Text Classification Using Convolutional Capsule Network Based on Dual-Channel Word Vectors

Text classification method based on space vector model has high latitude and sparse features in text expression, which leads to poor performance in feature description, and feature engineering relies on manual extraction, the cost of which is high. To address these problems, this paper proposes a text classification algorithm using convolutional capsule network based on dual-channel word vectors. This algorithm uses word vectors trained by Word2Vec and context vectors extended based on specific text classification tasks as two input channels of the neural network. Then a convolutional capsule network model with dynamic routing mechanism is used for text classification. Experimental results on multiple English datasets show that the dual-channel training method for word vectors has better performance than the single-channel training method. Also, the proposed algorithm has a higher accuracy rate in text classification compared with LSTM,RAE,MV-RNN and other algorithms.