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Exploration of Online Health Support Groups Through the Lens of Sentiment Analysis

  • Keyang Zheng
  • Ang Li
  • Rosta Farzan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10766)

Abstract

Online health support groups have been gaining prominence in supporting patients and their caregivers. However, it stays as a challenge to understand the role they play in the life of their members. In this paper, we propose a novel approach in utilizing sentiment analysis to explore the dynamics and impact of online health support groups. We present our sentiment analysis model designed for social media support groups and our preliminary results in utilizing the model to understand a Facebook support group for patients with Sickle Cell Disease.

References

  1. 1.
    Beaudoin, C.E., Tao, C.C.: Modeling the impact of online cancer resources on supporters of cancer patients. New Media Soc. 10, 321–344 (2008)CrossRefGoogle Scholar
  2. 2.
    Biyani, P., Caragea, C., Mitra, P., Yen, J.: Identifying emotional and informational support in online health communities. In: COLING, pp. 827–836 (2014)Google Scholar
  3. 3.
    Dunkel-Schetter, C.: Social support and cancer: findings based on patient interviews and their implications. J. Soc. Issues 40, 77–98 (1984)CrossRefGoogle Scholar
  4. 4.
    Farzan, R., Jonassaint, C.: Exploring dynamics of Facebook health support groups: a leadership perspective. In: Proceedings of the 50th Hawaii International Conference on System Sciences (2017)Google Scholar
  5. 5.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Proj. Rep. Stanf. 1(12) (2009)Google Scholar
  6. 6.
    Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014)Google Scholar
  7. 7.
    Pan, S.J., Ni, X., Sun, J.T., Yang, Q., Chen, Z.: Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th International Conference on World Wide Web, pp. 751–760. ACM (2010)Google Scholar
  8. 8.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, pp. 79–86 (2002)Google Scholar
  9. 9.
    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. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Qiu, B., Zhao, K., Mitra, P., Wu, D., Caragea, C., Yen, J., Greer, G.E., Portier, K.: Get online support, feel better - sentiment analysis and dynamics in an online cancer survivor community. In: Conference on Privacy, Security, Risk and Trust, pp. 274–281 (2011)Google Scholar
  11. 11.
    Rodgers, S., Chen, Q.: Internet community group participation: psychosocial benefits for women with breast cancer. J. Comput.-Mediat. Commun. 10(4), 00–00 (2005)CrossRefGoogle Scholar
  12. 12.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  13. 13.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D.: Sentiment strength detection in short informal text. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)CrossRefGoogle Scholar
  14. 14.
    Zhao, K., Yen, J., Greer, G., Qiu, B., Mitra, P., Portier, K.: Finding influential users of online health communities: a new metric based on sentiment influence. J. Am. Med. Inform. Assoc. 21, e212–e218 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computing and InformationUniversity of PittsburghPittsburghUSA

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