A Comprehensive Review on Sentiment Analysis in Machine Learning

In today's exponentially expanding online channel of communications, opinions expressed through text, speech, and social media are deemed a rich source of information. Sentiment Analysis (SA) involves machine learning methods used to categorize, on an automatic basis, sentiment expressed in a piece of content as either positive, negative, or neutral. It has various applications in all fields of life from product reviews to political campaigns, including e-commerce, social media monitoring, healthcare, and finance; their significance and application are immense. This survey paper concentrates on tracing the development of approaches in sentiment analysis-from rule-based to deep learning and transformer-based approaches. We also consider primary challenges such as sarcasm, multilingualism, and bias while looking at practical implementations in application areas like business, health, and social media monitoring. Lastly, we go through the newly emerging world of large language models (LLM), explainable AI, and multimodal sentiment detection that are beginning to pave the way for next-generation sentiment analysis systems while simultaneously addressing stubborn issues related to data annotation and computational efficiency.