Twitter Sentiment Analysis Using Machine Learning Techniques

Authors

  • G. Anish Kumar PG Scholar, Department of BDA, Karpaga Vinayaga College of Engineering and Technology, Chengalpattu, Tamil Nadu, India Author
  • Dr. C Jayapratha Professor, Department of CSE, Karpaga Vinayaga College of Engineering and Technology, Chengalpattu, Tamil Nadu, India Author

DOI:

https://doi.org/10.32628/IJSRST251241

Keywords:

sentiment analysis, Twitter, machine learning, natural language processing, text classification, social media analytics

Abstract

This paper presents an effective sentiment analysis system designed to classify the polarity of tweets into positive, negative, or neutral sentiments. The framework utilizes supervised machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), and Random Forest, trained on the Sentiment140 dataset. Text preprocessing techniques such as tokenization, stopword removal, stemming, and TF-IDF vectorization are applied to improve classification performance. The proposed system achieves an accuracy of 87.2% with SVM, outperforming other baseline models. This solution offers scalable deployment in social media monitoring, political campaign tracking, and customer feedback analysis.

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References

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Published

01-07-2025

Issue

Section

Research Articles