Sentiment Analysis of Sub-Events Extracted Out of an Event Using Word2vec

Abstract

Word2vec is an assortment of related models specially employed to yield word embeddings. By its application to a relatively large dataset that corresponds to a given event coming about at a given point of time at a given location, we can break down the event into sub-events, and study them further. Investigating sub-events in the right direction can help us in countless ways. It can enable us to decipher their local yet inevitable impacts which might otherwise have gone missing in the sea of the whole event altogether. In our paper, we have broken down the event (of the happenings of 'Kashmir') into sub-events and pulled out a few randomly. We have then applied sentiment-analysis to each one of them instead of applying it on to the whole event all at once. The rise and fall of the sentiment with respect to each sub-event is plotted and the variation is visualised in the end. The procedure is not just limited to our domain of interest but can be adopted to study any event.