Online security has been an important issue for several years. One of the burning online security problems lately in Japan has been online slandering and bullying, which appear on unofficial Web sites. The problem has been becoming especially urgent on unofficial Web sites of Japanese schools. School personnel and members of Parent-Teacher Association (PTA) have started Online Patrol to spot Web sites and blogs containing such inappropriate contents. However, countless number of such data makes the job an uphill task. This paper presents a research aiming to develop a systematic approach to Online Patrol by automatically spotting suspicious entries and reporting them to PTA members and therefore help them do their job. We present some of the first results of analysis of the inappropriate data collected from unofficial school Web sites. The analysis is performed firstly with an SVM based machine learning method to detect the inappropriate entries. After analysis of the results we perform another analysis of the data, using an affect analysis system to find out how the machine learning model could be improved.
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