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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.18 No.2
- 발행연도
- 2024.6
- 수록면
- 80 - 92 (13page)
- DOI
- 10.5626/JCSE.2024.18.2.80
이용수
초록· 키워드
오류제보하기
The increasing popularity of various types of online services has led to a growing number of consumers purchasing fresh agricultural products through online platforms. To address the discrepancy between the actual emotional disposition expressed in fresh produce reviews and the sentiment labels that are assigned to them, the current paper proposes a sentiment classification model based on the optimization and integration of a composite semantic structure dictionary. First, the TextRank algorithm is used to extract sentiment-bearing keywords from fresh produce reviews, which are used in the formation of a sentiment dictionary. Sentiment category matching scores are then calculated based on this dictionary. Concurrently, the text vectors processed by bidirectional encoder representations from transformers (BERT) are fed into an enhanced deep pyramid convolutional neural network (DPCNN) and a bidirectional long short-term memory network (BiLSTM), which are tasked with extracting global semantic information and contextual information, respectively. Subsequently, a weighted attention mechanism is used to consolidate these feature representations. Finally, a sentiment classification model, which is designated as Scores-BERT-IDPCNN-BiLSTM-Attention (S-BIDBA), is trained by incorporating sentiment category matching scores. The experimental results obtained herein demonstrate that S-BIDBA significantly enhances the accuracy of sentiment classification for online reviews of fresh agricultural products.
목차
Abstract
I. INTRODUCTION
II. RELATED WORK
III. CONSTRUCTION OF AN EMOTION LEXICON AND AN EMOTION CLASSIFICATION MODEL
IV. EXPERIMENT AND RESULT ANALYSIS
V. CONCLUSION
REFERENCES
참고문헌 (37)
참고문헌 신청
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S. Shojaeilangari, W. Y. Yau, K. Nandakumar, J. Li, and E. K. Teoh, “Robust representation and recognition of facial emotions using extreme sparse learning,” IEEE Transactions on Image Processing, vol. 24, no. 7, pp. 2140-2152, 2015. https://doi.org/10.1109/TIP.2015.2416634
G. Xu, Z. Yu, H. Yao, F. Li, Y. Meng, and X. Wu, “Chinese text sentiment analysis based on extended sentiment dictionary,” IEEE Access, vol. 7, pp. 43749-43762, 2019. https://doi.org/10.1109/ACCESS.2019.2907772
S. Zhang, Z. Wei, Y. Wang, and T. Liao, “Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary,” Future Generation Computer Systems, vol. 81, pp. 395-403, 2018. https://doi.org/10.1016/j.future.2017.09.048
J. Wu, K. Lu, S. Su, and S. Wang, “Chinese micro-blog sentiment analysis based on multiple sentiment dictionaries and semantic rule sets,” IEEE Access, vol. 7, pp. 183924-183939, 2019. https://doi.org/10.1109/ACCESS.2019.2960655
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