A Two-Dimensional Webpage Classification Model

Shih-Ting Yang and Chia-Wei Huang
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Shih-Ting Yang: Nanhua University, Chiayi, Taiwan
Chia-Wei Huang: Department of Information Management, Nanhua University, Chiayi, Taiwan

International Journal of Data Warehousing and Mining (IJDWM), 2017, vol. 13, issue 2, 13-44

Abstract: Regarding the webpage classification topics, most classification mechanisms may lack of consideration from the webpage article writer's perspective and the display characteristics of the webpage (color, graphic layout). Hence, this paper develops a Two-dimensional Webpage Classification model to analyze the webpage textual information and display characteristics from the perspectives of webpage users and designers. This model is consisted of the Webpage Block Distribution Analysis (WBDA) module, Webpage Emotion Category Determination (WECD) module and Webpage Specialty Category Determination (WSCD) module. Firstly, in WBDA module, the user and designer habits (such as the web browsing movement and the writing perspective of webpage) should be considered by combining with the eye movement tracking and tag-region judgment to determine the critical blocks and information of the webpage. Secondly, in WECD module, the webpage color codes are acquired to calculate the major colors of the webpage, and further determine the emotional category of webpage. Thirdly, the WSCD module analyzes the webpage textual information by integrating the keyword acquisition technology to identify the specialty category of the webpage. After that, the Two-dimensional category of the webpage can be obtained. In addition, this paper develops a web-based system accordingly for case verification to confirm the feasibility of the methodology. The verification results show that firstly for webpage emotion category judgment when 128 webpage files for training are imported into this system, the respondent's emotion evaluation score is increased to above Level 5 and the system recommendation success rate is increased to 75.78%. Secondly, for specialty category determination, when system uses 1010 to 1120 webpage files for training, the system performance can be increased to above 80%. Hence, the developed system has a high-performance level in webpage emotion category and specialty category determination. That is, this paper proposes a methodology of Two-dimensional Webpage Classification to classify the webpage file information contents and the effects on the emotions of the demanders to assist webpage providers in providing webpage suitable for demanders with the generated two-dimensional information of the webpage.

Date: 2017
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