JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Aspect-Based Sentiment Analysis with Position Embedding Interactive Attention Network |
| 영문제목(English Title) |
Aspect-Based Sentiment Analysis with Position Embedding Interactive Attention Network |
| 저자(Author) |
Yan Xiang
Jiqun Zhang
Zhoubin Zhang
Zhengtao Yu
Yantuan Xian
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| 원문수록처(Citation) |
VOL 18 NO. 05 PP. 0614 ~ 0627 (2022. 10) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generated natural language. So far, most of the methods only use the implicit position information of the aspect in the context, instead of directly utilizing the position relationship between the aspect and the sentiment terms. In fact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of given aspects, and proposes a position embedding interactive attention network based on a long short-term memory network. Firstly, it uses the position information of the context simultaneously in the input layer and the attention layer. Secondly, it mines the importance of different context words for the aspect with the interactive attention mechanism. Finally, it generates a valid representation of the aspect and the context for sentiment classification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurant dataset and 1% on the laptop dataset. |
| 키워드(Keyword) |
Aspect-Based Sentiment Analysis
Attention Mechani
Long Short-Term Memory Network
Position Information
Word Embedding
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| 파일첨부 |
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