Abstract
The growing popularity of social networks and microblogging services has gradually increased the demand for personalized applications. The microblogging services such as Twitter has a powerful forum for users to share their personal interest and opinions. Mining and analyzing user's interests is a crucial factor in buying decisions and tracking the emotions of the public about their items, business, etc. Although, Twitter has a broad range of topics in real-time, it poses significant challenges because of the unstructured short text. In this paper, the best model for finding the user's topics of interest is being investigated by building the profile of individual users based on their tweets. A hybrid Topic-based model is proposed that combines both two unsupervised learning algorithms with sentiment consideration and user features. Thus, we show that the proposed hybrid model has a higher performance in the topic extraction of user's interests on Social Networks.
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