Abstract
The existing end-to-end Aspect-Based Sentiment Analysis (ABSA) algorithms focus on feature extraction by a single model, which leads to the loss of the important local or global information. In order to capture both local and global information of sentences, an end-to-end ABSA method based on features fusion is proposed. Firstly, the pre-trained model BERT is applied to obtain word vectors; secondly, Iterated Dilated Convolutions Neural Networks (IDCNN) and Bi-directional Long Short-Term Memory (BiLSTM) with Self-Attention mechanism (BLSA) are adopted to capture local and global features of sentences, and the generated local and context dependency vectors are fused to yield feature vectors. Finally, Conditional Random Fields (CRF) is applied to predict aspect words and sentiment polarity simultaneously. On Laptop14 and Restaurant datasets, our model’s F1 scores increased by 0.51%, 3.11% respectively compared with the best model in the comparison experiment, and 0.74%, 0.78% respectively compared with the single model with the best effect in the ablation experiment. We removed each important module in turn in subsequent experiments and compared it with our model. The experimental results demonstrate the effectiveness of this method in aspect word recognition and its better generalization ability.
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Acknowledgement
The work is supported by National Key Research and Development Program Project “Research on data-driven comprehensive quality accurate service technology for small medium and micro enterprises” (Grant No. 2019YFB1405303).
The Project of Cultivation for Young Top-motch Talents of Beijing Municipal Institutions “Research on the comprehensive quality intelligent service and optimized technology for small medium and micro enterprises” (Grant No. BPHR202203233).
National Natural Science Foundation of China “Research on the influence and governance strategy of online review manipulation with the perspective of E-commerce ecosystem” (Grant No. 72174018).
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Liu, X., Chen, J., Zhang, W. (2023). End-to-End Aspect-Based Sentiment Analysis Based on IDCNN-BLSA Feature Fusion. In: Chen, J., Huynh, VN., Tang, X., Wu, J. (eds) Knowledge and Systems Sciences. KSS 2023. Communications in Computer and Information Science, vol 1927. Springer, Singapore. https://doi.org/10.1007/978-981-99-8318-6_4
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DOI: https://doi.org/10.1007/978-981-99-8318-6_4
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