Sentiment Analysis and Trend Prediction in Social Media

Bandanjot Kaur (), Divyansh Sandhu (), Sameer Sardana (), Devkinandan Garg () and Tauheed Ansari ()
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Bandanjot Kaur: Chandigarh University
Divyansh Sandhu: Chandigarh University
Sameer Sardana: Chandigarh University
Devkinandan Garg: Chandigarh University
Tauheed Ansari: Chandigarh University

A chapter in Proceedings of the 8th International Conference on Corporate Social Responsibility and Sustainable Development, 2026, pp 719-731 from Springer

Abstract: 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.

Keywords: Dataset; Visualization; Sentiment; Trend; Classification; Lexicon (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-981-95-4200-0_43

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