Abstract: Sentiment analysis has always been a hot research direction in natural language processing.Implicit sentiment classification refers to the task of sentiment classification without explicit sentiment words.At present,implicit sentiment analysis is still in its infancy.Implicit sentiment analysis is faced with problems such as lack of explicit sentiment words,euphemism of expression,and difficulty in understanding semantics.Traditional sentiment analysis methods,such as sentiment dictionary and bag-of-word models,are difficult to be effective,making the task of implicit sentiment classification more difficult.To solve the above problems,this paper proposes a graph neural network model that combines text,part-of-speech tags and dependency to perform implicit sentiment classification.Specifically,the model first extracts part of speech and dependency features of the text,and then uses pre-training language model BERT to extract text vector features,thus builds a graph attention neural network based on multiple linguistic features.The model has been tested on SMP2021 implicit sentiment recognition public dataset for several times.Experimental results show that the proposed model achieves the best results compared with multiple baseline models.The proposed implicit sentiment classification method is feasible and effective.

Key words: Implicit sentiment classification, Part-of-speech tagging, Dependency analysis, Graph model, BERT, Linguistic features