Mining Health Social Media with Sentiment Analysis.
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.
Department of Information Management, National Taiwan University, Taipei, Taiwan, Republic of China.|Big Data Laboratory, Chunghwa Telcom Laboratories, Taipei, Taiwan, Republic of China.
Fu-Chen
FC
Yang
Department of Information Management, National Taiwan University, Taipei, Taiwan, Republic of China. jtlee@ntu.edu.tw.
Anthony J T
AJ
Lee
Department of Information Management, National Taiwan University, Taipei, Taiwan, Republic of China.
Sz-Chen
SC
Kuo
D054626:Consumer Health Information / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D061108:Social Media / Q000706:statistics & numerical data*
Health social media; Latent Dirichlet Allocation; Sentiment analysis
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