We care: A User-Focused Sentiment Analysis Framework for Social Media Interactions

Abstract

This paper addresses the sentiment analysis of WhatsApp chats, focusing on the challenges and opportunities presented by conversational data. The introduction outlines the prevalence of sentiment analysis in social media platforms and the significance of accurate sentiment categorization. The literature review highlights previous work in sentiment analysis, emphasizing methods for categorizing sentiment polarity and feature selection. The proposed system introduces a comprehensive approach to sentiment analysis tailored to WhatsApp chats, considering the unique characteristics of conversational data. Key features include user-centric inclusion, nuanced extraction of sentiment, context-aware analysis, and effective handling of conversational nuances. The method section details the proposed system's emphasis on real-time sentiment tracking, user-specific sentiment scores, and integration with WhatsApp's API for broader applications. The results section summarizes the positive outcomes of the proposed method, including user-centric inclusion, nuanced extraction, context-aware illustration, and effective content handling. The future scope section discusses potential enhancements such as real-time sentiment alerts, multilingual support, detailed sentiment analysis, improved user feedback systems, personalized sentiment metrics, and collaboration capabilities. The conclusion highlights the significance of the "WeCare" sentiment analysis project in improving our understanding of digital conversations, particularly within the context of WhatsApp chats. The project's commitment to user-centric design, adaptability, and ethical considerations positions it as a dynamic tool for enhancing digital interactions and well-being. Finally, acknowledgments recognize the contributions of the development team and academic mentor.