CN111177583A - Social platform-based interpersonal analysis method and system - Google Patents
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Abstract
The invention discloses a method and a system for analyzing a relationship between persons based on a social platform, wherein the method comprises the following steps: acquiring personal information and social information of a target user group from a social platform; acquiring various relationship information of the users in the target user group based on the personal information and the social information; and constructing a relationship network according to the various relationship information of the users in the target user group. The invention can quickly mine various interpersonal relationships of the target user and quantize the interpersonal relationships based on the complex social network data.
Description
Technical Field
The invention belongs to the technical field of internet data mining, and particularly relates to a method and a system for analyzing a person relationship based on a social platform.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The social network covers all network service forms taking human social as a core, and the internet is an interactive platform capable of mutual communication and participation. In addition, the rapid development of the internet also promotes the social transparency, and the activity information of people in the network can be found in all forums and blogs.
However, it is very difficult to quickly mine social connections from cluttered and complex internet data. Currently, only basic information, posting information, friend information, hometown, residence, living, education experience, work experience and other information of users can be searched based on network forums such as Facebook, linkedIn and the like, but the searched personal information of the users has no direct relationship, and the contents and modes of each person shown in the forums are different.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a system for analyzing the relationship between persons based on a social platform, which can quickly mine and quantify various relationships between persons of a target user based on complex social network data.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
a method for analyzing a relationship between persons based on a social platform comprises the following steps:
acquiring personal information and social information of a target user group from a social platform;
acquiring various relationship information of the users in the target user group based on the personal information and the social information;
and constructing a relationship network according to the various relationship information of the users in the target user group.
Further, the personal information includes identity information and resume information; the social information comprises friend information and network track behavior data information of the user on the social platform.
Further, the relationship of the multiple kinds of relationship includes relationship of relatives, relationship of alumni, relationship of coworkers, relationship of neighbors, relationship of important relationship of relationship.
Further, the obtaining of the various relationship information of the users in the target user group includes:
judging whether a alumni relationship or a colleague relationship exists or not by comparing education experiences or work experiences between two users;
judging whether a neighbor relation exists or not according to the geographical positions of the place of birth/the place of residence between the two users; determining whether a relative relationship exists between the users according to the distance of the geographic position of the place of birth between the two users, the relative information published in the personal information of the users and the social information;
matching the positions and the descriptions of the two users and the concerned keywords according to the positions and the descriptions of the two users, and determining whether an important relationship exists; the matching is based on a pre-constructed attention object keyword library, and if the number of the keywords obtained by matching is larger than a set value, the important relationship exists.
Further, after obtaining a plurality of kinds of relationship information of the users in the target user group, the intimacy degree between the users is predicted, and the intimacy degree prediction method comprises the following steps:
quantifying the relationship of the arteries obtained in the step based on the corresponding grades and weights of the various relationships of the arteries divided in advance;
and predicting the intimacy between the users based on the intimacy prediction model.
Further, the establishing method of the intimacy degree prediction model comprises the following steps:
collecting supervision sample data, wherein the supervision sample data covers users with various interpersonal relationships;
quantifying the relationship of the interpersonal relationship among users in the supervision sample data, and assigning a relative density value;
and marking the sample data with the intimacy value larger than the set value as a positive example, marking other sample data as a negative example, and establishing an intimacy prediction model based on the logistic regression model.
Further, the relationship network is constructed based on a graph structure, nodes of the graph represent users, and edges represent relationships; wherein the relationship is described by the name of the relationship of the interpersonal relationship and/or the intimacy.
One or more embodiments provide a social platform-based personal relationship analysis system, comprising:
the user information capturing module is used for acquiring personal information and social information of a target user group from the social platform;
the relationship analysis module acquires various relationship information of the users in the target user group based on the personal information and the social information;
and the relationship network construction module is used for constructing a relationship network according to various relationship information of the users in the target user group.
One or more embodiments provide an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the social platform based human relationship analysis method when executing the program.
One or more embodiments provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the social platform based approach to relationship analysis.
The above one or more technical solutions have the following beneficial effects:
the invention identifies the key words related to the relationship between the user information and the user from the social platform with various expression modes by means of natural voice processing methods such as named entity identification and the like, takes the key words as the supplement of the filled-in data of the user, fully analyzes the relationship between the users, and subdivides the relationship between the relationship and the user into school friends (college and college), colleagues, relatives (family members and family names) and the like.
