Twitter Sentiment Analysis Using Machine Learning Techniques Article Swipe

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YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.32628/ijsrst251241

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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Computer Science

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Sentiment analysis Computer science Artificial intelligence Data science Machine learning

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Type: article
Language: en
Landing Page: https://doi.org/10.32628/ijsrst251241
PDF: https://www.ijsrst.com/index.php/home/article/download/IJSRST251241/IJSRST251241
OA Status: diamond
Related Works: 10
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DOI: https://doi.org/10.32628/ijsrst251241

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Title: Twitter Sentiment Analysis Using Machine Learning Techniques

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-07-01

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Authors: Gagan Kumar, C. Jayapratha

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Landing page: https://doi.org/10.32628/ijsrst251241

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PDF URL: https://www.ijsrst.com/index.php/home/article/download/IJSRST251241/IJSRST251241

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Open access: Yes

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OA status: diamond

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OA URL: https://www.ijsrst.com/index.php/home/article/download/IJSRST251241/IJSRST251241

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Concepts: Sentiment analysis, Computer science, Artificial intelligence, Data science, Machine learning

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Cited by: 0

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Related works (count): 10

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