Evaluation of Most Visited News Websites in Iran based on Machine learning

• Main Authors: Babak Sohrabi, Amir Manian, Molood Arman
• Format: Article
• Language: fas
• Published: Iranian Research Institute for Information and Technology 2017-03-01
• Series: Iranian Journal of Information Processing & Management
• Subjects: web sites evaluation; news websites; TOPSIS; automation; machine learning; neural network; support vector machine
• Online Access: http://jipm.irandoc.ac.ir/browse.php?a_code=A-10-3413-1&slc_lang=en&sid=1
• ISSN: 2251-8223; 2251-8231

Success and effectiveness of websites is largely dependent on the quality of the website. The biggest share of the quality`s new concept is that the technical aspects of products and services combines with customers usage and understanding. Therefore, websites evaluation based on the maximum usage and perception of the customers is considered an important issue to announce to the related organizations the success of website. This ranking and evaluation should be performed in a special activity domain so that the first place of website rank determines among its other competitors. In this article achieving the information of websites is automatic and without the intervention of human so that the instant evaluation could be possible. In this study, one of the Multi criteria Decision-making methods called TOPSIS is used and the weights of the criteria have been achieved of the method entropy in the mentioned method. Eventually, according to the Alexa ranking report the 791 ranking news website have been obtained which have most visitors of the Iranian users, but on the other hand, just a numerical rank as a final output of websites evaluation can’t be very inconsistent with the purpose of competition between websites, so, these numerical ranking from TOPSIS method used as output in machine learing method for seprating websites from excellent to very poor in six categories as lables for training dataset in classification, instead of using manual lables achieved from experts and users’ opinion. For this classification, Machine learning techniques, including artificial neural network and support vector machine were used.