Mining Health Social Media with Sentiment Analysis |
| |
Authors: | Fu-Chen Yang Anthony JT Lee Sz-Chen Kuo |
| |
Institution: | 1.Department of Information Management,National Taiwan University,Taipei,Republic of China;2.Big Data Laboratory,Chunghwa Telcom Laboratories,Taipei,Republic of China |
| |
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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|