A personality-aware followee recommendation model based on text semantics and sentiment analysis

Abstract

As the popularity of micro-blogging sites, followee recommendation plays an important role in information sharing over microblogging platforms. But as the popularity of microblogging sites increases, the difficulty of deciding who to follow also increases. The interests and emotions of users are often varied in their real lives. On the contrary, some other features of micro-blog are always unchangeable and they cannot describe the users characteristics very well. To solve this problem, we propose a personality-aware followee recommendation model (PSER) based on text semantics and sentiment analysis, a novel personality followee recommendation scheme over microblogging systems based on user attributes and the big-five personality model. It quantitatively analyses the effects of user personality in followee selection by combining personality traits with text semantics of micro-blogging and sentiment analysis of users. We conduct comprehensive experiments on a large-scale dataset collected from Sina Weibo, the most popular mircoblogging system in China. The results show that our scheme greatly outperforms existing schemes in terms of precision and an accurate appreciation of this model tied to a quantitative analysis of personality is crucial for potential followees selection, and thus, enhance recommendation.