A Comprehensive Review of Sentiment Analysis: From Rule-Based Methods to Deep Learning and Future Directions
Abstract: The following paper provides a systematic review of Sentiment Analysis in Natural Language Processing from the preliminary rule of thumb to modern deep learning methods. The background, main milestones, and significant techniques are discussed: lexicon-based strategies, machine learning, and recent deep learning applications based on BERT and BERT-like models are described. The paper also identifies and elaborates on existing issues in Sentiment Analysis such as the handling of sarcastic remarks, sentiment that is context-sensitive, and problems related to some specific domains. We also look at the uses of Sentiment Analysis in various industries such as social networks analysis, protection of brand image, and stock market prediction. In addition, the paper also elucidates certain areas of contemporary development and the direction of the further development of sentiment analysis including multimodal sentiment analysis, explainable AI and integration of common-sense reasoning. This review summarizes the current state of the issues relating to Sentiment Analysis present and possible future developments in the field.
Keywords: Sentiment Analysis, NLP, Machine Learning, Deep Learning, BERT, Aspect- based Sentiment Analysis, Multimodal Sentiment Analysis
How to Cite?: N John Kuotsu, "A Comprehensive Review of Sentiment Analysis: From Rule-Based Methods to Deep Learning and Future Directions", Volume 13 Issue 11, November 2024, International Journal of Science and Research (IJSR), Pages: 367-371, https://www.ijsr.net/getabstract.php?paperid=SR241104194610, DOI: https://dx.doi.org/10.21275/SR241104194610