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Enhance sentiment analysis on social networks with social influence analytics

  • Nadia ChouchaniEmail author
  • Mourad Abed
Original Research
  • 13 Downloads

Abstract

Sentiment analysis on social networks has attracted increasing research attention. Most previous works rely on text mining and the phenomenon of Homophily reflected by explicit friendship relations, which are a weak assumption for modeling sentiment and opinion similarities. In this paper we show that competitive results can be achieved with consideration of implicit influence relationships. In particular, we use heterogeneous graphs to infer sentiment polarities at user-level. We show that information about social influence processes can be used to improve sentiment analysis. Our transductive learning results reveal that incorporating such information can indeed lead to statistically significant sentiment classification improvements.

Keywords

Social networks Sentiment analysis Homophily Social influence 

Notes

References

  1. Agrawal R, Rajagopalan S, Srikant R, Xu Y (2003) Mining newsgroups using networks arising from social behavior. In: WWW 03: Proceedings of the 12th international conference on World Wide Web, ACM, New York, NY, USA, pp 529–535.  https://doi.org/10.1145/775152.775227
  2. Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: Huang CR, Jurafsky D (eds) COLING (Posters), Chinese Information Processing Society of China, pp 36–44Google Scholar
  3. Bermingham A, Smeaton A (2010) Classifying sentiment in microblogs: is brevity an advantage? In: Huang J, Koudas N, Jones GJF, Wu X, Collins- Thompson K, An A (eds) CIKM, ACM, pp 1833–1836Google Scholar
  4. Bifet A, Frank E (2010) Sentiment knowledge discovery in twitter streaming data. In: Pfahringer B, Holmes G, Hoffmann AG (eds) Discovery science, vol 6332. Lecture notes in computer science. Springer, Berlin, pp 1–15CrossRefGoogle Scholar
  5. Bollen J, Mao H, Zeng XJ (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):18CrossRefGoogle Scholar
  6. Carson JB, Tesluk PE, Marrone JA (2007) Shared leadership in teams: an investigation of antecedent conditions and performance. Acad Manag J 50(5):12171234.  https://doi.org/10.5465/amj.2007.20159921 Google Scholar
  7. Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the conference on web search and web data mining (WSDM), pp 231–240Google Scholar
  8. Dragoni M (2017) A three-phase approach for exploiting opinion mining in computational advertising. IEEE Intell Syst 32(3):2127.  https://doi.org/10.1109/MIS.2017.46 CrossRefGoogle Scholar
  9. Dragoni M, Petrucci G (2017) A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans Affect Comput 8(4):457470.  https://doi.org/10.1109/TAFFC.2017.2717879 CrossRefGoogle Scholar
  10. Dragoni M, Petrucci G (2018) A fuzzy-based strategy for multi-domain sentiment analysis. Int J Approx Reason 93:5973.  https://doi.org/10.1016/j.ijar.2017.10.021 MathSciNetCrossRefGoogle Scholar
  11. Fang J, Chen B (2011) Incorporating lexicon knowledge into SVM learning to improve sentiment classification. In: Where AI meets psychology (SAAIP) workshop at the 5th international joint conference on natural language processing (IJCNLP) SA (ed), pp 94–100Google Scholar
  12. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215239.  https://doi.org/10.1016/0378-8733(78)90021-7 CrossRefGoogle Scholar
  13. Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. Technical report, Stanford University, pp 1–6Google Scholar
  14. Gryc W, Moilanen K (2010) Leveraging textual sentiment analysis with social network modelling: sentiment analysis of political blogs in the 2008 US presidential election. In: Proceedings of the from text to political positions workshopGoogle Scholar
  15. Hajian B, White T (2011) Modelling influence in a social network: metrics and evaluation. In: Social-Com/PASSAT, IEEE, pp 497–500Google Scholar
  16. Hu X, Tang L, Tang J, Liu H (2013) Exploiting social relations for sentiment analysis in microblogging. In: Leonardi S, Panconesi A, Ferragina P, Gionis A (eds) WSDM, ACM, pp 537–546Google Scholar
  17. Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent twitter sentiment classification. In: Lin D, Matsumoto Y, Mihalcea R (eds) ACL, The Association for Computer Linguistics, pp 151–160Google Scholar
  18. Kaewpitakkun Y, Shirai K (2016) Incorporation of target specific knowledge for sentiment analysis on microblogging. IEICE Trans 99D(4):959–968CrossRefGoogle Scholar
  19. Kim J, Yoo J, Lim H, Qiu H, Kozareva Z, Galstyan A (2013) Sentiment prediction using collaborative filtering. In: Seventh international AAAI conference on weblogs and social mediaGoogle Scholar
  20. Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604632.  https://doi.org/10.1145/324133.324140 MathSciNetCrossRefzbMATHGoogle Scholar
