan distance calculation, euclidean distance calculation, and the like. However, usually, the KNN model uses a mathematical expression of the euclidean distance between two points in a two-dimensional space, as shown in expression (1).

Where ρ is two points (x ₁ ,y ₁ ) And (x) ₂ ,y ₂ ) Euclidean distance between them.

The mathematical expression of the euclidean distance is shown in formula (2) for the three-dimensional space.

Wherein ρ is two points (x ₁ ,y ₁ ,z ₁ ) And (x) ₂ ,y ₂ ,z ₂ ) Euclidean distance between them.

For an n-dimensional space, the mathematical expression of the distance is shown as formula (3).

Wherein ρ is the sum of the values in the n-dimensional space (x ₁ ,y ₁ )、(x ₂ ,y ₂ )…(x _n ,y _n ) Euclidean distance between them.

(b) K value selection

The value of K is critical to the accuracy of the model, usually starting from a smaller value of K, increasing the value of K continuously, then calculating the variance of the validation set, and finally finding a more suitable value of K. When K is increased, the error rate will generally decrease first, as more samples around can be used for reference, and the classification effect will be better. But the error rate is higher when the K value is larger.

(c) Selection of decision rules

In classification tasks, the KNN model typically employs a majority voting method or a weighted majority voting method. Wherein the weight of each sample in the majority voting method is the same, and the weight of each sample in the weighted majority voting method is different, and the weight is usually calculated in an inverse proportion manner with the distance. In the regression task, the KNN model generally adopts an average method or a weighted average method.

In an embodiment of the disclosure, the method for performing attitude analysis on the plurality of users corresponding to the public opinion data based on the public opinion analysis text vector further includes: respectively acquiring a plurality of public opinion analysis text vectors corresponding to the same user on a plurality of public opinion platforms or the public opinion data of the same public opinion platform; respectively splicing and fusing the plurality of public opinion analysis text vectors; based on a preset multi-classification model, carrying out attitude classification on each user by utilizing the fusion public opinion analysis text vector after the fusion, so as to obtain a plurality of analysis results corresponding to the attitude analysis of each user.

In an embodiment of the present disclosure, before performing attitude analysis on the plurality of users corresponding to the public opinion data, a method for determining the plurality of users corresponding to the public opinion data includes: obtaining social network topology corresponding to public opinion data, and generating a public opinion propagation search tree based on the social network topology; determining a public opinion initial user corresponding to a public opinion propagation search tree source node of the public opinion data propagation by using the public opinion propagation search tree; and determining a plurality of users corresponding to the public opinion data based on the social network topology corresponding to the public opinion initial user.

In the embodiments of the present disclosure and other possible embodiments, the method for determining a plurality of users corresponding to the public opinion data based on the social network topology corresponding to the public opinion initial user includes: determining a plurality of first propagation nodes connected with the social network topology initial nodes according to the social network topology initial nodes corresponding to the public opinion initial users; determining, based on the social network topology, a second propagation node connected to the plurality of first propagation nodes using the plurality of first propagation nodes; until a final propagation node connected with a previous level propagation node is determined based on the social network topology; and determining the initial node and the propagation node as a plurality of users corresponding to the public opinion data.

In an embodiment of the disclosure, the method for generating a public opinion propagation search tree based on the social network topology includes: generating a public opinion propagation topological graph based on the social network topology; and constructing a public opinion propagation search tree based on the public opinion propagation topological graph.

In an embodiment of the disclosure, the method for determining a public opinion initial user corresponding to a public opinion propagation search tree source node of the public opinion data propagation by using the public opinion propagation search tree includes: calculating a propagation center value and a priori estimated value corresponding to the public opinion propagation search tree node by using the public opinion propagation search tree; and tracing the public opinion data according to the propagation center value and the prior estimated value, and determining a public opinion initial user corresponding to a public opinion propagation search tree source node of the public opinion data propagation.

In the embodiments and other possible embodiments of the present disclosure, before the obtaining the social network topology corresponding to the public opinion data, establishing the social network topology corresponding to the public opinion data includes: acquiring a user under preset information and address information; and establishing social network topology corresponding to the public opinion data based on the public opinion data. The address information of the user can be configured as IP address information of the user, and the preset information can be configured as web page information corresponding to a browsing address of the user.

In an embodiment of the present disclosure and other possible embodiments, the method for establishing a social network topology corresponding to the public opinion data based on the address information and the setting information further optimizes a user who establishes the social network topology, including: determining potential users by using the address information of the users; and selecting the potential users by utilizing the preset information to obtain the end users for constructing the social network topology.

In the embodiments of the present disclosure and other possible embodiments, in the social network topology, users may be abstracted into different public opinion propagation search tree nodes, and an interactive relationship exists between the users, that is, a connection relationship exists between two public opinion propagation search tree nodes. When public opinion data information is transmitted in the social network topology, the source node of the public opinion transmission search tree can be rapidly positioned by using a tracing algorithm, and the negative influence of the public opinion data information on the network is minimized. Therefore, when performing the traceability calculation, a propagation network needs to be constructed according to the structure of the social network topology and the propagated public opinion propagation search tree node set. And a tracing algorithm is applied to the propagation network to rapidly and accurately position the public opinion propagation search tree source node. In the social network topology, each edge connects two public opinion propagation search tree nodes, and the social network topology edge connection relationship can form an adjacency matrix.

In embodiments of the present disclosure and other possible embodiments, for a piece of public opinion data, the public opinion propagation search tree node may be in one of two states of "belief" and "not belief," where "believes" is that the public opinion propagation search tree node has been propagated; the belief is that the public opinion propagation search tree node is not propagated and is in a susceptible state. After public opinion information is transmitted in the social network topology, the public opinion transmission search tree nodes in the transmission state transmit to the public opinion transmission search tree nodes around the public opinion transmission search tree nodes, and a local transmission network is formed after a certain time step. Therefore, the public opinion propagation search tree node in the propagation state at the initial moment is a core public opinion propagation search tree node of the propagation network, and other public opinion propagation search tree nodes in the propagation network are all caused by the propagation of the public opinion propagation search tree node, so that the importance degree of the core public opinion propagation search tree node in the network is higher than that of other propagation public opinion propagation search tree nodes. Wherein if a public opinion propagation search tree node is propagated, the public opinion propagation search tree node will always be in a propagated state and will not be propagated again.

In embodiments of the present disclosure and other possible embodiments, a set of propagated public opinion propagation search tree nodes is constructed within a certain propagation round. If the public opinion propagation search tree node is propagated, adding the public opinion propagation search tree node into the propagation search tree node set. And after information transmission is stopped, obtaining all transmitted public opinion transmission search tree nodes and public opinion transmission topological graphs according to the transmission public opinion transmission search tree node set and the adjacency matrix.

In an embodiment of the present disclosure and other possible embodiments, the method for generating a public opinion propagation topology map based on the social network topology includes: configuring propagation probability for public opinion propagation search tree nodes of the social network topology; determining whether public opinion propagation search tree nodes of the social network topology are propagated based on a set propagation round; if yes, the propagated public opinion propagation search tree node is determined to be the propagated public opinion propagation search tree node, and a public opinion propagation topological graph is generated based on the propagated public opinion propagation search tree node set constructed by the propagated public opinion propagation search tree node and the corresponding adjacency matrix. The propagation probability may be configured as a propagation probability corresponding to the easy propagation state, the difficult propagation state, or the difficult propagation state. And constructing a breadth-first public opinion propagation search tree taking a public opinion propagation search tree node as a root public opinion propagation search tree node based on the public opinion propagation topological graph according to the public opinion propagation topological graph induced in the social network topology, performing traceability operation, and determining a public opinion initial user corresponding to the public opinion data propagation public opinion propagation search tree source node.

