Chinese Text Sentiment Analysis Based on Dual Channel Attention Network with Hybrid Word Embedding

[Objective] This paper addresses the challenges facing the traditional static word vector embedding method, aiming to handle polysemy in Chinese texts effectively. It also excavates the contextual emotional features and internal semantic association structure. [Methods] In one channel, we integrated the sentiment elements related to the text into Word2Vec and FastText word vectors through rough data reasoning. We also used CNN to extract the local features of the text. In the other channel, we employed BERT for word embedding supplement and used BiLSTM to obtain the global features of the texts. Finally, we added the attention calculation module for the deep interaction of dual channel features. [Results] The experiment on three Chinese datasets achieved the highest accuracy of 92.43%, representing an improvement of 0.81% over the best value of the benchmark model. [Limitations] The selected datasets are only for modelling coarse-grained sentiment classification. We did not conduct experiments in the fine-grained domain. [Conclusions] The proposed model could effectively improve the performance of Chinese text sentiment classification. © 2023 The Author(s).