Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling
IEEE Transactions on Knowledge and Data Engineering
Data mining emotion in social network communication: Gender differences in MySpace
Journal of the American Society for Information Science and Technology
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Journal of the American Society for Information Science and Technology
SentiFul: A Lexicon for Sentiment Analysis
IEEE Transactions on Affective Computing
Tweet me home: exploring information use on twitter in crisis situations
OCSC'11 Proceedings of the 4th international conference on Online communities and social computing
A Cognitive Model of Improvisation in Emergency Management
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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An extreme event such as a natural disaster may cause social and economic damages. Human beings, whether individuals or society as a whole, often respond to the event with emotional reactions (e.g., sadness, anxiety and anger) as the event unfolds. These reactions are, to some extent, reflected in the contents of news articles and published reports. Thus, a systematic method for analyzing these contents would help us better understand human emotional reactions at a certain stage (or an episode) of the event, find out their underlying reasons, and most importantly, remedy the situations by way of planning and implementing effective relief responses (e.g., providing specific information concerning certain aspects of an event). This paper presents a clustering-based method for analyzing human emotional reactions during an event and detecting their corresponding episodes based on the co-occurrences of the words as used in the articles. We demonstrate this method by showing a case study on Japanese earthquake in 2011, revealing several distinct patterns with respect to the event episodes.