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A Comparative Study on Machine Learning Algorithms Used for Sentiment Analysis
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Author(s): Anuja Kotnala (University of Petroleum and Energy Studies, India), Dhakshinesh Anandh (University of Petroleum and Energy Studies, India), Saurabh Rawat (Graphic Era University, India), Anushree Sah (University of Petroleum and Energy Studies, India)and Abhirup Khanna (University of Petroleum and Energy Studies, India)
Copyright: 2025
Pages: 12
Source title:
Demystifying Emotion AI, Robotics AI, and Sentiment Analysis in Customer Relationship Management
Source Author(s)/Editor(s): Fazla Rabby (Stanford Institute of Management and Technology, Australia), Nasim Ahmed (The University of Sydney, Australia), Amandeep Sehmi (Canterbury Institute of Management, Australia), Rohit Bansal (Rockford College, Sydney, Australia)and Nishita Pruthi (Asian School of Business, India)
DOI: 10.4018/979-8-3373-1867-7.ch009
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Abstract
Customer Relationship Management (CRM) is a set of practices that businesses use to man- age customer interactions and customers' perception towards their products. The aim is to maximize profits by achieving customer satisfaction. Analyzing online reviews provides insights into customer satisfaction, which is pivotal for refining CRM strategies. The main factors of customer satisfaction include the quality of product and the service offered by it, which can be measured using sentiment analysis. Sentiment analysis has become remark- ably popular recently because of its wide range of applications in market capturing and improving customer experiences in various industries, such as e-commerce, restaurants, and products. The main aim of this research is to identify the class of sentiment of product reviews using a range of machine-learning algorithms. A few algorithms that can be used are SVM, Naive Bayes and LDA. Using this integrated algorithm model, the sentiment of recent product reviews can be predicted.
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