Clustering Depressed and Anti-Depressed keywords Based on a Twitter Event of Srilanka Bomb Blasts using text mining methods

Twitter users' post data on social websites that are casual, critical, emotional, and sharing in real-time. Many keywords related to an event will appear as tweet hashtags during an event and immediately after the event. Twitter allows a length of 140 characters as a hashtag keyword. Algorithms exist for event detection using several scientific methods and express the importance of the event and its features. Many of the earlier studies clustered the events based on the tweets. In this paper, we considered tweets with the bombing, depressed, and anti-depressed related keywords posted from Srilanka during the ‘Bomb’ blasts in April 2019. Similar tweets data also collected and analyzed from a normal period (during May 2019) to compare our results. Our results show that the keywords identified are related to the event. We could further cluster these two keywords sets into similar and dissimilar sets with a Twitter event. We applied Learning Quotient and Text mining methods, and our results support the clustering of keywords.