Short Text Sentiment Analysis Based on Multi-Channel CNN with Multi-Head Attention Mechanism

Abstract

In view of the limited text features of short texts, features of short texts should be mined from various angles, and multiple sentiment feature combinations should be used to learn the hidden sentiment information. A novel sentiment analysis model based on multi-channel convolutional neural network with multi-head attention mechanism (MCNN-MA) is proposed. This model combines word features with part of speech features, position features and dependency syntax features separately to form three new combined features, and inputs them into the multi-channel convolutional neural network, as well as integrates the multi-head attention mechanism to more fully learn the sentiment information in the text. Finally, experiments are carried out on two Chinese short text data sets. The experimental results show that the MCNN-MA model has a higher classification accuracy and a relatively low training time cost compared with other baseline models.