Enhancing sentiment analysis for social media data - Xu, Qianqian (2017)
Social media platforms are now widely used by people to express their feeling and opinions on various current issues and popular topics. The study of sentiment analysis of social media data benefits in evaluating opinions and gaining insights for researchers and business decision makers. This project explores the enhancement techniques in machine-learning based sentiment analysis for tweets. Data pre-processing, negation handling, feature extractor, one-step and two-step classification processes are implemented in this project to enhance the performance of classifiers. The results indicate that different enhancement techniques can improve classification accuracy differently.