Research on capsule network-based for aspect-level sentiment classification
Considering the difficulty of judging the mixed multiple sentimental polarities in a text,aspect-level sentiment analysis has become a hot research topic.Multiple sentiments of different targets when expressing multi-faceted in a sentence,it will cause problems such as feature overlap,which will hav...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2020-09-01
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| Series: | 智能科学与技术学报 |
| Subjects: | |
| Online Access: | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202031 |
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| Summary: | Considering the difficulty of judging the mixed multiple sentimental polarities in a text,aspect-level sentiment analysis has become a hot research topic.Multiple sentiments of different targets when expressing multi-faceted in a sentence,it will cause problems such as feature overlap,which will have a negative impact on text sentiment classification.A capsule network-based model for aspect-level sentiment classification (SCACaps) was proposed.Sequential convolution was used to extract the features of context and aspect words separately,and an interactive attention mechanism was introduced to reduce the mutual influence on each other,and then the text feature representation was transmitted into the capsule network after reconstruction.The routing algorithm was optimized by introducing high-level capsule coefficients between the capsule layers,and the global parameters were shared in the entire iterative update process to save relatively complete text feature information.By comparing with multiple models,the SCACaps model has the best classification effect,and the SCACaps model also performs better in small sample learning. |
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| ISSN: | 2096-6652 |