Stock Recommendations using Bio-Inspired Computations on Social Media - Swamiraj, Sophia Babu; Kannan, Rajkumar (Transactions on Engineering and Computing Sciences, 2017)
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.