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2015 | OriginalPaper | Buchkapitel

Improving Link Prediction in Social Networks by User Comments and Sentiment Lexicon

verfasst von : Feng Liu, Bingquan Liu, Chengjie Sun, Ming Liu, Xiaolong Wang

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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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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Titel: Improving Link Prediction in Social Networks by User Comments and Sentiment Lexicon
verfasst von: Feng Liu
Bingquan Liu
Chengjie Sun
Ming Liu
Xiaolong Wang
Verlag: Springer International Publishing
Buch: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
Print ISBN: 978-3-319-25815-7

Electronic ISBN: 978-3-319-25816-4

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-25816-4_29

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