Sentiment Classification of COVID-19 Tweets using BERT
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Abstract
The COVID-19 has instigated anxiety with loss of lives. Sentiment analysis is a technique to find out individual’s emotion by investigation on social media platforms. This could have been avoided if the spread was noticed in the early stages of the pandemic. In this paper, a methodology is proposed to carry out multi-label classification of COVID-19 tweets using Bidirectional Encoder Representation from Transformer (BERT). The proposed work compares the accuracy of BERT models on the SenWave dataset. The outcomes are indicated by heatmap representation of tweets across labels. The results specify that the greater part of the tweets have been joking, empathetic, optimistic, and pessimistic during the COVID-19 period. The carried work examines the occurrence of Unigrams, Bi-grams, and sentiment labels during the pandemic period.
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