Top

Published in:

2015 | OriginalPaper | Chapter

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

Authors : Feng Liu, Bingquan Liu, Chengjie Sun, Ming Liu, Xiaolong Wang

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

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Clustering Chinese Product Features with Multilevel Similarity

next chapter Finite-to-Infinite N-Best POMDP for Spoken Dialogue Management

Hasan, Al: M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: SDM 2006: Workshop on Link Analysis. Counter-terrorism and Security, pp. 1–10. SIAM, Philadelphia, PA, USA (2006)

Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Effects of user similarity in social media. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 703–712. ACM (2012)

Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)

Becker, L., Erhart, G., Skiba, D., Matula, V.: Avaya: Sentiment analysis on twitter with self-training and polarity lexicon expansion. In: SemEval, p. 333, Atlanta, Georgia, USA (2013)

Brzozowski, M.J., Hogg, T., Szabo, G.: Friends and foes: ideological social networking. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 817–820. ACM (2008)

Chalothom, T., Ellman, J.: Simple approaches of sentiment analysis via ensemble learning. In: Kim, K.J. (ed.) ISA. LNEE, vol. 339, pp. 631–639. Springer, Heidelberg (2015)CrossRef

Chelmis, C., Prasanna, V.K.: Social link prediction in online social tagging systems. ACM Trans. Inf. Syst. (TOIS) 31(4), 20–46 (2013)CrossRef

Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)MATH

Freno, A., Garriga, G., Keller, M.: Learning to recommend links using graph structure and node content. In: Neural Information Processing Systems Workshop on Choice Models and Preference Learning, NIPS, pp. 1–7 (2011)

10.

Getoor, L., Diehl, C.P.: Link mining: a survey. ACM SIGKDD Explor. Newsl. 7(2), 3–12 (2005)CrossRef

11.

Guerini, M., Gatti, L., Turchi, M.: Sentiment analysis: how to derive prior polarities from sentiwordnet. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1259–1269. Association for Computational Linguistics (2013)

12.

Kunegis, J., Lommatzsch, A., Bauckhage, C.: The slashdot zoo: mining a social network with negative edges. In: Proceedings of the 18th International Conference on World Wide Web, pp. 741–750. ACM (2009)

13.

Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 641–650. ACM (2010)

14.

Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370. ACM (2010)

15.

Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRef

16.

Liu, B.: Sentiment analysis and subjectivity. Handb. Nat. Lang. Process. 2, 627–666 (2010)

17.

Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C.C., Zhai, C.X. (eds.) Mining Text Data, pp. 415–463. Springer, New York (2012)CrossRef

18.

Liu, F., Liu, B., Sun, C., Liu, M., Wang, X.: Deep learning approaches for link prediction in social network services. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part II. LNCS, vol. 8227, pp. 425–432. Springer, Heidelberg (2013) CrossRef

19.

Liu, F., Liu, B., Sun, C., Liu, M., Wang, X.: Deep belief network-based approaches for link prediction in signed social networks. Entropy 17(4), 2140–2169 (2015)CrossRef

20.

Liu, F., Liu, B., Wang, X., Liu, M., Wang, B.: Features for link prediction in social networks: a comprehensive study. In: 2012 IEEE International Conference on. Systems, Man, and Cybernetics (SMC), pp. 1706–1711. IEEE (2012)

21.

Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A: Stat. Mech. Appl. 390(6), 1150–1170 (2011)CrossRef

22.

Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. pp. 115–124. Association for Computational Linguistics (2005)

23.

Symeonidis, P., Tiakas, E.: Transitive node similarity: predicting and recommending links in signed social networks. World Wide Web 17(4), 743–776 (2014)CrossRef

24.

Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58, 1–38 (2014)

25.

West, R., Paskov, S.H., Leskovec, J., Potts, C.: Exploiting social network structure for person-to-person sentiment analysis. Trans. Assoc. Comput. Linguist. 2(1), 297–310 (2014). http://aclweb.org/anthology/Q14-1024CrossRef

Title: Improving Link Prediction in Social Networks by User Comments and Sentiment Lexicon
Authors: Feng Liu
Bingquan Liu
Chengjie Sun
Ming Liu
Xiaolong Wang
Publisher: Springer International Publishing
Book: 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 Year: 2015
DOI: https://doi.org/10.1007/978-3-319-25816-4_29

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner