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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, Floor: 3, Aqua 313


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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