关键词: 直播弹幕, 带噪短文本, 文本过滤, 卷积神经网络

Abstract:

The rise of webcasting has made live broadcasts a new way of communication. However, there are various types of illegal barrage. In the identification of illegal barrage, manual screening is too inefficient, traditional keyword filtering methods and statistical machine learning methods have low recognition rates and cannot tackle mutated short texts. It is a very meaningful problem to make the machine more efficient and accurate to identify illegal barrage to create a better network environment. The TextCNN based method is proposed in this paper, which improves the recognition rate of illegal short text in live barrage by preprocessing the noisy short texts and mining text correlation features using text convolutional neural networks.

Key words: live barrage, text with noise, text filtering, convolutional neural network