Long Short-Term Memory Models

Conclusion

Sentiment analysis has been studied to harness the reviews, comments, and other written documents. The potential of sentiment analysis provided many benefits to various industries such as entertainment and e-commerce. This paper presents sentiment analysis models that utilize four ML techniques, i.e., LSTM, BoT, CNN, and transformer. Their performances in terms of time, loss, and accuracy are examined and compared. The BoT-based sentiment analysis model is faster than other ML models, whereas the transformer-based model performs poorly in terms of time. Furthermore, this study also demonstrates the accuracies of these models. The transformer-based sentiment analysis model achieved higher accuracy than other ML models.

This study indicates that ML techniques could be utilized successfully for sentiment analysis tasks. It is expected that this study will be helpful for both developers and researchers while deploying ML-based sentiment analysis algorithms into their projects.