This article provides a primer on sentiment analysis, a technique for deducing the author's feelings or ideas from textual content. Several industries, including commerce, instruction, politics, medicine, and the arts, make use of sentiment analysis. Several methods for analyzing the emotional tone of online content are covered in this study, including those based on dictionaries, rules, and even machine learning. The limitations of sentiment analysis are also discussed, including the reliability of sentiment analysis findings and the potential for bias in sentiment analysis. The research highlights the significance of sentiment analysis in understanding public opinion and making smart choices across disciplines. The study also includes case studies of relevant efforts in sentiment analysis, focusing on data extraction and sentiment classification from Twitter. In order to effectively analyse massive amounts of text data, the article emphasizes the need of developing automated sentiment analysis methods.
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