Abstract
Target-oriented sentiment analysis aims to extract the sentiment polarity of a specific target in a sentence. In this paper, we propose a model based on transformers and multi-scale convolutions. The transformer which is based solely on attention mechanisms generalizes well in many natural language processing tasks. Convolution layers with multiple filters can efficiently extract n-gram features at many granularities on each receptive field. We conduct extensive experiments on three datasets: SemEval ABSA challenge Restaurant and Laptop dataset, Twitter dataset. Our framework achieves state-of-the-art results, including improving the accuracy of Restaurant dataset to 84.20% (5.81% absolute improvement), improving the accuracy of the Laptop dataset to 78.21% (4.23% absolute improvement), and improving the accuracy of the Twitter dataset to 72.98% (0.87% absolute improvement).
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Pan, Y., Song, B., Luo, N., Chen, X., Cui, H. (2019). Transformer and Multi-scale Convolution for Target-Oriented Sentiment Analysis. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11642. Springer, Cham. https://doi.org/10.1007/978-3-030-26075-0_24
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