Optimizing User-Content Creator Engagement via Semantic Comment Clustering, Automated Chatbots, and Sentiment Analysis
Sahil Sharma, Mayur Waghmare, Ankita Darade, Nikhil Malusare, JaiK Mahajan · 2025
In the rapidly evolving digital landscape, social media platforms play a critical role in fostering interactions between users and content creators. However, the sheer volume of user comments presents challenges in identifying common concerns, providing timely responses, and understanding audience sentiment. This study examines three key technologies—comment clustering, chatbots for FAQs, and sentiment analysis—as tools to enhance the efficiency and quality of social media interactions. The study explores various clustering algorithms that group semantically related comments, enabling content creators to streamline their responses to recurring queries. Additionally, we review chatbot systems designed to automate responses to frequently posed questions, reducing response times and improving user satisfaction. Furthermore, sentiment analysis techniques are assessed for their effectiveness in categorizing user feedback as positive, negative, or neutral, providing creators with actionable insights into audience sentiment. By studying these approaches, this paper demonstrates how the integration of clustering, chatbots, and sentiment analysis can transform social media communication, making it more efficient, responsive, and personalized. This integrated approach offers significant benefits for both users and creators by improving engagement and fostering meaningful dialogue.