User-weighted sentiment analysis for financial community on Twitter

Abstract

Sentiment analysis is a popular research area in computer science. It aims to determine the attitude of a person with respect to some topic, such as his mood or opinion from textual documents generated by the person. With the proliferation of social micro-blogging sites, opinion text has become available in digital forms, thus enabling research on sentiment analysis to both deepen and broaden in different sociological fields, particularly in the finance field. In this paper, we propose a novel sentiment analysis method which added new user metrics to classical Naive Bayes based sentiment analysis method and applies it to the finance field. We also analyze the correlation between the mood of the financial community and the behavior of the stock exchange of Turkey, namely BIST 100 using Spearman's rank correlation coefficient (SRCC) method. Our empirical studies show that the proposed sentiment analysis method (SRCC value 0.5634) computes a moderate positive correlation between stock market behavior and the sentiment polarity of financial community.