https://scholars.lib.ntu.edu.tw/handle/123456789/415108
標題: | Mining Health Social Media with Sentiment Analysis | 作者: | Yang F.-C. Lee A.J.T. Kuo S.-C. |
關鍵字: | Health social media;Latent Dirichlet Allocation;Sentiment analysis | 公開日期: | 2016 | 卷: | 40 | 期: | 11 | 來源出版物: | Journal of Medical Systems | 摘要: | 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. ? 2016, Springer Science+Business Media New York. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/415108 | ISSN: | 01485598 | DOI: | 10.1007/s10916-016-0604-4 | SDG/關鍵字: | caregiver; experimental model; extract; human; human experiment; mining; social media; symptom; consumer health information; data mining; procedures; social media; statistics and numerical data; Consumer Health Information; Data Mining; Humans; Social Media |
顯示於: | 資訊管理學系 |
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