基于方面-词性感知的方面级情感分析OA北大核心CSTPCD

Aspect-Based Sentiment Analysis(ABSA)is a research focal point in natural language processing,whose task is to predict the emotional polarity of a given aspect in a sentence.Currently,most studies have overlooked the role of aspect words and specific Part-Of-Speech(POS)words in filtering contextual semantic information related to emotional polarity and understanding contextual grammatical information.Accordingly,a Graph Convolutional Network(GCN)based on aspect POS perception,ASP_POSGCN,is proposed.It adopts a bidirectional Long Short-Term Memory(LSTM)network to model context and POS information,filtering contextual semantic information related to aspect words through a gate mechanism,and further filtering using the state of the POS information hiding layer.Simultaneously,it designs an aspect POS perception matrix algorithm to reconstruct the original dependency relationship of sentences based on the contribution of different POS words to the emotional polarity of aspect words,to obtain the reconstructed dependency syntax graph.The original and reconstructed dependency syntax graphs are applied to the dual-channel GCN and multi-graph perception mechanism.Finally,it uses the filtered contextual semantic information and the output of the dual channel GCN,and attention is calculated to obtain the final classification representation.The experimental results demonstrate that the accuracy of the model on four public datasets,Twitter,Laptop14,Restaurant14 and Restaurant16,is 74.57%,79.15%,83.84%,and 91.23%,and the F1 values are 72.59%,75.76%,77.00%,and 77.11%,respectively.Compared with the traditional ABSA benchmark model,it has improved and is beneficial to sentiment polarity classification in aspects.