Abstract
Social networks have become the most effective information communication platform, which is deeply loved by netizens. However, the wide application of social networks also provides a cyberspace for the spread of sensitive content. Aiming at the detection difficulty caused by a large number of deformed and disguised sensitive words, we first identified sensitive words and sensitive deformation words. Secondly, considering the semantic relationship between the deformed words and the original words, we proposed the sensitive words fingerprint convergence method to associate the deformed words of sensitive words with the original words. Finally, for the text containing sensitive words, we established a convolutional neural network model based on multi-task learning, which combines sensitivity and emotional polarity to detect text content. The experimental results show that the sensitive content detection method proposed in this paper has a good effect.
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