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Sentiment Analysis and Trend Prediction in Social Media

In: Proceedings of the 8th International Conference on Corporate Social Responsibility and Sustainable Development

Author

Listed:
  • Bandanjot Kaur

    (Chandigarh University)

  • Divyansh Sandhu

    (Chandigarh University)

  • Sameer Sardana

    (Chandigarh University)

  • Devkinandan Garg

    (Chandigarh University)

  • Tauheed Ansari

    (Chandigarh University)

Abstract

This chapter presents research on extracting, analyzing, and predicting user sentiments from various social media platforms with the help of Natural Language Processing (NLP) techniques. The project seeks to analyze public opinion by understanding how people feel about the world and predict future developments within fields such as politics, entertainment, and consumer trends. Event Sentiment Analysis from Data Mining based on Natural Language Processing involves the preprocessing, analysis, and classification of textual data extracted from social media sites using sophisticated NLP techniques. Some of these are tokenization, categorization of sentiment, polarity measurement, and elimination of stop words. For drawing graphs of sentiment distributions and correlations between keywords; data visualization libraries like Matplotlib, Seaborn, and Plotly are used to show more clear, elegant, and insightful representation of actual data. Furthermore, the predictive part employs machine learning algorithms and time-series analysis to identify trends and make accurate predictions and spot patterns based on past and present data.

Suggested Citation

  • Bandanjot Kaur & Divyansh Sandhu & Sameer Sardana & Devkinandan Garg & Tauheed Ansari, 2026. "Sentiment Analysis and Trend Prediction in Social Media," Springer Proceedings in Business and Economics, in: Vikas Kumar & Tuan Hung Vu & Pooja Nanda & Suddin Lada (ed.), Proceedings of the 8th International Conference on Corporate Social Responsibility and Sustainable Development, pages 719-731, Springer.
    • Handle: RePEc:spr:prbchp:978-981-95-4200-0_43
    DOI: 10.1007/978-981-95-4200-0_43
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