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With the development of internet, users can express their attitudes towards public events on the social media. Monitoring and analyzing the public opinion can provide effective support for government's policy making. In this paper, a novel online public opinion (OPO) analysis platform over multi-source text streams is proposed. This OPO platform contains three layers: data collection layer, data process...
The rapid expansion of user data and geographic location data in the location-based social networking applications, it is become increasingly difficult for users to quickly and accurately find the information they need. The characteristics of the traditional friend recommendation algorithm are analyzed and discussed in this paper. In order to improve the performance of friend recommendation, we proposed...
Social network community detection has occupied an important place in many scientific fields like biology, sociology, or computer science. This problem still attracted a lot of work. The challenge is how to identify inside these networks, groups of persons strongly linked and sharing the same preferences. As in the literature, there are many works trying to detect communities we tried in this paper...
In location-based social networks, the current friend recommendation algorithms just take a relatively single factor into account without comprehensive evaluations. To solve this problem, we design a framework - Multiple Heterogeneous Social Network (MHSN) according to users' profiles, check-in records and interests. Based on this framework, we propose a friend recommendation model which consider...
In view of the problems existing in traditional recommendation algorithm of low accuracy and low efficiency, this paper presents a machine learning based social media recommendation algorithm. The algorithm is based on the traditional personalized collaborative filtering algorithm, and combines with the correlation characteristics among users in a social network. Besides, the algorithm also considers...
Current collaborative tagging systems do not allow community members to easily view available data related to their communities. In this paper, we present our study on community formation centered around common interests, utilizing data from del.icio.us. We propose an approach to clustering tags and users based on their similarity. We also report some practical results related to implicit grouping...
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