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International Conference on Social Informatics

SocInfo 2014: Social Informatics pp 309–325Cite as

Probabilistic User-Level Opinion Detection on Online Social Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8851))

Abstract

The mass popularity of online social networks, such as Facebook and Twitter, makes them an interesting and important platform for exchange of ideas and opinions. Accurately capturing the opinions of users from their self-generated data is crucial for understanding these opinion flow processes. We propose a supervised model that uses a combination of hashtags and n-grams as features to identify the opinions of Twitter users on a topic, from their publicly available tweets. We use it to detect opinions on two current topics: U.S. Politics and Obamacare. Our approach requires no manual labeling of features, and is able to identify user opinion with a very high accuracy over a randomly chosen set of users tweeting on each topic.

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References

  1. Akkaya, C., Wiebe, J., Mihalcea, R.: Subjectivity word sense disambiguation. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 1, pp. 190–199. Association for Computational Linguistics (2009)

    Google Scholar 

  2. Asur, S., Huberman, B.A.: Predicting the future with social media. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 492–499. IEEE (2010)

    Google Scholar 

  3. Avaaz (The World in Action) (2013), http://www.avaaz.org/en/highlights.php

  4. 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)

    Google Scholar 

  5. Bespalov, D., Bai, B., Qi, Y.,, S.: Sentiment classification based on supervised latent n-gram analysis. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 375–382. ACM (2011)

    Google Scholar 

  6. Bird, S., Klein, E., Loper, E.: Natural language processing with Python. O’Reilly Media, Inc. (2009)

    Google Scholar 

  7. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)

    Article  Google Scholar 

  8. Bono, J.E., Ilies, R.: Charisma, positive emotions and mood contagion. The Leadership Quarterly 17(4), 317–334 (2006)

    Article  Google Scholar 

  9. Conover, M.D., Gonçalves, B., Ratkiewicz, J., Flammini, A., Menczer, F.: Predicting the political alignment of twitter users. In: Privacy, security, risk and trust (passat), 2011 IEEE Third International Conference on Social Computing (SocialCom), pp. 192–199. IEEE Computer Society Press, Los Alamitos (2011)

    Google Scholar 

  10. Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528. ACM (2003)

    Google Scholar 

  11. Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 241–249. Association for Computational Linguistics (2010)

    Google Scholar 

  12. Denecke, K.: Using sentiwordnet for multilingual sentiment analysis. In: IEEE 24th International Conference on Data Engineering Workshop, ICDEW 2008, pp. 507–512. IEEE (2008)

    Google Scholar 

  13. Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. Proceedings of LREC 6, 417–422 (2006)

    Google Scholar 

  14. Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  15. Galland, A.: Moveon.org (2013), http://front.moveon.org/thank-you-for-an-awesome-2013/#.Uty0RnmttFQ

  16. Garas, A., Garcia, D., Skowron, M., Schweitzer, F.: Emotional persistence in online chatting communities. Scientific Reports 2 (2012)

    Google Scholar 

  17. Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2009, pp. 211–220. ACM, New York (2009), http://doi.acm.org/10.1145/1518701.1518736

    Google Scholar 

  18. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford pp. 1–12 (2009(a))

    Google Scholar 

  19. Go, A., Huang, L., Bhayani, R.: Twittersentiment (2009b), http://www.sentiment140.com

  20. Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology 60(11), 2169–2188 (2009)

    Article  Google Scholar 

  21. Kassim, S.: How the arab spring was helped by social media (2012), http://www.policymic.com/articles/10642/twitter-revolution-how-the-arab-spring-was-helped-by-social-media

  22. Kucuktunc, O., Cambazoglu, B.B., Weber, I., Ferhatosmanoglu, H.: A large-scale sentiment analysis for yahoo! answers. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 633–642. ACM (2012)

    Google Scholar 

  23. LIWC: LIWC software (2001), http://www.liwc.net/index.php

  24. Marzouki, Y., Oullier, O.: Revolutionizing revolutions: Virtual collective consciousness and the arab spring (2012), http://www.huffingtonpost.com/yousri-marzouki/revolutionizing-revolutio_b_1679181.html

  25. MPQA: MPQA (2005), http://mpqa.cs.pitt.edu/lexicons/

  26. Ng, A.Y.: Feature election, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 78. ACM (2004)

    Google Scholar 

  27. Onuch, O.: Social networks and social media in ukrainian “euromaidan” protests (2014), http://www.washingtonpost.com/blogs/monkey-cage/wp/2014/01/02/social-networks-and-social-media-in-ukrainian-euromaidan-protests-2/

  28. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC (2010)

    Google Scholar 

  29. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  30. Pennacchiotti, M., Popescu, A.M.: A machine learning approach to twitter user classification. In: ICWSM (2011)

    Google Scholar 

  31. Pennebaker, J.W., Chung, C.K., Ireland, M., Gonzales, A., Booth, R.J.: The development and psychometric properties of liwc 2007, Austin, TX, LIWC. Net (2007)

    Google Scholar 

  32. Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count: LIWC 2001 (2001)

    Google Scholar 

  33. Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 695–704. ACM (2011)

    Google Scholar 

  34. Rutledge, P.: How obama won the social media battle in the 2012 presidential campaign (2013), http://mprcenter.org/blog/2013/01/25/how-obama-won-the-social-media-battle-in-the-2012-presidential-campaign/

  35. Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1397–1405. ACM (2011)

    Google Scholar 

  36. Tan, S., Li, Y., Sun, H., Guan, Z., Yan, X., Bu, J., Chen, C., He, X.: Interpreting the public sentiment variations on twitter. IEEE Transactions on Knowledge and Data Engineering (2012)

    Google Scholar 

  37. Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment in twitter events. Journal of the American Society for Information Science and Technology 62(2), 406–418 (2011)

    Article  Google Scholar 

  38. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 61(12), 2544–2558 (2010)

    Article  Google Scholar 

  39. Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1031–1040. ACM (2011)

    Google Scholar 

  40. Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2-3), 165–210 (2005)

    Article  Google Scholar 

  41. Wilson, T.: Fine-grained subjectivity analysis. Ph.D. thesis, Doctoral Dissertation, University of Pittsburgh (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of California, Santa Barbara, USA

    Kasturi Bhattacharjee & Linda Petzold

Authors
  1. Kasturi Bhattacharjee
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  2. Linda Petzold
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Editor information

Editors and Affiliations

  1. Yahoo! Research, Barcelona, Spain

    Luca Maria Aiello

  2. Stanford Graduate School of Education, 485 Lasuen Mall, 94305-3096, Stanford, CA, USA

    Daniel McFarland

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Bhattacharjee, K., Petzold, L. (2014). Probabilistic User-Level Opinion Detection on Online Social Networks. In: Aiello, L.M., McFarland, D. (eds) Social Informatics. SocInfo 2014. Lecture Notes in Computer Science, vol 8851. Springer, Cham. https://doi.org/10.1007/978-3-319-13734-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-13734-6_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13733-9

  • Online ISBN: 978-3-319-13734-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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