Abstract
As a result of the transition from Web 2.0 to Web 3.0, people have adapted to being educated and socially connected with others while also becoming more empowered to deliver and obtain various services based on their individual thoughts and views. The natural language processing approach, known as sentiment analysis, uses the emotional tone behind the written text material and determines whether such implications are positive, negative, or neutral. It encompasses text extraction for sentiment and qualitative information using data mining, machine learning, and artificial intelligence. This research paper is a review of the literature about different machine learning techniques. It compares accuracy, benefits, and limitations of each machine learning method.
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Desai, T., Meva, D. (2023). Comparative Analysis of Different Machine Learning Approaches for Sentiment Analysis. In: Sharma, H., Shrivastava, V., Bharti, K.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2022. Lecture Notes in Networks and Systems, vol 686. Springer, Singapore. https://doi.org/10.1007/978-981-99-2100-3_15
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DOI: https://doi.org/10.1007/978-981-99-2100-3_15
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