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.

scholarly journals A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

2020 ◽  
Vol 4 (4) ◽  
pp. 33
Author(s):  
Toni Pano ◽  
Rasha Kashef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Significant Role ◽  
Prediction Models ◽  
Financial Sector ◽  
Research Gap ◽  
Text Preprocessing ◽  
The Impact

During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.

Do social media create revolutions? Using Twitter sentiment analysis for predicting the Maidan Revolution in Ukraine

2019 ◽  
Vol 15 (3) ◽  
pp. 275-283 ◽  
Author(s):  
Iana Sabatovych
Keyword(s):  
Social Media ◽  
Social Movements ◽  
Public Opinion ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Social Engagement ◽  
Naive Bayes ◽  
Public Attitudes ◽  
Social Media Platforms ◽  
Online Prediction

A wide variety of social media platforms have become integral to contemporary forms of social engagement, including mass protests. Twitter is considered specifically indicative of public attitudes in this regard. This study attempts to examine the feasibility of using Twitter sentiment analysis to predict the 2014 revolution in Ukraine. Tweets representing public opinion are clustered by means of the ‘StreamKM++’ algorithm into three classes (likely, neutral and unlikely). The resulting prediction model for the three classes (using Naïve Bayes) was 96.75 per cent. As such, this study offers a promising way to perform an online prediction of social movements.

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

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.

Stock Market prediction based on Technical –Deviation-ROC indicators using stock and Feeds data

2020 ◽  
Vol 13 ◽  
Author(s):  
P. Victer Paul ◽  
Deepika N
Keyword(s):  
Social Media ◽  
Standard Deviation ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Short Term Memory ◽  
Back Propagation ◽  
Support Vector ◽  
Technical Indicators ◽  
Novel Approach

Background: The attempt of this research is to propose a novel approach for the efficient prediction of stock prices. The scope of this research extends by including the feature of sentiment analysis using the emotions and opinions carried by social media platforms. The research also analyzes of impact of social media, feeds data and Technical indicators on stock prices for the design of prediction model. Objectives: The goal of this research is to analyze and compare the models to predict stock trend by adjusting the feature set. Method: The basic technical and new momentum, volatility indicators are calculated for the benchmark index values of stock. The text summarization applied on collected day wise tweets for a particular company then performed sentiment analysis to get sentiment value. All these collected features were integrated to form the final dataset and accuracy comparisons were made by experimenting the algorithms- Support vector machine (SVM), Backpropogation and Long short-term memory (LSTM). Results: The execution is carried out for each algorithm with 30 epochs. It is observed that the SVM exhibits 2.78%, Backpropogation exhibits 5.02% and LSTM exhibits10.30 % enhanced performance than the prediction model designed using basic technical indicators. And along with human sentiment the SVM provides 5.48%, Backpropogation 5.28% and LSTM 0.07% better accuracy. The standard deviation results are for SVM 1.59, for back propagation 2.46, and LSTM 0.19. Conclusion: The experimental results show that the standard deviation of LSTM is less than the SVM and back propagation algorithms. Hence obtaining the steady accuracy is highly possible with LSTM.

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.

Analysis of Customer-Oriented Information for Tourism Enterprises Under the Social Media Environment

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ziwei Yang
Keyword(s):  
Social Media ◽  
Market Competition ◽  
Virtual Community ◽  
Current Status ◽  
Information Analysis ◽  
Value Analysis ◽  
Media Environment ◽  
The Social ◽  
Study Results ◽  
Tourism Enterprises

Abstract Social media is a virtual community or network platform that the public uses to achieve self-creation and it’s sharing with others; under the social media environment, self-media channels become more abundant, and the autonomy and originality of content dissemination are also continuously enhanced. When tourism enterprises face increasing market competition, personalized and targeted promotional programs will, to a certain extent, have a certain appeal to competitors’ potential customer groups, thereby providing tourism enterprise customers with relevant benefits for oriental information, and also serving as an important way for companies to develop new customers. Based on the summary and analysis of previous literature works, this paper expounded the research status and significance of social media environment, elaborated the development background, current status and future challenges of customer-oriented information analysis for tourism enterprises, introduced the methods and principles of customer’s transfer value and life cycle and social media environment’s cognitive composition, proposed a sentiment model of tourist-oriented information analysis under the social media environment, and analysed the management strategy and scheduling platform of customer-oriented information, constructed an analysis system of customer-oriented information in social media environment, performed the reliability, validity, transfer and perception value analysis of customer-oriented information and finally conducted case simulation and its result analysis. The study results of this paper provide a reference for further researches on the customer-oriented information analysis for tourism enterprises under the social media environment.

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