Abstract :

In The Rapidly Evolving Digital Age, Social Media Platforms Generate An Immense Amount Of Data That Reflects Public Opinion, User Preferences, And Trending Topics. Understanding This Data Is Crucial For Organizations Aiming To Engage With Audiences And Respond Proactively To Sentiments. This Project Focuses On Decoding The Social Media Landscape By Applying Machine Learning Techniques To Analyze User Sentiments, Likes, And Retweets. The Methodology Involves Preprocessing The Raw Dataset, Transforming Text Data Using TF-IDF Vectorization, Encoding Sentiment Labels, And Employing Machine Learning Models To Classify And Predict Sentiment Trends. By Visualizing The Distribution Of Sentiments Across Platforms And Engagement Metrics Like Likes And Retweets, Insightful Conclusions Can Be Drawn About User Behavior And Platform Influence. To Enhance The Accuracy Of Sentiment Classification, The Project Implements Ensemble Machine Learning Algorithms Such As Gradient Boosting And Random Forest. The Models Are Trained And Tested On A Processed Dataset, And Their Performance Is Evaluated Based On Accuracy Scores And Visualized Through Comparison Graphs. The System Provides A Userfriendly Interface For Analyzing Trends, Training Models, And Comparing Algorithmic Performance. The Results Offer Valuable Insights Into The Dynamics Of User Sentiment And Content Engagement Across Platforms, Ultimately Contributing To More Strategic Decision-making In Digital Marketing And Public Engagement.

Published:

13-2-2026

Issue:

Vol. 26 No. 2 (2026)

Page Nos:

329-335

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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