DNet: A lightweight and efficient model for aspect based sentiment analysis

作者：

Highlights：

• A lightweight and efficient model is proposed for aspect-based sentiment analysis.

• A tradeoff is explored between model accuracy and model complexity.

• We construct hierarchical gating structures to filter out noisy context words.

• The proposed model achieves the state-of-the-art performance with less complexity.

摘要

•A lightweight and efficient model is proposed for aspect-based sentiment analysis.•A tradeoff is explored between model accuracy and model complexity.•We construct hierarchical gating structures to filter out noisy context words.•The proposed model achieves the state-of-the-art performance with less complexity.

论文关键词：Sentiment analysis,Convolutional neural network,BERT,Lightweight

论文评审过程：Received 26 July 2019, Revised 20 February 2020, Accepted 16 March 2020, Available online 19 March 2020, Version of Record 5 April 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113393