Text Classification Models for Web Content Filtering and Online Safety

Living in an era of anywhere anytime connectedness for the great mass, safety and security on the web presents enormous challenges. There is a great need for better content detection systems that can more accurately identify excessively offensive and harmful websites. Web classification models in the early days are limited by the methods and data available. Today advanced developments in computing methodologies and technology have brought us many new and better means for text content analysis, for example new methods for topic extraction, topic modeling and sentiment analysis. Our recent studies suggested the promising potential of combing topic analysis and sentiment analysis in web content classification. This paper further explores new classification models for better classification performance, especially to enhance precision and reduce false positives, by incorporation of semantics in developing classification models and by examination and handling of the issues with the dataset reliability, class imbalance and covariate shift.