Sentiment Analysis Review: Methods and Challenges

Sentiment analysis is an aspect of natural language processing (NLP) that focuses on locating opinions and emotions within text. It has diverse applications in e-commerce, healthcare, entertainment and politics. This paper describes the evolution of techniques of sentiment analysis, from lexicon-based approaches to modern deep learning and transformer-based models like BERT and GPT. It draws attention to the difficulties of nuanced sentiments such as sarcasm, domain adaptation and multilingual processing, especially in low-resource languages. The research also explores hybrid approaches that combine traditional methods with machine learning and deep learning to improve performances. It also deals with ethical issues like algorithmic bias and privacy. Through a comparative analysis, the paper evaluates the usefulness of different methodologies, illuminates unresolved problems and draws out possible future research directions such as multimodal integration and ethical AI frameworks. All these developments aim to increase the flexibility, fairness and contextual understanding of sentiment analysis and thus increase its potential applicability to the real world.