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Informative Article | Engineering Science | India | Volume 11 Issue 2, February 2022 | Rating: 5 / 10
A Comprehensive Survey of Sentiment Analysis Methods
Abstract: Sentiment analysis, is a process in natural language processing (NLP) which plays a crucial role in analyzing and interpreting the underlying emotions, attitudes, and opinions expressed within textual data. In this survey paper we aim to provide a comprehensive examination of sentiment analysis methods by categorizing them into distinct approaches and analyzing their key characteristics, strengths, and limitations. Our survey presents a wide variety of methodologies, including lexicon-based approaches, machine learning-based techniques, and hybrid models, and their unique advantages and challenges in sentiment classification tasks. Additionally, we also discuss the major challenges faced by sentiment analysis methods, such as handling sarcasm, contextual understanding, and domain adaptation, and highlight the importance of understanding the historical development of sentiment analysis for advancing the field. Furthermore, we analyze and identify gaps in the literature and discuss potential areas for future research including multimodal sentiment analysis, context-aware sentiment analysis, continuous learning and adaptation techniques, and ethical considerations in sentiment analysis. By discussing these challenges and the emerging research in the field of sentiment analysis we want to provide guidelines towards developing more accurate, robust, and ethically responsible sentiment analysis systems, with diverse applications across domains such as marketing, social media monitoring, and public opinion analysis.
Keywords: Sentiment Analysis, Natural Language Pro- cessing (NLP), Lexicon-based Approaches, Machine Learning, Context-aware Sentiment Analysis, Future Directions
Edition: Volume 11 Issue 2, February 2022,
Pages: 1318 - 1322