Abstract: Customer sentiment analysis is a process of extensive exploration of data stored on the web in the form of online reviews to identify and categorize the views expressed in a part of the text as customer sentiments. Customer Sentiment analysis acquires importance in many areas of business, politics, and thought. Study of Sentiment analysis contains a comprehensive overview of the most important studies in this field from the past to the recent studies. The main aim of this paper is to provide a empirical analysis using sentiment analysis techniques and classification of customer reviews using machine learning (ML) techniques. Sentiment analysis has emerged as a pivotal tool in deciphering and understanding human emotions from textual data. This paper provides a succinct overview of customer sentiment analysis, its methodologies, applications, and significance in contemporary digital environments. At its core, sentiment analysis employs computational techniques to discern the sentiment or emotional tone expressed within text data. Techniques range from rule-based systems to ML algorithms, enabling automated classification of text into positive, negative, or neutral sentiments. Applications span various domains, including social media monitoring, customer feedback analysis, market research, and brand reputation management
Keywords: Opinion mining, Customer reviews, decision-making, ML algorithms, Sentiment analysis, Classification.
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DOI:
10.17148/IJARCCE.2024.13505
