Improving Sentiment Analysis Accuracy: A Comparative Study of Machine Learning Approaches |
Author(s): |
| Hari Shankar , Goel Institute of Technology and Management; Ankit Maurya, Goel Institute of Technology and Management; Dinesh Kumar Gupta, Goel Institute of Technology and Management; Mr. Dileep Kumar Gupta, Goel Institute of Technology and Management |
Keywords: |
| Sentiment Analysis, Machine Learning Approaches, TF-IDF Feature Extraction, Logistic Regression, Naive Bayes, E-commerce Reviews, Restaurant Review Sentiment, Text Classification, Natural Language Processing (NLP), Model Performance Comparison |
Abstract |
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This study improves sentiment analysis accuracy from 70% to 88% by integrating e-commerce datasets (~10,000 reviews) with restaurant reviews, employing TF-IDF feature extraction, and adopting Logistic Regression over Gaussian Naive Bayes. This study details the methodologies, highlights key improvements, and discusses the implications for sentiment analysis applications in e-commerce and restaurant review domains. |
Other Details |
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Paper ID: IJSRDV13I30185 Published in: Volume : 13, Issue : 3 Publication Date: 01/06/2025 Page(s): 255-259 |
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