基于深度学习的中文微博作者身份识别研究.

Author identification 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, this paper proposed the CABLSTM Chinese microblog author identification model without expert feature modeling, and tested the accuracy of the model in the open microblog corpus. This model maximized the extraction of short text features, fused the attention mechanism in the CNN and removed the pooling layer, and obtained context-related information through the bidirectional LSTM. The identity recognition result was output through the softmax layer. Experimental results show that the model has a certain improvement in accuracy, recall rate, and F-measure in comparison with traditional machine learning algorithms and TextCNN and LSTM algorithms in the identification task of Chinese microblog authors. [ABSTRACT FROM AUTHOR]