Back to Search
Start Over
Residual attention and other aspects module for aspect-based sentiment analysis
- Source :
- Neurocomputing. 435:42-52
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
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.
- 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
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 435
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........9caee918f4e947e2fd9c5a66eef936b4