Influential user weighted sentiment analysis on topic based microblogging community

Nowadays, social microblogging services have become a popular expression platform of what people think. People use these platforms to produce content on different topics from finance, politics and sports to sociological fields in real-time. With the proliferation of social microblogging sites, the massive amount of opinion texts have become available in digital forms, thus enabling research on sentiment analysis to both deepen and broaden in different sociological fields. Previous sentiment analysis research on microblogging services generally focused on text as the unique source of information, and did not consider the social microblogging service network information. Inspired by the social network analysis research and sentiment analysis studies, we find that people's trust in a community have an important place in determining the community's sentiment polarity about a topic. When studies in the literature are examined, it is seen that trusted users in a community are actually influential users. Hence, we propose a novel sentiment analysis approach that takes into account the social network information as well. We concentrate on the effect of influential users on the sentiment polarity of a topic based microblogging community. Our approach extends the classical sentiment analysis methods, which only consider text content, by adding a novel PageRank-based influential user finding algorithm. We have carried out a comprehensive empirical study of two real-world Twitter datasets to analyze the correlation between the mood of the financial social community and the behavior of the stock exchange of Turkey, namely BIST100, using Pearson correlation coefficient method. Experimental results validate our assumptions and show that the proposed sentiment analysis method is more effective in finding topic based microblogging community's sentiment polarity. (C) 2017 Elsevier Ltd. All rights reserved.