Besides mining various types of interpersonal relationships among users, the invention also establishes an intimacy prediction model for calculating intimacy among the users based on the mined interpersonal relationships and intuitively measuring the intimacy degree among the users.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flowchart of a social platform based method for analyzing relationships of people according to an embodiment of the present invention;
FIG. 2 is a flow chart of data processing according to an embodiment of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment provides a method for analyzing a relationship between persons based on a social platform, aiming at capturing relevant information based on the social platform aiming at a target user group, and mining the relationship between the users in the target user group to construct a relationship between persons and a relationship network. The method specifically comprises the following steps:
step 1: personal information and social information of a target user group are obtained from the social platform.
Wherein the target user group may be defined by setting an area range or a condition range. For example, B city may be used to define an initial target user population in order to obtain all the relationships of the target users in B city.
The social platform includes, but is not limited to, microblog, circle of friends, Facebook, LinkedIn, and is not limited herein. The personal information comprises identity information and resume information, and specifically comprises data items such as personal basic information, living information, work experience, education experience and the like, wherein the personal basic information comprises identity information such as names, past names, birth months and nationalities, the living information comprises places of birth, places of residence and the like, the work experience comprises worked work units, positions of work and work starting time, and the education experience comprises schools, professions, years of study and the like; the social information comprises friend information of the user on a social platform, and data items of network track behavior data information (concerned content, related comments, posts, praise, related geographical location positioning information and the like).
In this embodiment, Named entity identification (NER) is used to identify Named Entities such as a person name, a place name, an organization name, a relationship name, and the like in personal information and social information. Since these named entities are increasing in number, they are usually not possible to be listed exhaustively in dictionaries, and their construction methods have their own regularity, the recognition of these words is usually handled independently from the task of lexical morphological processing (e.g. chinese segmentation), called named entity recognition. The named entity recognition technology is an indispensable component of various natural language processing technologies such as information extraction, information retrieval, machine translation, question and answer systems and the like. The system adopts a jieba word segmentation system, and mainly realizes three modules of word segmentation, part of speech tagging and keyword extraction. The HMM model is used for word segmentation, and the word segmentation problem is mainly regarded as a sequence labeling (sequence labeling) problem, wherein sentences are observation sequences, and the word segmentation result is a state sequence. Firstly, training a model related to an HMM through corpus, then solving by using a Viterbi algorithm to finally obtain an optimal state sequence, and then outputting a word segmentation result according to the state sequence.
Step 2: and acquiring various types of relationship information of the users in the target user group based on the personal information and the social information.
The multiple relationship information includes a relative relationship, a alumni relationship, a colleague relationship, a neighbor relationship, a friend-involved relationship, etc.
(1) And acquiring the alumni relationship and the colleague relationship.
Whether a alumni relationship or a colleague relationship exists is judged by comparing the educational experience or the work experience between the two users. Specifically, the alumni/colleague relationship between users is obtained based on the name of the school/work unit and the time of the user at the school/work unit.
the relation of the colleagues includes that the colleagues and the colleagues regard the colleagues as same as the school name of the school with the school T in the education experience (T > -1), regard as the colleagues as same as the school with the school in the school if the school time difference does not exceed N years (N > -0.5), and regard as same as the work units in the work experience (T > -1) if the same work units occur and the entry time S1, S2 and the departure time E1, E2 of the two persons intersect, namely (S1+ E1) # (S1+ E2) intersects as non-empty.
(2) And acquiring a neighbor relation and a relative relation.
And judging whether the neighbor relation exists or not according to the geographical positions of the place of birth/the place of residence between the two users. And determining whether the relative relationship exists between the users according to the distance of the geographic position of the place of birth between the two users, the relative information published in the personal information of the users and the social information.
If the place of birth/place of living user is not filled in on the social platform, analysis can be carried out according to the positioning information of the user: and acquiring all positioning information of the user and time and frequency corresponding to the same positioning information, and accordingly judging the place of birth/place of residence.
Specifically, the neighbor relations include a close-proximity relation, a Country neighbor relation, and a proximity relation. Comparing and analyzing the places where the two users live, the places where the two users live and the hometown in pairs, wherein if the hometown address of the relation person A is close to the geographical position of the place where the relation person B live, the relation person A is in close proximity; if the hometown of the relation person A is consistent with the hometown geographic position of the relation person B, the relation person A is a same-hometown neighbor relation; if the home address of the relation A is close to the geographical position of the current residence place of the relation B, the relation is a proximity relation.
The family members: the related person appears N times (N > ═ 1) in the family member information item issued by the target person. Family name relatives: the relationship person appears in the family member information item released by the target person, and coincides with the last name appearing of the target person and other family members for N times (N > ═ 1). If the target person A issues related family member information, the target person is in a relationship with the group of people; if the matching between the target person A and the names of the persons related to the friend circle is the same surname, the relationship is the relationship of relatives.
