The Methodology of Answer filtering through online healthcare Q&A Community
*Corresponding Author E-mail: firstname.lastname@example.org
With the advent of Web 2.0, a low quality is overflowing in reality. Because human life is connected directly with healthcare, it is important to manage and assess the information.
In this paper, we have analyzed and classified answers for knowledge customers to get high-quality information by using medical noun list in Naver Knowledge in which is a Q&A community site. We gathered 784 questions and 1542 answers.
The result of accuracy of classification is that ‘Naive Bayes’ records 46% and matching status scores 60%. It shows that library about lung cancer we developed could be used to filter worthless knowledge whenever knowledge consumer wants to get useful medical information. Our development of keyword library is different from the existing medical library in that we consider keywords of non-expert and expert about medical information.
Experimental results contribute to filter the healthcare information for people who are easily seduce by wrong medical information.
Lung cancer, text mining, medical term, healthcare, answer filtering.