1. Residual attention and other aspects module for aspect-based sentiment analysis
- Author
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Wang Kaige, Chao Wu, Yang Zhengyi, Qiwu Zhu, Min Gao, Qingyu Xiong, Yang Yu, and Qiude Li
- Subjects
0209 industrial biotechnology ,Artificial neural network ,Computer science ,business.industry ,Cognitive Neuroscience ,Sentiment analysis ,Context (language use) ,02 engineering and technology ,Residual ,Machine learning ,computer.software_genre ,Computer Science Applications ,Term (time) ,Task (computing) ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Representation (mathematics) ,computer - Abstract
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed to predict the sentiment polarity of each aspect term in a text. Recent research mainly uses neural networks to model text and utilizes attention mechanisms to interact for associate aspect terms and context to obtain more effective feature representation. However, the general attention mechanism is easy to lose the original information. Besides, in the multi-aspect text, the sentiment information of other aspect terms interferes with the sentiment analysis of the current aspect term likely. In this paper, we propose two models named RA-CNN and RAO-CNN for ABSA tasks. In RA-CNN, we apply CNN to model the aspect term and utilize a specially designed residual attention mechanism to interact with the text. Based on the RA-CNN, RAO-CNN adds other aspect terms module, which can reduce interference of sentiment information related to other aspect terms in the multi-aspect text. To verify the proposed models’ effectiveness, we conduct a large number of experiments and comparisons on seven public datasets. Experimental results show that our proposed models are useful and achieve state-of-the-art results.
- Published
- 2021
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