Performance Analysis of Naïve Bayes and Stochastic Gradient Descent-based SVM for Sentiment Analysis

International Journal of Computer Science and Engineering

© 2025 by SSRG - IJCSE Journal

Volume 12 Issue 5

Year of Publication : 2025

Authors : Shrushti Jeetendra Vadher

10.14445/23488387/IJCSE-V12I5P102

How to Cite?

Shrushti Jeetendra Vadher, "Performance Analysis of Naïve Bayes and Stochastic Gradient Descent-based SVM for Sentiment Analysis," SSRG International Journal of Computer Science and Engineering , vol. 12,  no. 5, pp. 10-18, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I5P102

Abstract:

This paper explores the effectiveness of sentiment classification on food review dataset, mainly focusing on Naïve bayes and Stochastic Gradient Descent-based Support Vector Machine (SGD-based SVM). The findings highlight the performance of both models and the impact of data preprocessing methods, as sentiment analysis is necessary for natural language processing and business customer services. The dataset obtained from Amazon on food review underwent preprocessing using Term Frequency-Inverse Document Frequency (TF-1DF) vectorization to transform textual data into numerical representations and Synthetic Minority Oversampling Technique to rectify class imbalances, ensuring fair and robust evaluation. The evaluation metrics demonstrate that SGD-based SVM has performed better than Naïve bayes with 84% and 79.9% accuracy, respectively. It can be observed that the SGD-based performs better as compared to the naïve bayes model.

Keywords:

Class imbalance, Naïve bayes, Natural language processing, Sentiment analysis, Support Vector Machine.

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