Abstract
The tremendous growth of the social networks has paved way for social interactions of investing communities about a company’s stock performance. Investors are able to share their comments on stocks using social media platforms. These interactions are captured and mined to produce advice on investing which helps retail investors to do prospective investments to increase profits. In this paper, we propose a novel stock recommendation methodology using ant colony optimization (ACO). This method extracts sentiments from the investor’s stock reviews and performs the sentiment analysis, which is optimized by the ACO. This method helps to find the correlation between sentiments and stock values, to make future stock predictions and to give stock recommendations to the retail investor.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Transactions on Machine Learning and Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
