Abstract
Traditional methods for identifying communities in networks are based on direct link structures, which ignore the content information shared among groups of entities. Recently, community detection approaches by using both link and content have been studied. It is necessary to identify communities with different sentiment distributions based on corresponding topics, which cannot be identified by existing community discovery techniques. To directly detect the sentiment-topic level communities and to better explore the hidden knowledge within them, we propose to integrate social links, content/topics, and sentiment information to work out a novel community model. Experimental results on two types of real-world datasets demonstrate that our model can not only achieve comparable performance compared with a state-of-the-art community model, but also can identify communities with different topic-sentiment distributions.
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Notes
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Note that we will use Enron to represent EnronFourUsrs in the following sections.
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Yang, B., Manandhar, S. (2014). STC: A Joint Sentiment-Topic Model for Community Identification. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_48
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DOI: https://doi.org/10.1007/978-3-319-13186-3_48
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