In the embodiments of the present disclosure and other possible embodiments, the method for constructing a public opinion propagation search tree based on the public opinion propagation topological graph includes: and determining a root public opinion propagation search tree node of the public opinion propagation topological graph, and establishing a public opinion propagation search tree based on the root public opinion propagation search tree node. In embodiments of the present disclosure and other possible embodiments, the establishing of the public opinion propagation search tree based on the root public opinion propagation search tree node may be a breadth-first public opinion propagation search tree established based on the root public opinion propagation search tree node.

In an embodiment of the present disclosure and other possible embodiments, the method for calculating a propagation center value corresponding to a public opinion propagation search tree node and an a priori estimate value corresponding to the public opinion propagation search tree node by using the public opinion propagation search tree includes: based on the public opinion propagation search tree, respectively calculating a first probability value corresponding to the public opinion propagation topological graph under the public opinion propagation search tree node and a second probability value corresponding to the public opinion propagation search tree node; and respectively configuring the first probability value and the second probability value as a propagation center value and a priori estimated value corresponding to the public opinion propagation search tree node.

In an embodiment of the present disclosure and other possible embodiments, the method for tracing the public opinion data according to the propagation center value and the prior estimation value to determine a public opinion propagation search tree source node of the public opinion data propagation includes: determining the tracing probability of the public opinion propagation search tree node based on the propagation center value and the prior estimated value; and tracing the public opinion data according to the tracing probability and the set tracing probability to determine a public opinion propagation search tree source node of the public opinion data propagation. Wherein, the person skilled in the art can set the setting traceability probability according to actual needs. In the embodiments of the present disclosure and other possible embodiments, the method for tracing the public opinion data according to the tracing probability and the set tracing probability to determine a public opinion propagation search tree source node of the public opinion data propagation includes: if the traceability probability is larger than or equal to the set traceability probability, determining a public opinion propagation search tree source node of the public opinion data propagation by a public opinion propagation search tree node corresponding to the set traceability probability; otherwise, the public opinion propagation search tree node corresponding to the set traceability probability determines the non-public opinion propagation search tree source node of the public opinion data propagation.

In an embodiment of the disclosure, the method for determining the traceability probability of the public opinion propagation search tree node based on the propagation center value and the prior estimation value includes: and multiplying the propagation center value by the prior estimated value to determine the tracing probability of the public opinion propagation search tree node.

The embodiment of the disclosure also provides a public opinion data mining method applied to target users, comprising: according to the method, the target user is recommended based on the perception sensitivity corresponding to the plurality of users of the public opinion data under the same cognition or different cognitions.

The embodiment of the disclosure also provides a public opinion data mining method applied to a speaker, comprising: according to the method, the target user is determined based on the perception sensitivity corresponding to the users of the public opinion data under the same cognition or different cognitions; and recommending the speaker corresponding to the public opinion data based on the target user.

The main body of execution of the public opinion data mining method may be a public opinion data mining system, for example, the public opinion data mining method may be executed by a terminal device or a server or other processing device, wherein the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal digital processing (Personal DigitalAssistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or the like. In some possible implementations, the public opinion data mining method may be implemented by a processor invoking computer readable instructions stored in a memory.

It will be appreciated by those skilled in the art that in the above public opinion data mining method of the specific embodiments, the written order of steps is not meant to imply a strict order of execution but rather should be construed according to the function and possibly inherent logic of the steps.

Fig. 6 shows a block diagram of a public opinion data mining system according to an embodiment of the present disclosure. As shown in fig. 6, the public opinion data mining system includes: the analysis unit is used for acquiring public opinion data and respectively carrying out attitude analysis on a plurality of users corresponding to the public opinion data; and the determining unit is used for determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis.

As shown in fig. 6, the public opinion data mining system, applied to a target user, includes: the analysis unit is used for acquiring public opinion data and respectively carrying out attitude analysis on a plurality of users corresponding to the public opinion data; the determining unit is used for determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis; the first recommending unit is used for recommending the target user based on the perception sensitivity corresponding to the plurality of users of the public opinion data under the same cognition or different cognitions.

