Sentilytics - Sentiment Analyzer
Sarvesh Yogesh Wagh1, Somnath Vinod Jadhav2, Ashish Kailas Shewale3, Mrs Nilima Gite4
1 Information Technology Department, K. K. Wagh Polytechnic, Nashik
2 Information Technology Department, K. K. Wagh Polytechnic, Nashik
3 Information Technology Department, K. K. Wagh Polytechnic, Nashik
⁴ Information Technology Department, K. K. Wagh Polytechnic, Nashik
Abstract - This project focuses on developing a smart and automated tool designed to analyze large volumes of comments in real time. The primary objective is to classify comments into sentiment categories such as positive, negative, or neutral, while also incorporating mechanisms to filter out spam and toxic content that may include offensive or abusive language. Unlike traditional sentiment analysis systems, this project integrates emoji mapping, where emojis are interpreted and mapped to their respective sentiment categories, resulting in a more precise and user-centric analysis of digital expressions. To enhance the interpretability of results, the system provides graphical visualizations such as pie charts, bar graphs, and word or emoji clouds. These visual tools allow users to quickly understand sentiment distribution, keyword frequency, and common emotional tones within a large dataset of comments. Moreover, the project includes YouTube integration, enabling real-time fetching and analysis of video comments directly from the platform. This functionality makes it especially relevant for content creators, marketers, and businesses who seek to monitor audience engagement and feedback continuously. The system has broad applicability across multiple domains. In social media monitoring, it helps track public opinion and trends. For product reviews, it assists businesses in evaluating customer satisfaction and areas of improvement. In educational settings, it can analyze feedback from students to improve learning experiences. Additionally, for content moderation, the tool helps platforms automatically detect and flag spam or toxic comments, creating safer digital environments. Overall, this project aims to deliver an efficient, scalable, and insightful sentiment analysis solution that goes beyond simple text classification. By combining comment analysis, spam detection, emoji sentiment mapping, and real- time visualization, it empowers users with actionable insights, ultimately improving decision-making and audience engagement
Key Words: Sentiment Analysis, Comment Analysis, Spam Detection, Toxic Comment Filtering, Emoji Mapping, Data Visualization, YouTube Comments, Feedback Analysis, Social Media Monitoring
