Dynamic commonsense knowledge fused method for Chinese implicit sentiment analysis

Compared with explicit sentiment analysis that attracts considerable attention, implicit sen-timent analysis is a more difficult task due to the lack of sentimental words. The abundant information in an external sentimental knowledge base can play a significant complementary and expansion role. In this paper, a sentimental commonsense knowledge graph embedded multi-polarity orthogonal attention model is proposed to learn the implication of the implicit sentiment. We analyzed the effectiveness of different knowledge relations in the ConceptNet knowledge base in detail, and proposed a matching and filtering method to distill useful knowledge tuples for implicit sentiment analysis automatically. By introducing the sentimental information in the knowledge base, the proposed model can extend the semantic of a sentence with an implicit sentiment. Then, a bi-directional long-short term memory model with multi-polarity orthogonal attention is adopted to fuse the distilled sentimental knowledge with the semantic embedding, effectively enriching the representation of sentences. Experiments on the SMP2019-ECISA implicit sentiment dataset show that our model fully utilizes the information of the knowledge base and improves the performance of Chinese implicit sentiment analysis.

Automated summary

This paper proposes a method for learning the implication of implicit sentiment using sentimental commonsense knowledge graph embedding and multi-polarity orthogonal attention. By automatically extracting useful knowledge tuples through a matching and filtering method, the sentimental knowledge is combined with semantic embedding, improving the performance of implicit sentiment analysis.