A Sensitive Words Filtering Model Based on Web Text Features

The false advertising of food and drag on the Internet is mainly based on the content of the product website promotion pages. When people browse a website, they get the most parts of the information from texts on the web. In order to help people to distinguish whether it is false propaganda on this website, we propose a solution for identifying false advertising of text content on food and drug websites by designing the sensitive word recognition model. This paper introduces in detail the specific design and implementation of the food webpage text sensitive text recognition model, including the system improvement of text acquisition and word segmentation algorithm, feature extraction algorithm and text classification in the sensitive word list extraction. The detailed design and execution flow of the voting decision determination result algorithm of the five text classification algorithms are combined for filtering. Finally, we conducted a series of experiments, and the experimental results demonstrated that the proposed filtering solution is effective.