Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Occupational psychological character analysis method based on social network

A technology of social network and analysis method, applied in the field of psychoanalysis, which can solve the problems of emotional cognition and untargeted research

Active Publication Date: 2019-07-26
JINAN UNIVERSITY
View PDF6 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Emotion and interest recognition are to a certain extent the concrete manifestation of personality characteristics, but it is difficult to form a comprehensive cognition of people only by analyzing emotions
[0008] On the other hand, most of the current relevant researches focus on personality psychology in a general sense, and there is no targeted research on the field of recruitment-job-seeking

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Occupational psychological character analysis method based on social network
  • Occupational psychological character analysis method based on social network
  • Occupational psychological character analysis method based on social network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0087] The present invention provides such as Figure 1-4 Shown is a social network-based occupational psychological personality analysis method, the specific steps are:

[0088] Step 1: Collect the basic information of the user, and label various usage behavior tags, topic tags, and emotional tendencies according to the content of the basic information, and calculate the usage percentage of each tag respectively, and make statistics on the words used in the basic information, including high-frequency words and their rate of use;

[0089] Step 2: Construct a classifier for the four dimensions of MBTI professional personality, and divide the personality into four dimensions: motivation (extraversion / introversion), information collection (feeling / intuition), decision-making style (reason / emotion), and lifestyle (independence / dependence) A total of 16 combinations, use the training data to train the four classifiers respectively, and optimize the classifier by predicting the acc...

Embodiment 2

[0107] Can know by embodiment one:

[0108] In step 2, the classifiers are trained separately, including the Logistic regression algorithm, which is mostly used to estimate the possibility of something. It is a method of learning f:X->Y equation or P(Y|X), where Y is a discrete value , and X= is any vector, where each feature component Xi can take discrete or continuous values. It can be used for probability prediction and classification, and does not require that the features Xi are independent of each other. It is a commonly used machine learning method in the industry. Logistic regression methods include

[0109] 1) Construct a prediction function h;

[0110] 2) Construct loss function J;

[0111] 3) Find a way to minimize the J function and obtain the regression parameters (θ) in three steps, where:

[0112] 1) Construction predictive function h, the present invention uses Logistic function (or claims Sigmoid function), and the form is:

[0113] For the case of a lin...

Embodiment 3

[0153] attached by the manual image 3 , Figure 4 As can be seen from Example 1:

[0154] The analysis results of these classifiers were integrated using the Adaboost iterative algorithm. The core idea of ​​Adaboost is to train different weak classifiers for the same training set, and then combine these weak classifiers to form a stronger final strong classifier. Adaboost determines the weight of each sample according to whether the classification of each sample in each training set is correct and the accuracy of the last overall classification. Send the new data set with modified weights to the lower-level classifier for training, and finally integrate the classifiers obtained from each training, and use it as MBTI for the analysis of motivation, information collection, decision-making methods, and lifestyle. classifier.

[0155] The algorithm of Adaboost in the described step 4 is described as follows:

[0156] Let the training data set T={(x1,y1),(x2,y2)...(xN,yN)}

...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an occupational psychological character analysis method based on a social network, and particularly relates to the field of psychological analysis. The occupational psychological character analysis method comprises the following steps: 1) collecting basic information of a user; 2) constructing an MBTI occupational character four-dimensional classifier; 3) sending a new dataset of which the weight is modified to a lower-layer classifier for training; and 4) completing an MBTI analysis report of the object. According to the occupational psychological character analysis method based on a social network, personal social network content is collected and comprehensive and deep quantitative analysis is carried out; the association between the personal microblog content and the professional psychological character is quantified by means of an MBTI model; the recruiter can comprehensively, rapidly and accurately judge the occupational psychological characters of the candidate according to the content published by the candidate in the social network by collecting the samples to train the classifier, so that an objective basis is provided for decision making; and meanwhile, automation of the whole analysis process is achieved through the information technology, and the time cost of analysis is greatly reduced.

Description

technical field [0001] The present invention relates to the technical field of psychoanalysis, more specifically, the present invention relates to a kind of professional psychological character analysis method based on social network. Background technique [0002] Psychoanalysis refers to the method of inferring another psychological phenomenon, characteristic or behavior from one psychological phenomenon based on the causal relationship between psychological phenomena and between psychological phenomena and behaviors. It judges a person's temperament, character and character through the analysis of people's psychological phenomena and activities (intellectual activities, emotional activities, volitional activities, dreams, etc.), makes a diagnosis of a certain mental disease, and analyzes the quality of a certain thinking. process, predicting (predicting) a certain psychological phenomenon, etc. It can be used for identifying and employing people, interpersonal communicati...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G16H50/20G16H15/00G06K9/62
CPCG16H50/20G16H15/00G06F18/2148G06F18/24
Inventor 朱蔚恒龙舜石文娟王会进
Owner JINAN UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com