Title

自動議題分類機制應用於愛滋病提問議題-以Yahoo知識家為例

Translated Titles

Theme Classification for HIV/AIDS Medical Questions-A Case Study of Yahoo! Answers

DOI

10.6838/YZU.2011.00029

Authors

陳信佑

Key Words

文件探勘 ; 推薦系統 ; 議題分類 ; 基因演算法 ; Text Mining ; Recommendation System ; Theme Classification ; Genetic Algorithm

PublicationName

元智大學資訊管理學系學位論文

Volume or Term/Year and Month of Publication

2011年

Academic Degree Category

碩士

Advisor

邱昭彰

Content Language

繁體中文

Chinese Abstract

近幾年在健康醫療服務(Health Care Service)之研究趨勢上,對於醫療諮詢者的需求,導向於建構實體和虛擬管道進行醫療諮詢之環境。隨著網際網路的發展,網路平台已成為尋求醫療諮詢與討論之熱門虛擬管道。從社會觀感來看,被貼上負面標籤的愛滋病議題上,提問者傾向選擇透過網路管道而逃開使用實體管道尋求相關資訊,其中尤其以尋求HIV/AIDS相關醫療諮詢更加明顯。   因此本研究針對愛滋病議題,開發Crawler程式以收集存在「Yahoo!奇摩知識家」之愛滋病提問文章,透過文件探勘技術(Text Mining)、人工智慧(Artificial Intelligence)等技術,提出一套愛滋病相關文章自動化議題分類機制,以有系統的彙整各項愛滋討論議題建立愛滋病提問之醫療回覆推薦系統資料庫,以提供相關單位及人員後續應用。   實驗結果發現同時考量平均字詞權重與出現位置區塊,予以加權計算,以及針對出現位置區塊進行適切的文章區塊劃分,皆能有效提升分類效果。本研究提出的權重處理方法之分類F-measure為76.32%。

English Abstract

Studies on health care service have been conducted to provide the medical consultation environments to the people who seek the medical suggestions in the real or the virtual way. With the fast development of the Internet, network platforms have become the popular place supplying with medical asking and discussion. Moreover, askers will choose virtual way instead of the real way to quest about the stigmatized HIV/AIDS issue. If people take the unreliable and unverified medical advice from the Internet would cause some problems. For solving this issue, we collect the HIV/AIDS questions from “Yahoo! Answers” and use Text Mining and Artificial Intelligence to conduct a HIV/AIDS recommendation system. Askers can use the text form to describe their problems or the events they suffered, and the system will timely respond the dependably medical recommendation. In the experiment, we found adjustively dividing article can improve the effectiveness of the classification. In addition, considering statistical and linguistic keywords weight can also enhance the accuracy. The F-measure of the best  classification result shows 76.34%.

Topic Category 資訊學院 > 資訊管理學系
社會科學 > 管理學
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