Revolutionizing Sentiment Analysis: Accelerated Data Science Approaches for Reddit Submissions

Social media platforms has been demonstrated as an effective method for gathering user feedback to support requirements engineering and software evolution. Social networking services such as Reddit and Facebook have achieved widespread global popularity as platforms where individuals can establish public profiles, engage with genuine friends, share their interests and viewpoints, and publish messages on many subjects. Every post is labeled with tags for the purpose of sifting. In the Reddit community, these specific tags are referred to as flairs. This article explores the application of sentiment analysis to Reddit comments, with a specific focus on comparing the speed of computational operations utilizing CPU and GPU-based methods. Data extraction from the Reddit platform was accomplished by utilizing web scraping techniques, with the assistance of the Python-based Reddit API Wrapper (PRAW). More precisely, the investigation focused on gathering submissions from the r/funny subreddit. A thorough assessment of the performance of well-established sentiment classification models, such as Random Forest [9], Logistic Regression [11], KNN [14], and SVM [14], was carried out. This study goes beyond traditional methods that rely on central processing units (CPUs) and instead investigates the possible advantages of using graphics processing units (GPUs) for computation. The study specifically emphasizes the benefits of using the Rapids framework for GPU computing. In addition, the research provides a comprehensive data visualization method using Tableau dashboards [5] to improve the clarity and understanding of the results. This work utilizes the extensive collection of user-generated content on Reddit to enhance sentiment analysis techniques in social media settings, providing significant insights for requirements engineering and software evolution processes.