Numerous studies have been conducted to extract relationships from different documents. However, extracting relationships from microblog posts is rarely studied. In this paper, we improve a novel kernel-based learning algorithm to mine the personae social relationships from microblog posts by combining the syntax and semantic meanings of the dependency trigram kernels (DTK). To deeply extract the personal social relationships of microblog posts, we define the relation feature words, provide seven rules for extracting these feature words, and propose a rule-based approach that mines these relation feature words from microblog posts. We construct relation feature word dictionaries for different relation types because of the lack of prominent relation features in microblog posts. We propose an algorithm to classify relation feature words by considering two features of the relation feature words, namely, syntax and semantic similarities between relation feature words in microblog posts and by using relation feature word dictionaries. Experimental results show that the average recall, precision, and F-measure of our proposed approach outperforms the original DTK in sentence selection, personae social relation extraction, and personae social relation classification. Finally, the relation graphs of five topics clarify that our proposed approach is effective for extracting personae social relations from microblog posts.
With the increasing popularity of online social media platforms, netizens always chat with their friends and share information, such as what they like in their daily lives, on these platforms. Netizens publish tons of information on social platforms every day. These platforms converge many people and information. The processes by which the publishers find the sharers who are interested in their publications and the sharers find some interesting things and information in what the publishers published have resulted in the challenge of retrieving information from social network fields. To address these issues, we propose a novel algorithm, named Hot Persona Mining, to analyze the users' focus personae from microblog posts in the online social networks. During mining, we first utilize local-based graph clustering to establish the nearest neighbor nodes of target users. Then, we mine users' focused personae entities from their neighbors' published microblog posts in different periods. Then, we construct the users' active score vector and their interest matrix to mine the hot personae in every local social graph. The experimental results show that our algorithm effectively mines current focus of the target user, and exhibits good performance as shown by its precision, recall and F-measures.
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