Abstract
Readers often search relevant articles via various search engines as they want to have better understanding about domain knowledge they are interested in. They can read through the articles to make sure if the articles meet their needs. After that, readers can absorb contents of the article they are interested in to acquire the domain knowledge. However, articles collected from multiple search engines are great in number and complicated and readers tend to rely on their first impressions on the articles or other subjective evaluations for article selection. Under such circumstances, the important information might be missed. In order to solve the above problem, a comment valuation model based on comment content and structure is developed in this study. Firstly, key attributes of a comment are extracted and defined. After that, the Particle Swarm Optimization (PSO) algorithm is applied to solve the optimization model for the impact of key attributes on comment value evaluation in order to derive an optimal weight for the attributes. The derived weights can be further used to establish the relationship between key attributes and valuation indexes of comments. Values of a target comment can be calculated and visualized via the relationship model. Furthermore, real-world cases from “Mobile01” internet forum and “Yahoo! Knowledge+” are used to evaluate the feasibility and performance of the proposed methodology and platform. As a whole, the proposed methodology and platform can effectively and efficiently extract key attributes from comments in order to assist readers select the important comments from discussion threads.