Sentiment Analysis of Amazon Product Reviews using VADER and RoBERTa Models

Natural language processing's rapidly expanding field of sentiment analysis seeks to extract subjective information from text data. This article offers a thorough overview of the most advanced lexicon-based, machine learning-based, and hybrid sentiment analysis methodologies. The study discusses the challenges and limitations of sentiment analysis, such as the lack of labelled data, the ambiguity of language, and ethical considerations. The study also presents a critical analysis of the current applications of sentiment analysis in various domains, including marketing, customer service, social media, healthcare, and politics. Furthermore, the research work highlights future directions and opportunities for research in the field, such as the development of more accurate sentiment analysis techniques, the integration of multimodal data, and the exploration of ethical and privacy implications. This study offers a thorough grasp of sentiment analysis, including its current applications, difficulties, and potential future research areas.