ML Model Comparison for Sentiment Analysis Across Diverse Datasets

Year : 2026 | Volume : 12 | Issue : 02 | Page : 26 33
    By

    Ruchika Gupta,

  • N.P. Singh,

  1. Research Scholar, School of Engineering & Technology, Modern Vidya Niketan University, Haryana, India
  2. Professor, School of Engineering & Technology, Modern Vidya Niketan University, Haryana, India

Abstract

Analyzing sentiment is crucial for understanding public opinion on various issues in marketing, politics, and social sciences. This study compares the performance of seven different machine learning algorithms for sentiment classification, focusing on their effectiveness, accuracy, and complexity. The research is conducted on a pre-processed dataset with balanced text samples, utilizing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF). The performance assessment criteria consist of accuracy, precision, recall, F1 score, and overall computational efficiency. Results indicate that Support Vector Machines (SVM) outperform other classifiers, followed by ensemble methods like Gradient Boosting and Random Forest. Recent studies have reinforced the effectiveness of deep learning models in sentiment classification, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which have demonstrated higher accuracy compared to traditional methods. Additionally, advancements in hybrid approaches, such as quantum-classical machine learning, have shown promising results, particularly after incorporating dimension reduction techniques. Moreover, recent research has highlighted challenges in deep learning-based sentiment analysis and emphasized future directions, including improvements in text representation and embedding techniques. This study also discusses the implications of model selection and suggests that future research should explore deep learning approaches and hybrid models.

Keywords: Sentiment analysis, machine learning, text classification, natural language processing, sentiment analysis

[This article belongs to Journal of Operating Systems Development & Trends ]

How to cite this article:
Ruchika Gupta, N.P. Singh. ML Model Comparison for Sentiment Analysis Across Diverse Datasets. Journal of Operating Systems Development & Trends. 2025; 12(02):26-33.
How to cite this URL:
Ruchika Gupta, N.P. Singh. ML Model Comparison for Sentiment Analysis Across Diverse Datasets. Journal of Operating Systems Development & Trends. 2025; 12(02):26-33. Available from: https://journals.stmjournals.com/joosdt/article=2025/view=236536


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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 28/06/2025
Accepted 04/08/2025
Published 11/08/2025
Publication Time 44 Days
12

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