User personality prediction based on topic preference and sentiment analysis using LSTM model

2020 ◽  
Vol 138 ◽  
pp. 397-402
Author(s):  
Jinghua Zhao ◽  
Dalin Zeng ◽  
Yujie Xiao ◽  
Liping Che ◽  
Mengjiao Wang
Keyword(s):  
Sentiment Analysis ◽  
User Personality ◽  
Personality Prediction

Sentimental prediction model of personality based on CNN-LSTM in a social media environment

2020 ◽  
pp. 1-10
Author(s):  
Jinghua Zhao ◽  
Jie Lin ◽  
Shuang Liang ◽  
Mengjiao Wang
Keyword(s):  
Social Media ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Support Effect ◽  
Media Environment ◽  
Short Text ◽  
Solid Foundation ◽  
User Personality ◽  
Personality Prediction ◽  
Good Support

The paper first analyzes the correlation between text sentiment values and personality traits, proves that text sentiment can have a good support effect on user personality prediction, then on this basis, a method based on CNN-LSTM is proposed, which can be used to deeply analyze the sentiment analysis capability of the model, hoping to improve the precision of sentiment classification and lay a solid foundation for the next experiment. This experiment proves that the CNN-LSTM constructed in this paper can better predict the emotional tendency of the short text of microblog, has good generalization ability, and has higher precision than other methods.

A psychologically-inspired fuzzy-based approach for user personality prediction in rumor propagation across social networks

2021 ◽  
pp. 1-15
Author(s):  
V. Indu ◽  
Sabu M. Thampi
Keyword(s):  
Social Networks ◽  
Factor Model ◽  
Five Factor Model ◽  
Principal Component ◽  
Common People ◽  
Rumor Propagation ◽  
Usage Patterns ◽  
User Personality ◽  
Personality Prediction ◽  
High Degree

Social networks have emerged as a fertile ground for the spread of rumors and misinformation in recent times. The increased rate of social networking owes to the popularity of social networks among the common people and user personality has been considered as a principal component in predicting individuals’ social media usage patterns. Several studies have been conducted to study the psychological factors influencing the social network usage of people but only a few works have explored the relationship between the user’s personality and their orientation to spread rumors. This research aims to investigate the effect of personality on rumor spread on social networks. In this work, we propose a psychologically-inspired fuzzy-based approach grounded on the Five-Factor Model of behavioral theory to analyze the behavior of people who are highly involved in rumor diffusion and categorize users into the susceptible and resistant group, based on their inclination towards rumor sharing. We conducted our experiments in almost 825 individuals who shared rumor tweets on Twitter related to five different events. Our study ratifies the truth that the personality traits of individuals play a significant role in rumor dissemination and the experimental results prove that users exhibiting a high degree of agreeableness trait are more engaged in rumor sharing activities and the users high in extraversion and openness trait restrain themselves from rumor propagation.

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

scholarly journals Myers-briggs Personality Prediction and Sentiment Analysis of Twitter using Machine Learning Classifiers and BERT

2021 ◽  
Vol 13 (6) ◽  
pp. 48-60
Author(s):  
Prajwal Kaushal ◽  
◽  
Nithin Bharadwaj B P ◽  
Pranav M S ◽  
Koushik S ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Analysis Model ◽  
Research Recruitment ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Myers Briggs ◽  
Personality Prediction ◽  
Twitter Users ◽  
Psychological Instruments

Twitter being one of the most sophisticated social networking platforms whose users base is growing exponentially, terabytes of data is being generated every day. Technology Giants invest billions of dollars in drawing insights from these tweets. The huge amount of data is still going underutilized. The main of this paper is to solve two tasks. Firstly, to build a sentiment analysis model using BERT (Bidirectional Encoder Representations from Transformers) which analyses the tweets and predicts the sentiments of the users. Secondly to build a personality prediction model using various machine learning classifiers under the umbrella of Myers-Briggs Personality Type Indicator. MBTI is one of the most widely used psychological instruments in the world. Using this we intend to predict the traits and qualities of people based on their posts and interactions in Twitter. The model succeeds to predict the personality traits and qualities on twitter users. We intend to use the analyzed results in various applications like market research, recruitment, psychological tests, consulting, etc, in future.

Predicting user personality with social interactions in Weibo

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuting Jiang ◽  
Shengli Deng ◽  
Hongxiu Li ◽  
Yong Liu
Keyword(s):  
Social Media ◽  
Social Interaction ◽  
Personality Traits ◽  
Interaction Data ◽  
Content Type ◽  
Social Media Platforms ◽  
Media Platform ◽  
User Personality ◽  
Personality Prediction ◽  
Media Data

PurposeThe purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality.Design/methodology/approachSocial interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data.FindingsThe results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. “Reposting,” “being reposted,” “commenting” and “being commented on” were found to be the key interaction features that reflected Weibo users' personalities, whereas “liking” was not found to be a key feature.Originality/valueThe findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

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