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Expert finding is important to the development of community question answering websites and e-learning. In this study, we propose a topic-sensitive probabilistic model to estimate the user authority ranking for each question, which is based on the link analysis technique and topical similarities between users and questions. Most of the existing approaches(More)
Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention in recent years. In this article, we propose an efficient algorithm and three pruning strategies to automatically build a word-level emotional dictionary for social emotion detection. In the dictionary, each word is associated with the(More)
The rapid development of social media services has facilitated the communication of opinions throughonlinenews, blogs,microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers’ emotions evoked by social media materials. Compared to the classical sentiment analysis from writers’ perspective, sentiment analysis of(More)
Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention. We propose an efficient method of automatically building the word-emotion mapping dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, three(More)
The rapid development of social media services has been a great boon for the communication of emotions through blogs, microblogs/tweets, instant-messaging tools, news portals, and so forth. This paper is concerned with the detection of emotions evoked in a reader by social media. Compared to classical sentiment analysis conducted from the writer's(More)
Social emotion classification is important for numerous applications, such as public opinion measurement, corporate reputation estimation, and customer preference analysis. However, topics that evoke a certain emotion in the general public are often context-sensitive, making it difficult to train a universal classifier for all collections. A multilabeled(More)
Several existing recommender algorithms combine collaborative filtering and social/trust networks together in order to overcome the problems caused by data scarcity and to produce more effective recommendations for users. In general, those methods fuse a user’s own taste and his trusted friends/users’ tastes using an ensemble model where a parameter is used(More)
With the development of Web 2.0, many users express their opinions online. This paper is concerned with the classification of social emotions on varied-scale data sets. Different from traditional models which weight training documents equally, the concept of emotional entropy is proposed to estimate the weight and tackle the issue of noisy documents. The(More)
In the era of big data, collaborative tagging (a.k.a. folksonomy) systems have proliferated as a consequence of the growth of Web 2.0 communities. Constructing user profiles from folksonomy systems is useful for many applications such as personalized search and recommender systems. The identification of latent user communities is one way to better(More)