Sentiment analysis from social media in crisis situations

Advent of social media has created an unprecedented environment for people to share their thoughts with the world. These online platforms like facebook, Twitter are usually the first resort people turn to in times of crisis to voice their opinions and relay other crucial information. But when it comes to detecting sentiments out of this gigantic pool of opinions, it becomes an arduous task and doing it manually is practically impossible. Hence different methods have been devised to perform automatic polarity classification of textual data. This paper provides a brief overview of different techniques being developed for analyzing social media data, particularly Twitter data. We developed a workflow for applying sentiment analysis to a comparatively new domain of natural disasters to detect public emotions in crisis. A base line model is designed and trained on unigram features using Naïve Bayes. The model is further tested on Kashmir floods dataset collected from Twitter and an overall accuracy of 67% is achieved. The result provides valuable information which will assist the authorities to strategize their actions with due consideration to public sentiments and hence ameliorate the process of managing such situations.