(3) And for each user in the target user group, analyzing the relationship between the user and the user with the friend relationship.
(3-1) obtaining important interpersonal relationships
And constructing an attention object keyword library in advance, and extracting keywords of the content concerned by the user based on the word library to obtain the keywords concerned by the user.
If the job positions and the descriptions of the two users are engaged in; and for two users with friend relationships, comparing and analyzing according to the keywords concerned by the user A and the job positions and descriptions engaged in by the user B, and judging whether the user B is the important relationship of the user A. If T matching items (T > -1) appear in the matching, the relationship is regarded as an important relationship.
Specifically, according to the requirements of specific business scenes, an attention object keyword library for companies, academic institutions and intellectual libraries and an important position and industry attention object keyword library for business attention are established.
(3-2) obtaining the relationship of the friends involved in the flowers
Analyzing 7 dimensionalities of the fiduciary elements in the hometown, the residential place, the place where the people live in China, the social attribute and the geographic position information according to the Chinese pinyin characteristics in the names of the related people in the friend range of the target person, whether the university where the education experience exists is in China, and if the two dimensionalities are matched with N (N > -1), considering the relationship of the fiduciary friends; according to the posting information of the target person and the friends, named entity recognition and emotion analysis are carried out on the post content, if there is an actively upward discourse and emotion to China, the friends involved in the Chinese are considered, and the enthusiasm degree of the Chinese is calculated and marked by scoring based on machine learning and feature extraction, so that the accuracy of judging whether the target person and the friends are friends involved in the Chinese can be visually and clearly judged. The friends involved in the country: whether the names of the target person and the related person are Chinese or not and whether the names of the target person and the related person are Chinese pinyin or not; the school where the education experience is present is the number T (T > -1) of domestic schools; hometown, place once living, number of T of present domestic geographical positions (T > -1).
And storing the extracted data in Hive.
And step 3: and calculating the intimacy between the users according to the various relationship information of the users in the target user group.
In this embodiment, the relationship between relatives, alumni, coworkers, neighbors, and wading friends among the relationship information is ranked in advance and given a weight. The more intimate the relationship, the higher the weight. For example, the relationship of relatives includes family members, relatives with the same family name, and relatives, the weights of the three decrease in turn, the relationship of alumni with the same alumni is heavier than the same alumni, and the relationship of neighbors, the relationship of neighbors with the same country, and the relationship of neighbors decrease in turn.
The step 3 specifically includes:
step 3.1: quantifying the relationship of the arteries obtained in the step 2 based on the corresponding grades and weights of the various relationships of the arteries which are divided in advance;
step 3.2: and predicting the intimacy between the users based on the intimacy prediction model.
The establishing method of the intimacy prediction model comprises the following steps:
(1) and carrying out sample data statistics on various human relationship users according to the relationship characteristics.
(2) And performing manual supervised learning according to the counted sample data, wherein the data of the times of the same family members, the times of the same school friends and the time of the same unit T (T >0) is A-type data (the label value is 1), and the other sample data is B-type data (the label is 0).
(3) And establishing an intimacy prediction model based on the supervision sample data, wherein the intimacy prediction model is an LR (model of intimacy prediction) established based on logistic regression.
Quantifying the relationship of the interpersonal relationship among users in the supervision sample data, and assigning a relative density value; and marking the sample data with the intimacy value larger than the set value as a positive example, marking other sample data as a negative example, and establishing an intimacy prediction model LR based on the logistic regression model. Specifically, the intimacy prediction model is created by setting model parameters including the maximum iteration number n (n >0), the regularization coefficient r (r > ═ 0), the threshold t of the binary prediction (range [0,1] interval value), the convergence l of the iterative algorithm (range [0,1] interval value), and the like.
The main idea of Logistic Regression is to fit historical data into a straight line, and use the straight line to perform data prediction on new data, and for Logistic Regression, the idea is also linear Regression. The function output of sigmoid is between (0, 1), so we can consider the sigmoid function as a probability density function of sample data, indicating the probability that the data belongs to a certain class.
For every two users, a probability value v (v e [0,1]) can be obtained based on the logic intimacy degree prediction model, and the probability value v represents the possibility of obtaining the cognition between the two users, namely intimacy degree.
And acquiring prediction data from Hive, and taking the prediction data as prediction data required by the model, thereby obtaining the value of the human vein intimacy degree from the model. And converting the prediction result value from the two-dimensional matrix into a one-dimensional array and storing the one-dimensional array in Hive.
And 4, step 4: and constructing a relationship network according to the various relationship information of the users in the target user group.
And the users in the personal relationship network cover the target user group and the users having friend relationships with the users in the group.
