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Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism
Joint Authors
Chen, Xiao-liang
Tang, Mingwei
Wang, Xiaodi
Yang, Tian
Wang, Zhen
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-08
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences.
Existing neural network models provide a useful account of how to judge the polarity.
However, context relative position information for the target terms is adversely ignored under the limitation of training datasets.
Considering position features between words into the models can improve the accuracy of sentiment classification.
Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU).
Firstly, the position features of words in a sentence are initialized to enrich word embedding.
Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network.
Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis.
Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks.
Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features.
MI-biGRU can obviously improve the performance of classification.
American Psychological Association (APA)
Wang, Xiaodi& Chen, Xiao-liang& Tang, Mingwei& Yang, Tian& Wang, Zhen. 2020. Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1153201
Modern Language Association (MLA)
Wang, Xiaodi…[et al.]. Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1153201
American Medical Association (AMA)
Wang, Xiaodi& Chen, Xiao-liang& Tang, Mingwei& Yang, Tian& Wang, Zhen. Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1153201
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1153201