Extracting Deep Personae Social Relations in Microblog Posts

Abstract

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.