Sentiment Triplet Extraction Method for Multi-feature Weighted Graph Convolutional Networks
Aspect-based sentiment analysis(ABSA)aims to identify users'opinions expressed about specific text aspects using elements such as aspect words,opinion words,and sentiment polarity.However,the existing research mainly focuses on individual tasks,which neglects feature interactions between different parts and causes error propagation issues.A sentiment triplet extraction method based on a multi-feature weighted graph convolutional network is proposed to jointly model multiple subtasks.Then,a double affine attention module is employed to capture the relational probability distribution among word pairs.Additionally,prior information such as text semantics,syntax,and location is encoded into multi-feature vectors.Finally,graph convolution operations are utilized for achieving multi-feature fusion and realizing the joint extraction of aspect term-opinion term-sentiment polarity.Through the estimation test based on 2 benchmark datasets,the experimental results reveal that the sentiment triplet extraction method based on a multi-feature weighted graph convolutional network can effectively alleviate the error propagation issues in pipeline methods.Moreover,feature interaction among each factor of the triplet set is proposed,and it is proved that the model in the current work performs much better than the previous benchmark model at triplet extraction.
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