Due to the flood of pornographic web sites on the internet, an effective web filtering system is essential. Web filtering has become one of the important techniques to handle and filter inappropriate information on the web. In this paper, we introduce a web filtering system based on contents. The system uses a probabilistic text classifier to filter pornographic information on the WWW. We focus initially only on Thai and English language web sites. The first process is to parse the web sites collection to extract unique words and to reduce stop-words. Afterwards, these features are transformed into a structurized “bag of words”. The next process is calculating the probabilities of each category in the naïve bayes classifier (as a pornographic web filter). Finally, we have implemented and experimented on our techniques. After testing by the F-measure, the experimental results of our system show high accuracy. This demonstrates that naïve bayes can provide more effectiveness for web filtering based on text content.
