Twitter Sentiment Analysis Using Machine Learning Techniques

G. Anish Kumar and C Jayapratha

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 4, 01-04

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.

Keywords: sentiment analysis; Twitter; machine learning; natural language processing; text classification; social media analytics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i4:id:970

DOI: 10.32628/IJSRST251241

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