A Process to Support Analysts in Exploring and Selecting Content from Online Forums

• 作者: Carlos Lucena Darlinton Carvalho Ricardo Marcacini Solange Rezende
• 来源期刊: 社交网络(英文)
• 学科: 医学
• 关键词: Qualitative Analysis of ONLINE Forums EXPLORE and SELECT the ONLINE Forums CONTENT Machine Learning Hierarchical Clustering Terms CO-OCCURRENCE Network Consolidated and Structured Results
• 年，卷（期）: 2014,（2）
• 页码范围: 86-93
• 页数: 8页
• 分类号: R73

摘要

The public content increasingly available on the Internet, especially in online forums, enables researchers to study society in new ways. However, qualitative analysis of online forums is very time consuming and most content is not related to researchers’ interest. Consequently, analysts face the following problem: how to efficiently explore and select the content to be analyzed? This article introduces a new process to support analysts in solving this problem. This process is based on unsupervised machine learning techniques like hierarchical clustering and term co-occurrence network. A tool that helps to apply the proposed process was created to provide consolidated and structured results. This includes measurements and a content exploration interface.