Comparative Study of Covid-19 Tweets Sentiment Classification Methods

Abstract

Covid-19 is a disease caused by a virus and has become a pandemic in many countries around the world. The disease not only affects public health, but also affects other aspects of life. People tend to write comments about things happening during the pandemic on social media, one of which is Twitter. Sentiment analysis on Twitter data is not an easy task due to the characteristics of the tweeter text which is user generated content. Therefore, in this paper, a sentiment analysis study is carried out on Twitter data using three schemes, namely the vector space model (Bag of Words and TF-IDF) with Support Vector Machine, word embedding (word2vec and Glove) with Long Short-Term Memory, and BERT (Bidirectional Encoder Representations from Transformers). Based on the conducted experiments, BERT achieved the best performance compared to the other two schemes, reaching 0.85 (weighted F1-score) and 0.83 (macro F1-score) for the classification of three sentiment classes on Kaggle competition data (Coronavirus tweets NLP – Text Classification).