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Mining Health Social Media with Sentiment Analysis

  • Patient Facing Systems
  • Published:
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Abstract

With the rapid development of the Internet, more and more users utilize health communities (known as forums) to find health-related information, share their medical stories and experiences, or interact with other people in the communities. In this paper, we propose a framework to analyze the user-generated contents in a health community. The proposed framework contains three phases. First, we extract medical terms, including conditions, symptoms, treatments, effectiveness and side effects to form a virtual document for each question in the community. Next, we modify Latent Dirichlet Allocation (LDA) by adding a weighted scheme, called conLDA, to cluster virtual documents with similar medical term distributions into a conditional topic (C-topic). Finally, we analyze the clustered C-topics by sentiment polarities, and physiological and psychological sentiment. The experiment results show that conLDA outperforms the original LDA, and can cluster relevant medical terms and relevant questions together. The C-topics clustered by conLDA are more thematic than those clustered by the original LDA. The results of sentiment analysis may provide a quick reference and valuable insights for patients, caregivers and doctors.

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Notes

  1. http://curetogether.com/

  2. http://www.medhelp.org/

  3. http://www.nlm.nih.gov/research/umls/

  4. http://en.wikipedia.org/wiki/Gibbs_sampling

  5. http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010

  6. http://tartarus.org/martin/PorterStemmer/

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Acknowledgments

The authors are grateful to the anonymous referees for their helpful comments and suggestions. This research was supported in part by the Ministry of Science and Technology, Republic of China under Grant No. MOST 103-2410-H-002-109-MY3.

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Authors and Affiliations

  1. Department of Information Management, National Taiwan University, Taipei, Taiwan, Republic of China

    Fu-Chen Yang, Anthony J.T. Lee & Sz-Chen Kuo

  2. Big Data Laboratory, Chunghwa Telcom Laboratories, Taipei, Taiwan, Republic of China

    Fu-Chen Yang

Authors
  1. Fu-Chen Yang
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  2. Anthony J.T. Lee
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  3. Sz-Chen Kuo
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Correspondence to Anthony J.T. Lee.

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This article is part of the Topical Collection on Patient Facing Systems

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Yang, FC., Lee, A.J. & Kuo, SC. Mining Health Social Media with Sentiment Analysis. J Med Syst 40, 236 (2016). https://doi.org/10.1007/s10916-016-0604-4

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