Abstract
In some online Social Network Services, users are allowed to label their relationship with others, which can be represented as links with signed values. The link prediction problem is to estimate the values of unknown links by the information from the social network. A lot of similarity based metrics and machine learning based methods are proposed. Most of these methods are based on the network topological and node states. In this paper, by considering the information from user comment and sentiment lexicon, our methods improved the performances of link prediction for both similarity based metrics and machine learning based methods.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (61272383 and 61300114), Specialized Research Fund for the Doctoral Program of Higher Education (No. 20132302120047), the Special Financial Grant from the China Postdoctoral Science Foundation (No. 2014T70340), China Postdoctoral Science Foundation (No. 2013M530156), and Natural Science Foundation of Heilongjiang Province(F201132).
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Liu, F., Liu, B., Sun, C., Liu, M., Wang, X. (2015). Improving Link Prediction in Social Networks by User Comments and Sentiment Lexicon. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_29
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