Sentiment Analysis Across Twitter, Instagram & Facebook

Abstract In today's digital landscape, social media has evolved into a powerful tool for communication, public engagement, and brand expression. With billions of users generating content every second, platforms like Twitter, Instagram, and Facebook serve as rich sources of user sentiment, opinion, and behavior. This research paper presents a sentiment-based comparative case study of these three platforms using a dataset of timestamped social media posts, enriched with metadata such as user, platform, hashtags, likes, retweets, and geographic location. The study categorizes each post by sentiment like positive, negative neutral or many more and visualizes trends across platform usage, country-wise engagement, and hashtag popularity. Through data analysis and visual representation, the research investigates key questions such as which platform is most dominant in user activity, which hashtags drive higher engagement, and which countries contribute the most liked or retweeted content. The analysis reveals distinct sentiment trends and engagement patterns specific to each platform. The study aims to provide data-driven insights that help understand user behavior and sentiment distribution in cross-platform environments, supporting more strategic decision-making in the domains of social media marketing, content strategy, and brand management. Acknowledgment I would like to express my heartfelt gratitude to Mrs. Elizabeth Leah George and Dr. Jyotshna Ma’am for their invaluable guidance, consistent support, and encouragement throughout the course of this research. Their depth of knowledge, critical feedback, and timely suggestions played a vital role in shaping the direction and quality of this paper. Their mentorship has been instrumental in helping me overcome challenges and maintain focus, and I am truly thankful for the opportunity to learn under their supervision.