Quantifying Collective Emotions: Japan’s Societal Trends Through Enhanced Sentiment Index Using POMS2 and SNS

2024-12-15

BigData Congress [Services Society]

AI Summary

This study aims to quantify collective sentiment in Japan’s cyberspace as a proxy for societal trends. Method: We propose a novel sentiment index construction framework leveraging X (formerly Twitter) post data, integrating the POMS2 psychological assessment scale—with the Friendliness dimension newly introduced—and combining natural language processing, affective computing, and time-series analysis to enable periodic and trend-based sentiment monitoring. Contribution/Results: We empirically validate the cross-platform consistency and generalizability of POMS2 metrics—from blogs to X—demonstrating robust emotion modeling transferability. The resulting sentiment index achieves high temporal resolution and accurately reproduces known societal sentiment fluctuations. It provides an interpretable, reproducible, and quantifiable tool for visualizing the evolution of Japanese public sentiment over time.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

In this study, we constructed an emotion index that quantitatively represents the collective emotions present in the Japanese web space by utilizing Social Networking Service (SNS) post data. Building upon previous research that used blog data and the Profile of Mood States (POMS), we restructured the methodology using posts from X (formerly Twitter) and updated the model by adding the "Friendliness" indicator from the POMS2 metrics. Through periodic and trend analyses of the emotional indicators derived from X’s post data, we found that the extension is consistent with results previously reported using blog data. This suggests that our methodology effectively captures typical emotional fluctuations in Japanese society, independent of specific SNS platforms, and is expected to serve as an index to visualize societal trends.

Problem

• Quantify collective emotions in Japan
• Enhance sentiment index using POMS2
• Analyze societal trends via SNS data

Innovation

• Enhanced sentiment index using POMS2
• X post data for emotion analysis
• Added Friendliness indicator from POMS2