A Review of Sentiment Analysis In Machine Learning for Stock Prediction on Twitter and Stocktwits
Abstract
The increasing influence of online platforms, particularly social media, has propelled Sentiment Analysis or Opinion Mining to prominence. This paper explores sentiment analysis concerning stock prediction on microblogging platforms such as Twitter and Stocktwits. With the surge in internet users and the growing impact of online review sites, sentiment analysis has become a pivotal tool for gauging public sentiment toward stocks. Sentiment Analysis in Machine Learning for Stock Prediction on Twitter and Stocktwits critically examines the applicability of sentiment mining to financial markets through social media. The paper conducts a comprehensive review of existing sentiment analysis techniques, with a specific focus on feature extraction methodologies. The objective is to identify research gaps, highlighting both well-explored and less-addressed areas in this domain. The research methodology involves an extensive study of scholarly works, emphasizing the evolving landscape of feature extraction. The anticipated outcome aims to uncover insights into feature selection techniques, providing valuable input for future research directions. This study consolidates knowledge, offering researchers a roadmap to advance sentiment analysis in the context of stock prediction on Twitter and Stocktwits.
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