As shown in fig. 6, the public opinion data mining system, applied to a target user, includes: the analysis unit is used for acquiring public opinion data and respectively carrying out attitude analysis on a plurality of users corresponding to the public opinion data; the determining unit is used for determining the perception sensitivity corresponding to the user under the same cognition or different cognitions based on a plurality of analysis results corresponding to the attitude analysis; the second recommending unit is used for recommending the target user based on the perception sensitivity corresponding to the plurality of users of the public opinion data under the same cognition or different cognitions; and recommending the speaker corresponding to the public opinion data based on the target user.

In some embodiments, the functions or modules included in the apparatus provided by the embodiments of the present disclosure may be used to perform the public opinion data mining method described in the foregoing method embodiments, and the specific implementation of the public opinion data mining method may refer to the description of the foregoing public opinion data mining method embodiments, which is not repeated herein for brevity.

The disclosed embodiments also provide a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described public opinion data mining method. The computer readable storage medium may be a non-volatile computer readable storage medium.

The embodiment of the disclosure also provides an electronic device, which comprises: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the public opinion data mining method. The electronic device may be provided as a terminal, server or other form of device.

Fig. 7 is a block diagram of an electronic device 800, according to an example embodiment. For example, electronic device 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.

Referring to fig. 7, an electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the public opinion data mining method described above. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen between the electronic device 800 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operational mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 further includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.

The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the electronic device 800. For example, the sensor assembly 814 may detect an on/off state of the electronic device 800, a relative positioning of the components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in position of the electronic device 800 or a component of the electronic device 800, the presence or absence of a user's contact with the electronic device 800, an orientation or acceleration/deceleration of the electronic device 800, and a change in temperature of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices, either wired or wireless. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.

In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 804 including computer program instructions executable by processor 820 of electronic device 800 to perform the above-described methods.

Fig. 8 is a block diagram illustrating an electronic device 1900 according to an example embodiment. For example, electronic device 1900 may be provided as a server. Referring to fig. 8, electronic device 1900 includes a processing component 1922 that further includes one or more processors and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by processing component 1922. The application programs stored in memory 1932 may include one or more modules each corresponding to a set of instructions. Further, processing component 1922 is configured to execute instructions to perform the methods described above.

The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 1932, including computer program instructions executable by processing component 1922 of electronic device 1900 to perform the methods described above.

The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, 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. 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.

The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. The public opinion data mining method is characterized by comprising the following steps of:

2. The public opinion data mining method according to claim 1, characterized in that the method for performing attitude analysis on a plurality of users corresponding to the public opinion data, respectively, comprises:

acquiring emotion expression key information;

3. The public opinion data mining method according to claim 1 or 2, characterized in that the method of determining the perception sensitivity corresponding to the user under the same or different cognition based on a plurality of analysis results corresponding to the attitude analysis comprises:

respectively determining cognition corresponding to the plurality of users;

4. A public opinion data mining method according to any of claims 1-3, characterized in that prior to said determining the perception sensitivity corresponding to the users under the same or different cognition, determining the cognitive method corresponding to the plurality of users comprises:

determining a situation and/or a plot corresponding to the public opinion data;

the method for acquiring public opinion data comprises the following steps:

5. The public opinion data mining method of claim 4, wherein the method of generating a public opinion propagation search tree based on the social network topology comprises:

determining a number of speakers of the speech signal;

6. A public opinion data mining method is applied to target users, and comprises the following steps: the method of any one of claims 1-5, wherein the target user is recommended based on perceived sensitivities corresponding to a plurality of users of the public opinion data under the same cognition or different cognitions.

7. A public opinion data mining method is applied to a speaker and comprises the following steps: the method of any one of claims 1-5, wherein the target user is determined based on perceived sensitivities corresponding to a plurality of users of the public opinion data under the same cognition or different cognitions; and recommending the speaker corresponding to the public opinion data based on the target user.

8. A public opinion data mining system, comprising:

9. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

Wherein the processor is configured to invoke the instructions stored by the memory to perform the public opinion data mining method of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the public opinion data mining method of any of claims 1 to 7.