Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms

• Published in: Journal of intelligent & fuzzy systems Vol. 40; no. 4; pp. 6375 - 6383
• Main Authors: Ajitha, P., Sivasangari, A., Immanuel Rajkumar, R., Poonguzhali, S.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2021; Sage Publications Ltd
• Subjects: Algorithms; Customer satisfaction; Customer services; Data mining; Feature extraction; Machine learning; Natural language processing; Sentiment analysis; Sentiment analysis; naive bayesian algorithm; natural language processing; lexicon method
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-189478

Text Sentiment Analysis is a system where text feeling polarity is positive or negative or neutral from a series of texts or documents or public opinions on a particular product or general subject. Using machine learning and natural language processing techniques, the current work aims to gain insight into sentiment mining on tweets. Text classification is accomplished using Machine Learning Algorithm-based fusion technique. This research suggested a system for grading feelings based on a lexicon. Bag-of-words (BOW) or lexicon-based methodology is currently the main standard way of modeling text for machine learning in sentiment analysis approaches. Marketers can use sentiment analysis to analyze their business and services, public opinion, or to evaluate customer satisfaction. Organizations can even use this analysis to gather significant feedback on issues related to newly released products. The main objective of this is to resolve the data overload problem.