Sentiment Analysis of Train Derailment in India: A Case Study from Twitter Data

The services of Indian Railway are availed by many people in the country. It is an important mode of transportation. Most of the users of Indian Railway express their views about it on different social media sites like Twitter, Facebook etc. It leads to generation of large amount of data and sentimental analysis of that data can be very helpful in understanding public opinions towards Indian Railway and in decision making. In this paper, the lexicon based sentimental analysis technique has been applied to the twitter data collected corresponding to three train accidents namely Puri-Haridwar-Kalinga Utkal Express, Delhi-bound Kaifiyat Express and Mumbai-Nagpur Duranto Express which took place on 19/08/2017, 23/08/2017 and 29/08/2017 respectively. Further, tweets are classified into different categories and analyzed in terms of percentage frequency. The results present the pattern how the sentiments of the public fluctuate with time as when derailment happens the negative tweets has high frequency of occurrence but with passage of time frequency of occurrence of neutral tweets become high.