Abstract:

[Application Background] For the task of sentiment analysis in natural language processing, current deep learning methods are based on big data training to gradually improve the effect, and do not fully exploit the information of sentiment words in the text. [Methods] This paper proposes a dual-channel text sentiment analysis model that integrates sentiment lexicon and attention mechanism. The channel based on self-attention is responsible for extracting semantic features, and the channel based on emotional attention is to extract emotional features, and the features extracted by the two channels are fused to obtain the final vector representation of the text. A soft constraint of attention is also introduced to balance the attention in both channels. [Results] The experimental results show that the dual-channel structure can focus on different features of the text separately, and the combination of semantic and sentiment features significantly improves the sentiment classification performance of the model. The model also has better interpretability due to the integration of the sentiment lexicon. [Conclusions] The sentiment analysis model proposed in this paper has better performance and interpretability compared with related models.

Key words: deep learning, lexicon, text sentiment analysis, dual channel, attention