Opinion Mining for Comment Sentiment Analysis of Social Media
Srikanth Bhat K, Mallesh Sajjan B N, M. Raza, N. Hemanth Bhat, B. Naveen · 2024
The increasing influence of social media platforms has transformed how public opinion is shaped and shared. Our work introduces a sentiment analysis system capable of processing comments from major platforms (YouTube, Reddit and Instagram) to classify user sentiments as Positive, Negative or Neutral. The goal is to provide actionable insights into user emotions and trends, helping businesses and organizations improve decision-making in the fields like marketing, brand reputation management and public policy analysis. Our system allows users to input URLs from social media posts, automatically fetching and analyzing the comments. By utilizing Natural Language Processing (NLP) methods and Ensemble Learning Models, our system classifies sentiments to deliver meaningful insights in real-time. To increase classification accuracy, the ensemble learning approach integrates several models, such as Random Forest, Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. Our system's performance is assessed using parameters such as F1-score, accuracy, precision, and recall to ensure reliability in real-world applications. Through this user-friendly platform, organizations can better understand audience sentiment, enabling more informed, data-driven decisions for improved business outcomes.