  21. Kumar A, Sebastian TM (2012) Sentiment analysis on twitter. Int J Comput Sci 9:372–378Google Scholar
  22. Lee AL (2010) Who are the opinion leaders? The physicians, pharmacists, patients, and direct-to-consumer prescription drug advertising. J Health Commun 15:629655Google Scholar
  23. Leenders RT (2002) Modeling social influence through networ autocorrelation: constructing the weight matrix. Soc Netw 24(1):2147CrossRefGoogle Scholar
  24. Leitcha D, Sherif M (2017) Twitter mood, ceo succession announcements and stock returns. J Comput Sci 21:110Google Scholar
  25. Li Y, Ma S, Zhang Y, Huang R, Kinshuk, (2013) An improved mix framework for opinion leader identification in online learning communities. Knowl Based Syst 43:43–51.  https://doi.org/10.1016/j.knosys.2013.01.005 CrossRefGoogle Scholar
  26. Liu KL, Li WJ, Guo M (2012) Emoticon smoothed language models for twitter sentiment analysis. In: 26th AAAI conference on artificial intelligence (AAAI 2012), pp 1678–1684Google Scholar
  27. Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Lin D, Matsumoto Y, Mihalcea R (eds) ACL, The Association for Computer Linguistics, pp 142–150Google Scholar
  28. Malouf R, Mullen T (2008) Taking sides: user classification for informal online political discourse. Internet Res 18:177190CrossRefGoogle Scholar
  29. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27(1):415444CrossRefGoogle Scholar
  30. Mudinas A, Zhang D, Levene M (2012) Combining lexicon and learning based approaches for concept-level sentiment analysis. In: Proceedings of the 1st international workshop on issues of sentiment discovery and opinion mining, ACM, pp 1–8Google Scholar
  31. Nozza D, Maccagnola D, Guigue V, Messina E, Gallinari P (2014) A latent representation model for sentiment analysis in heterogeneous social networks. In: Canal C, Idani A (eds) SEFM workshops, vol 8938. lecture notes in computer science. Springer, Berlin, pp 201–213Google Scholar
  32. OConnor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of ICWSM, 11, pp 122–129Google Scholar
  33. Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, Stanford UniversityGoogle Scholar
  34. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retriev 2:1135Google Scholar
  35. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, 10, pp 79–86Google Scholar
  36. Pozzi FA, Maccagnola D, Fersini E, Messina E (2013) Enhance user-level sentiment analysis on microblogs with approval relations. In: Baldoni M, Baroglio C, Boella G, Micalizio R (eds) AI*IA, lecture notes in computer science, vol 8249. Springer, Berlin, pp 133–144Google Scholar
  37. Saif H, He Y, Alani H (2012) Semantic sentiment analysis of twitter. In: The semantic WebISWC. Springer, Berlin, pp 508–524Google Scholar
  38. Smith LM, Zhu L, Lerman K, Kozareva Z (2013) The role of social media in the discussion of controversial topics. In: SocialCom, IEEE Computer Society, pp 236–243Google Scholar
  39. Speriosu M, Sudan N, Upadhyay S, Baldridge J (2011) Twitter polarity classification with label propagation over lexical links and the follower graph. In: Proceedings of the first workshop on unsupervised learning in NLP, Association for Computational Linguistics, EMNLP 11, pp 53–63Google Scholar
  40. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37:267307CrossRefGoogle Scholar
  41. Tan C, Lee L, Tang J, Jiang L, Zhou M, Li P (2011) User-level sentiment analysis incorporating social networks. In: Apt C, Ghosh J, Smyth P (eds) KDD, ACM, pp 1397–1405Google Scholar
  42. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61:25442558Google Scholar
  43. Thelwall M, Buckley K, Paltoglou G (2012) Sentiment strength detection for the social web. JASIST 63:163173Google Scholar
  44. Vishwanath A (2006) The effect of the number of opinion seekers and leaders on technology attitudes and choices. Hum Commun Res 32(322):350.  https://doi.org/10.1111/j.1468-2958.2006.00278.x Google Scholar
  45. Vo DT, Zhang Y (2015) Target-dependent twitter sentiment classification with rich automatic features. In: Yang Q, Wooldridge M (eds) IJCAI, AAAI Press, pp 1347–1353Google Scholar
  46. Wang S, Manning CD (2012) Baselines and bigrams: simple, good sentiment and topic classification. In: ACL (2), The Association for Computer Linguistics, pp 90–94Google Scholar
  47. Wick M, Rohanimanesh K, Culotta A, McCallum A (2009) Samplerank: learning preferences from atomic gradients. In: On advances in ranking NIP- SNW, pp 1–5Google Scholar
  48. Wu SJ, Chiang RD, Chang HC (2018) Applying sentiment analysis in social web for smart decision support marketing. J Ambient Intell Hum Comput.  https://doi.org/10.1007/s12652-018-0683-9 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.LAMIH UMR CNRS 8201, Université Polytechnique des Hauts-de-France (UPHF)Valenciennes CEDEX 9France

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