Identification of Opinion Leaders Based on User Clustering and Sentiment Analysis

Abstract

Opinion leaders play an important role in influencing topics of discussion among a group of persons. Hence, identification of opinion leaders has receive recent attention. Specifically, discovering opinion leaders in a Web-based stock message board might be valuable for many investors. Current methods for finding opinion leaders mainly concentrate on a graph of user connections, and thus leads to large amount of computation. On the other hand, opinions in user message are usually ignored so that the effectiveness in finding opinion leaders is very limited. In the paper, a new method is proposed to recognize opinion leaders in Web-based stock message boards. We combine clustering algorithm and sentiment analysis to address the two problems in current methods. Features of user activities are calculated based on messages posted on the board, then clustering algorithm is applied to the user data and generate clusters which contain potential opinion leaders. Next, we employ sentiment analysis to candidates and associate the sentiment with the actual price movement trend. By this means, opinion leaders can be well discovered since good ability in analyzing stock market is considered as skills of Influential users. Comparative experiments on a data set which contains real discussions and stock messages are conducted and the effectiveness of the proposed method is evaluated.