Research on Aspect Category Sentiment Classification Based on Gated Convolution Neural Network Combined with Self-Attention Mechanism
Abstract: [Purpose/Significance] In recent years, a common method for aspect category sentiment classification is to combine LSTM model with attention mechanism. Compared to that, the gated convolutional neural network model not only has a simple structure, fewer parameters and shorter training time, but also achieves higher classification accuracy being able to extract aspect features and emotion features. [Method/Process] Considering that the quality of aspect category is crucial for aspect category sentiment classification, this paper coupled aspect category extraction and aspect category sentiment classification, and put forward Gated Convolutional Neural Network with Self Attention-based Aspect Embedding (GCAE_SelfAtt) model to relate the aspect category embeddings to corresponding context, and to achieve a higher accuracy. [Result/Conclusion] The experiment on SemEval dataset shows that GCAE_SelfAtt model does help to extract more coherent aspect categories and achieve higher accuracy for sentiment classification.
文章引用: 张 颖 , 郑建国 (2020) 基于自注意力的门控卷积神经网络的要素类情感分类研究。 计算机科学与应用， 10， 2064-2077. doi: 10.12677/CSA.2020.1011218
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