Methods for Assessing the Psychological Tension of Social Network Users during the Coronavirus Pandemic and Its Uses for Predictive Analysis

Aida Khakimova (), Oleg Zolotarev, Bhisham Sharma, Shweta Agrawal () and Sanjiv Kumar Jain
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Aida Khakimova: Institute of Information Systems and Engineering Computer Technologies, Russian New University, Radio St. 22, Moscow 105005, Russia
Oleg Zolotarev: Institute of Information Systems and Engineering Computer Technologies, Russian New University, Radio St. 22, Moscow 105005, Russia
Bhisham Sharma: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
Shweta Agrawal: Institute of Advance Computing, Sage University, Indore 452001, Madhya Pradesh, India
Sanjiv Kumar Jain: Department of Electrical Engineering, Medi-Caps University, Indore 452001, Madhya Pradesh, India

Sustainability, 2023, vol. 15, issue 13, 1-19

Abstract: This article address approaches to the development of methods for assessing the psychological state of social network members during the coronavirus pandemic through sentiment analysis of messages. The purpose of the work is to determine the psychological tension index by using a previously developed thematically ranked dictionary. Researchers have investigated methods to evaluate psychological tension among social network users and to forecast the psychological distress. The approach is novel in the sense that it ranks emojis by mood, considering both the emotional tone of tweets and the emoji’s dictionary meanings. A novel method is proposed to assess the dynamics of the psychological state of social network users as an indicator of their subjective well-being, and develop targeted interventions for help. Based on the ranking of the Emotional Vocabulary Index (EVI) and Subjective Well-being Index (SWI), a scheme is developed to predict the development of psychological tension. The significance lies in the efficient assessment of the fluctuations in the mental wellness of network users as an indication of their emotions and a prerequisite for further predictive analysis. The findings gave a computed value of EVI of 306.15 for April 2022. The prediction accuracy of 88.75% was achieved.

Keywords: sentiment analysis; n-gram; Twitter; emoji; social networks; COVID-19 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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