Sentiment Analysis Using Twitter Data in the Context of Natural Calamity

Abstract

This study is intended to take advantage of the data from social media to get some insights on the situations generated during a natural calamity. Specifically, the case in point is on Twitter during November–December 2015 south Indian floods resulted from heavy rainfall triggered by the annual northeast monsoon in Chennai, the metropolitan. In all, about 2,500 tweets were examined for the sentiment analysis. The approaches used in this work are (i) coarse-grained classification of sentiments into positive and negative as well as fine grained classification of negative sentiments, (ii) finding temporal distribution of sentiments during and after the tenor of calamity and (iii) finding the significance of conjoint expression of different sentiments in single tweets through measure of interestingness. The results of our analysis showed that negative sentiments will be predominant during the incident and a few days after the incident and gradual subsidence with an increase in positive sentiments as an outcome of good relief work, voluntary services and philanthropic support. We also found that among the combination of two sentiments in a single tweet, sadness and disgust combination was more prevalent followed by sadness and anger.