Sentiment Analysis of Product Reviews Using LSTM - A Comparative Evaluation with Machine Learning Algorithms Employing BOW and TF-IDF Techniques
Karthiga S
Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India.
Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India.
Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India.
Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Tiruchirappalli Campus, Tamil Nadu, India.
Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India.
Sentiment analysis has become an invaluable tool in understanding consumer opinions in large datasets. This study explores sentiment analysis of the product review dataset applying different machine learning classification algorithms, specifically focusing on two primary feature extraction methods: (TF-IDF) and (BOW) A thorough comparison was conducted to assess the effectiveness of each method alone, as well as a novel hybrid technique that merges both TF-IDF and BOW. And compared with deep learning approach, our findings demonstrate that feature extraction technique significantly enhances classification performance. Among the tested algorithms, logistic regression with tfidf, bow exhibited even greater accuracy. Obtaining the most accurate results possible from the sentiment analysis is the primary objective of this endeavor. The first step in the process of analyzing and classifying the data is going to be the preprocessing of the data, followed by the extraction of features, then the categorization of sentiments via the use of machine learning algorithms, and lastly the assessment of the algorithms. The end findings indicate that the SVM classifier obtained an accuracy of 93%, the Naive Bayes classifier achieved an accuracy of 91%, the Logistic regression classifier got an accuracy of 94%, and the LSTM classifier earned an accuracy which was 93.58%. In future work may explore the integration of additional feature extraction methods with deep learning to refine and improve sentiment analysis models.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Karthiga S, Sutha K, Pavithra V, Sakthivel S, Sowmya V and Sasidevi J;
Data Curation: Karthiga S, Sutha K and Pavithra V;
Writing- Original Draft Preparation: Karthiga S, Sutha K, Pavithra V, Sakthivel S, Sowmya V and Sasidevi J;
Visualization: Karthiga S, Sutha K and Pavithra V;
Investigation: Karthiga S, Sutha K, Pavithra V, Sakthivel S, Sowmya V and Sasidevi J;
Supervision: Sakthivel S, Sowmya V and Sasidevi J;
Validation: Karthiga S, Sutha K and Pavithra V;
Writing- Reviewing and Editing:
Karthiga S, Sutha K, Pavithra V, Sakthivel S, Sowmya V and Sasidevi J; All authors reviewed the results and approved the final version of the manuscript.
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Pavithra V
Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India.
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Cite this article
Karthiga S, Sutha K, Pavithra V, Sakthivel S, Sowmya V and Sasidevi J, “Sentiment Analysis of Product Reviews Using LSTM - A Comparative Evaluation with Machine Learning Algorithms Employing BOW and TF-IDF Techniques”, Journal of Machine and Computing, vol.5, no.3, pp. 1606-1614, July 2025, doi: 10.53759/7669/jmc202505127.
