Analyzing the Performance of Sentiment Analysis using BERT, DistilBERT, and RoBERTa

Sentiment analysis or opinion mining is a natural language processing (NLP) technique to identify, extract, and quantify the emotional tone behind a body of text. It helps to capture public opinion and user interests on various topics based on comments on social events, product reviews, film reviews, etc. Linear Regression, Support Vector Machines, Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM (Long Short Term Memory), and other machine learning and deep learning algorithms can be used to analyze the sentiment behind a text. This work analyses the sentiments behind movie reviews and tweets using the Coronavirus tweets NLP dataset and Sentiment140 dataset. Three advanced transformer-based deep learning models like BERT, DistilBERT, and RoBERTa are experimented with to perform the sentiment analysis. Finally, the performance obtained using these models on these two different datasets is compared using the accuracy as the performance evaluation matrix. On analyzing the performance, it can be seen that the BERT model outperforms the other two models.