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Sentiment Analysis of Twitter Data of a Crisis: Supervised Machine Learning Method

Sat, May 27, 8:00 to 9:15, Hilton San Diego Bayfront, 3, Aqua 313

Abstract

Organizational crises tend to get exacerbate due to the speedy and interactive nature of communication on social media such as Twitter. This study analyzed sentiments on Twitter during a crisis by using supervised machine learning method. This paper discussed the data processing and labeling procedures, and the results of LIWC2015 which was used as a benchmark. The full paper will discuss the development of code using Python, the results of the sentiment analysis, and the comparison between LIWC and a supervised machine learning method.

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