Sentiment Analysis for Social Media Platform

The exponential growth of social media platforms has resulted in massive volumes of user- generated textual data, making manual sentiment interpretation increasingly inefficient and impractical. Sentiment analysis has emerged as a critical natural language processing (NLP) task for extracting meaningful insights from such unstructured content. This research proposes an automated, scalable, and platform-independent sentiment analysis framework designed for social media environments. The current implementation focuses on YouTube comment analysis, where the system collects user comments through the YouTube Data API, performs comprehensive text preprocessing, and applies the TextBlob-based sentiment classification model to categorize comments into positive, negative, and neutral sentiments. In addition to polarity detection, the system incorporates complaint pattern identification and AI-driven suggestion generation to provide actionable insights for content creators and analysts. An interactive visualization dashboard built using Chart.js presents statistical summaries and sentiment distributions to support data-driven decision-making. Experimental evaluation demonstrates that the proposed system efficiently processes large-scale comment datasets while maintaining reliable classification performance suitable for real-world applications. Unlike many existing solutions that are platform-specific, the proposed architecture is modular and extensible, enabling future integration with other social media platforms such as Twitter (X), Instagram, and Facebook. The system has potential applications in digital marketing, brand monitoring, educational feedback analysis, and social media analytics. Future work will focus on incorporating multilingual support, transformer-based deep learning models, real-time streaming analysis, and enhanced emotion detection capabilities. The proposed research contributes toward transforming raw social media feedback into structured business intelligence through an automated and scalable AI-driven approach.