Finding Influential Twitter Users for the Stock Market Optimized by Genetic Algorithm
Abstract Twitter has been used increasingly by the scientific community as an information source with the purpose of predicting the stock market. This work aims to take another step into this emerging area. Everyday, thousands of users, with different features, comment on Twitter regarding several companies. These comments can influence the behavior of the people who read them relatively to the transaction of shares. This work purposes the use of a Genetic Algorithm to select a set of ideal features belonging to a user capable of influencing the rise and fall of the share values. To meet this goal, techniques to identify the potential influent Twitter users were created, a methodology was developed for filtering the tweets related to the stock market and an analyzer of tweets' sentiment was implemented. The final results show that the most influent Twitter users are those that are more active in this social network, posting a high number of tweets and having a lot of new followers. The total number of tweets and the ratio between followers and friends are important characteristics as well. At last, two case studies were made with the goal of assessing if the Genetic Algorithm was efficient. The results are very promising since in one of the case studies the Profit did not depend on the behavior of the market which is a very appealing quality for any investor.