Identifying Situational Information during Mass Emergency

ABSTRACT

In the advent of Natural Language Processing, what finds itself in much use is analysis. This research paper finds itself in reference to the same that enables it in analysing sentiments of a text. The tasks that were covered in working with NLP includes – firstly, differentiating tweets on the basis of claims and facts, and secondly to create an effective classifier that finds out if a tweet is anti-covid vaccine, pro-covid vaccine or neutral. The beauty of our paper resides in the fact, that we have hit high end accuracies without using hefty algorithms, namely 93% for the first task using Random Forest and 45.4% for the second task using BERT’s Algorithm. Our accuracies are the best among all the teams working on the same tasks, which deepens the effect that this paper resonates. The details of the IRMiDis 2021 data challenge have been discussed elaborately here, and we hope our paper marks its significance by virtue of its own merit. © 2021 Copyright for this paper by its authors.