Improving the Classification of Sentiments Towards Covid-19 Vaccines using Semantic Enrichment and Emojis

In this study we investigate different strategies for enriching tweets (now posts) to improve tweets classification accuracy. In particular, we investigate the effects of emoji-to-text translation and semantic enrichment using a locally trained word2vec model. Before doing the enrichment, we develop a baseline system where we investigate the effects of stopwords removal. In our investigation, we found out that stopwords removal degrades the classification accuracy of tweets. In the first approach, we compare the difference in the classification accuracies between tweets with stopwords and tweets without stopwords. In the second approach, we enrich tweets by translating emojis contained in tweets to their equivalent text. To test the accuracy of our approaches, we employed 3 classifiers, namely, BERT classifier, Random Forest and Multinomial Naïve Bayes. The third approach builds on the results of the first approach, and it uses a locally trained word2vec model to expand tweets. The results of our study suggest that semantic enrichment and translating emojis to their equivalent meaning increases the classification accuracy of tweets.