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

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.

[1]  Jeremy Ellman,et al.  Simple Approaches of Sentiment Analysis via Ensemble Learning , 2015 .

[2]  Feng Liu,et al.  Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks , 2015, Entropy.

[3]  Hui Chen,et al.  A literature survey on smart cities , 2015, Science China Information Sciences.

[4]  Feng Liu,et al.  Deep Learning Approaches for Link Prediction in Social Network Services , 2013, ICONIP.

[5]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[6]  Lei Zhang,et al.  A Survey of Opinion Mining and Sentiment Analysis , 2012, Mining Text Data.

[7]  Lise Getoor,et al.  Link mining: a survey , 2005, SKDD.

[8]  Tad Hogg,et al.  Friends and foes: ideological social networking , 2008, CHI.

[9]  Jure Leskovec,et al.  Signed networks in social media , 2010, CHI.

[10]  Panagiotis Symeonidis,et al.  Transitive node similarity: predicting and recommending links in signed social networks , 2014, World Wide Web.

[11]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[12]  Peng Wang,et al.  Link prediction in social networks: the state-of-the-art , 2014, Science China Information Sciences.

[13]  Lee Becker,et al.  AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon Expansion , 2013, *SEMEVAL.

[14]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[15]  Mohammad Al Hasan,et al.  Link prediction using supervised learning , 2006 .

[16]  Gemma C. Garriga,et al.  Learning to Recommend Links using Graph Structure and Node Content , 2011, NIPS 2011.

[17]  Marco Guerini,et al.  Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet , 2013, EMNLP.

[18]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[19]  Feng Liu,et al.  Features for link prediction in social networks: A comprehensive study , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[20]  Jure Leskovec,et al.  Exploiting Social Network Structure for Person-to-Person Sentiment Analysis , 2014, TACL.

[21]  Jure Leskovec,et al.  Effects of user similarity in social media , 2012, WSDM '12.

[22]  Viktor K. Prasanna,et al.  Social Link Prediction in Online Social Tagging Systems , 2013, TOIS.

[23]  Bo Pang,et al.  Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.

[24]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[25]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[26]  Christian Bauckhage,et al.  The slashdot zoo: mining a social network with negative edges , 2009, WWW.