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Understanding Public Opinion through Sentiment Analysis: A Case Study Using the Sentiment140 Dataset

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  • Johnbenetic Gnanaprakasam
  • Jayasree Sivakumar

Abstract

The rise of social media has provided an unprecedented opportunity to analyze public opinion at scale. Sentiment analysis, a key technique in natural language processing (NLP), enables the automatic classification of opinions expressed in textual data. This study explores the effectiveness of sentiment analysis using the Sentiment140 dataset, a collection of tweets labeled as positive, negative, or neutral. We compare machine learning and deep learning models, including logistic regression, support vector machines (SVM), convolutional neural networks (CNN), and long short-term memory (LSTM) networks. Our results highlight the comparative strengths of each model in classifying sentiments with varying degrees of accuracy. This research contributes to the ongoing development of AI-powered sentiment analysis by evaluating model performance on real-world social media data.

Suggested Citation

  • Johnbenetic Gnanaprakasam & Jayasree Sivakumar, 2025. "Understanding Public Opinion through Sentiment Analysis: A Case Study Using the Sentiment140 Dataset," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(3), pages 307-313, June.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i3:id:1462
    DOI: 10.32628/CSEIT25112869
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112869
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