VADER: A Lightweight and Effective Approach for Sentiment Analysis

Sentiment analysis (SA) has become an essential field in text mining, aiming to computationally interpret subjective information such as opinions and emotions. This paper surveys recent advancements in SA, motivated by its growing applications across domains like politics, sports, and real-time event analysis. Key findings reveal that emerging techniques, such as transfer learning and emotion detection, have significantly improved SA’s adaptability and accuracy. Research survey organizes these studies by method and application type, with visual aids to enhance usability. This synthesis provides researchers with a comprehensive resource for understanding SA trends and supports future research directions to extend SA’s practical impact.