The context relationship network is constructed using a graph structure, wherein nodes of the graph represent users and edges represent relationships. Specifically, the relationship can be described by using the specific relationship obtained in step 2, and/or by using the intimacy degree obtained in step 3. When describing in two ways at the same time, intimacy can be expressed by the thickness of the edge, and the name of the specific relationship of the arteries is indicated by characters on the edge.
Example two
The embodiment aims to provide a social platform-based interpersonal relationship analysis system.
In order to achieve the above object, the present embodiment provides a system for analyzing a relationship between persons based on a social platform, including:
the user information capturing module is used for acquiring personal information and social information of a target user group from the social platform;
the relationship analysis module acquires various relationship information of the users in the target user group based on the personal information and the social information;
and the relationship network construction module is used for constructing a relationship network according to various relationship information of the users in the target user group.
EXAMPLE III
The embodiment aims at providing an electronic device.
In order to achieve the above object, this embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the following steps, including:
acquiring personal information and social information of a target user group from a social platform;
acquiring various relationship information of the users in the target user group based on the personal information and the social information;
and constructing a relationship network according to the various relationship information of the users in the target user group.
Example four
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, performs the steps of:
acquiring personal information and social information of a target user group from a social platform;
acquiring various relationship information of the users in the target user group based on the personal information and the social information;
and constructing a relationship network according to the various relationship information of the users in the target user group.
The steps involved in the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
One or more of the above embodiments have the following technical effects:
the invention identifies the key words related to the relationship between the user information and the user from the social platform with various expression modes by means of natural voice processing methods such as named entity identification and the like, takes the key words as the supplement of the filled-in data of the user, fully analyzes the relationship between the users, and subdivides the relationship between the relationship and the user into school friends (college and college), colleagues, relatives (family members and family names) and the like.
Besides mining various types of interpersonal relationships among users, the invention also establishes an intimacy prediction model for calculating intimacy among the users based on the mined interpersonal relationships and intuitively measuring the intimacy degree among the users.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (9)
1. A method for analyzing a relationship between persons based on a social platform is characterized by comprising the following steps:
acquiring personal information and social information of a target user group from a social platform;
acquiring various relationship information of the users in the target user group based on the personal information and the social information;
and constructing a relationship network according to the various relationship information of the users in the target user group.
2. The social platform-based relationship analysis method of people according to claim 1, wherein the personal information comprises identity information and resume information; the social information comprises friend information and network track behavior data information of the user on the social platform.
3. The social platform based relationship analysis method as claimed in claim 1, wherein the relationships comprise relations between relatives, alumni, coworkers, neighbors, important relationships and friendships.
4. The social platform-based relationship analysis method of claim 1, wherein obtaining a plurality of relationship information of users in the target user group comprises:
judging whether a alumni relationship or a colleague relationship exists or not by comparing education experiences or work experiences between two users;
judging whether a neighbor relation exists or not according to the geographical positions of the place of birth/the place of residence between the two users; determining whether a relative relationship exists between the users according to the distance of the geographic position of the place of birth between the two users, the relative information published in the personal information of the users and the social information;
matching the positions and the descriptions of the two users and the concerned keywords according to the positions and the descriptions of the two users, and determining whether an important relationship exists; the matching is based on a pre-constructed attention object keyword library, and if the number of the keywords obtained by matching is larger than a set value, the important relationship exists.
5. The social platform-based relationship analysis method of claim 1, wherein after obtaining a plurality of relationship information of users in the target user group, the method further predicts intimacy between users, and the intimacy prediction method comprises:
quantifying the relationship of the arteries obtained in the step based on the corresponding grades and weights of the various relationships of the arteries divided in advance;
and predicting the intimacy between the users based on the intimacy prediction model.
6. The social platform-based relationship analysis method of claim 5, wherein the relationship network is constructed based on a graph structure, nodes of the graph represent users, and edges represent relationships; wherein the relationship is described by the name of the relationship of the interpersonal relationship and/or the intimacy.
7. A system for analyzing interpersonal relationships based on a social platform, comprising:
the user information capturing module is used for acquiring personal information and social information of a target user group from the social platform;
the relationship analysis module acquires various relationship information of the users in the target user group based on the personal information and the social information;
and the relationship network construction module is used for constructing a relationship network according to various relationship information of the users in the target user group.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the social platform based relationship analysis method according to any one of claims 1 to 6 when executing the program.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the social platform based relationship analysis method according to any one of claims 1 to 6.
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| CN111858692A (en) * | 2020-07-30 | 2020-10-30 | 重庆新申言科技有限公司 | System and method for calculating interpersonal relationship based on classmate records |
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