基于深度学习的中文微博作者身份识别研究

uthor identification has always plays an important role in the public security and literary inspection work. Texts feature extraction is cumbersome and not universal. To solve this problem, the CABLSTM Chinese microblog author identification model is proposed without expert feature modeling, and the accuracy of the model is tested in the open microblog corpus. This model maximizes the extraction of short text features, fuses the Attention mechanism in the CNN and removes the pooling layer, and obtains context-related information through the bidirectional LSTM. The identity recognition result is output through the Softmax layer. Experimental results show that the model has a certain improvement in accuracy, recall rate, and F value in comparison with traditional machine learning algorithms and TextCNN and LSTM algorithms in the identification task of Chinese microblog authors.