Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (9): 2674-2679.DOI: 10.11772/j.issn.1001-9081.2021081448
• Artificial intelligence • Previous Articles
Received:
2021-08-13
Revised:
2021-12-09
Accepted:
2021-12-09
Online:
2022-01-07
Published:
2022-09-10
Contact:
Tianbao XU
About author:
HENG Hongjun, born in 1968, Ph. D., associate professor. His research interests include intelligent information processing, natural language processing, knowledge graph.
通讯作者:
徐天宝
作者简介:
衡红军(1968—),男,河南太康人,副教授,博士,主要研究方向:智能信息处理、自然语言处理、知识图谱;
CLC Number:
Hongjun HENG, Tianbao XU. Attention sentiment analysis model based on multi-scale convolution and gating mechanism[J]. Journal of Computer Applications, 2022, 42(9): 2674-2679.
衡红军, 徐天宝. 基于多尺度卷积和门控机制的注意力情感分析模型[J]. 《计算机应用》唯一官方网站, 2022, 42(9): 2674-2679.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021081448
数据集 | 类别数 | 评论数 | 用户数 | 产品数 | 评论/用户 | 评论/产品 |
---|---|---|---|---|---|---|
IMDB | 10 | 84 919 | 1 310 | 1 635 | 64.82 | 51.94 |
Yelp2013 | 5 | 231 163 | 4 818 | 4 194 | 48.42 | 48.36 |
Yelp2014 | 5 | 78 966 | 1 631 | 1 633 | 47.97 | 55.11 |
Tab. 1 Statistical information of IMDB, Yelp2013 and Yelp2014 datasets
数据集 | 类别数 | 评论数 | 用户数 | 产品数 | 评论/用户 | 评论/产品 |
---|---|---|---|---|---|---|
IMDB | 10 | 84 919 | 1 310 | 1 635 | 64.82 | 51.94 |
Yelp2013 | 5 | 231 163 | 4 818 | 4 194 | 48.42 | 48.36 |
Yelp2014 | 5 | 78 966 | 1 631 | 1 633 | 47.97 | 55.11 |
模型 | IMDB | Yelp2013 | Yelp2014 | |||
---|---|---|---|---|---|---|
Accuracy/% | RMSE | Accuracy/% | RMSE | Accuracy/% | RMSE | |
UPNN | 43.5 | 1.602 | 59.6 | 0.748 | 60.8 | 0.746 |
InterSub | 47.6 | 1.392 | 62.3 | 0.714 | 63.5 | 0.690 |
UPDMN | 46.5 | 1.351 | 61.3 | 0.720 | 63.9 | 0.662 |
TUPCNN | 48.8 | 1.451 | 63.9 | 0.694 | 63.9 | 0.688 |
NSC | 53.3 | 1.281 | 65.0 | 0.692 | 66.7 | 0.654 |
PMA | 54.0 | 1.301 | 65.8 | 0.668 | 67.5 | 0.641 |
DUPMN | 53.9 | 1.279 | 66.2 | 0.667 | 67.6 | 0.639 |
CMA | 54.0 | 1.191 | 66.4 | 0.677 | 67.6 | 0.637 |
HCSC | 54.2 | 1.213 | 65.7 | 0.660 | — | — |
BLBC | — | — | 67.1 | 0.662 | — | — |
HUPMA | 54.5 | 1.253 | 67.0 | 0.659 | 67.6 | 0.641 |
MSC-GTU | 1.191 | 66.3 | 0.673 | 0.643 | ||
MSC-GLU | 1.200 | 66.2 | 0.669 | 0.640 | ||
MSC-GTUU | 66.9 | 0.649 |
Tab. 2 Comparison of accuracy and RMSE among different models
模型 | IMDB | Yelp2013 | Yelp2014 | |||
---|---|---|---|---|---|---|
Accuracy/% | RMSE | Accuracy/% | RMSE | Accuracy/% | RMSE | |
UPNN | 43.5 | 1.602 | 59.6 | 0.748 | 60.8 | 0.746 |
InterSub | 47.6 | 1.392 | 62.3 | 0.714 | 63.5 | 0.690 |
UPDMN | 46.5 | 1.351 | 61.3 | 0.720 | 63.9 | 0.662 |
TUPCNN | 48.8 | 1.451 | 63.9 | 0.694 | 63.9 | 0.688 |
NSC | 53.3 | 1.281 | 65.0 | 0.692 | 66.7 | 0.654 |
PMA | 54.0 | 1.301 | 65.8 | 0.668 | 67.5 | 0.641 |
DUPMN | 53.9 | 1.279 | 66.2 | 0.667 | 67.6 | 0.639 |
CMA | 54.0 | 1.191 | 66.4 | 0.677 | 67.6 | 0.637 |
HCSC | 54.2 | 1.213 | 65.7 | 0.660 | — | — |
BLBC | — | — | 67.1 | 0.662 | — | — |
HUPMA | 54.5 | 1.253 | 67.0 | 0.659 | 67.6 | 0.641 |
MSC-GTU | 1.191 | 66.3 | 0.673 | 0.643 | ||
MSC-GLU | 1.200 | 66.2 | 0.669 | 0.640 | ||
MSC-GTUU | 66.9 | 0.649 |
模型 | IMDB | Yelp2013 | Yelp2014 | |||
---|---|---|---|---|---|---|
Accuracy/% | RMSE | Accuracy/% | RMSE | Accuracy/% | RMSE | |
NoMSC-GTUU | 54.1 | 1.233 | 65.8 | 0.683 | 67.2 | 0.667 |
MSC-NoGate | 54.7 | 1.261 | 66.1 | 0.678 | 67.9 | 0.646 |
MSC-GTUU(no up) | 49.4 | 1.381 | 63.9 | 0.697 | 64.5 | 0.680 |
MSC-GTUU | 55.7 | 1.165 | 66.9 | 0.657 | 68.3 | 0.649 |
Tab. 3 Ablation experiment of multi-scale convolution, gating unit and user-product information
模型 | IMDB | Yelp2013 | Yelp2014 | |||
---|---|---|---|---|---|---|
Accuracy/% | RMSE | Accuracy/% | RMSE | Accuracy/% | RMSE | |
NoMSC-GTUU | 54.1 | 1.233 | 65.8 | 0.683 | 67.2 | 0.667 |
MSC-NoGate | 54.7 | 1.261 | 66.1 | 0.678 | 67.9 | 0.646 |
MSC-GTUU(no up) | 49.4 | 1.381 | 63.9 | 0.697 | 64.5 | 0.680 |
MSC-GTUU | 55.7 | 1.165 | 66.9 | 0.657 | 68.3 | 0.649 |
卷积尺度 | Accuracy/% | RMSE |
---|---|---|
Conv(1) | 54.7 | 1.198 |
Conv(2) | 54.3 | 1.229 |
Conv(4) | 53.7 | 1.267 |
Conv(1,2,4) | 55.7 | 1.165 |
Tab. 4 Comparison of experimental results of different convolution filters
卷积尺度 | Accuracy/% | RMSE |
---|---|---|
Conv(1) | 54.7 | 1.198 |
Conv(2) | 54.3 | 1.229 |
Conv(4) | 53.7 | 1.267 |
Conv(1,2,4) | 55.7 | 1.165 |
1 | PANG B, LEE L, VAITHYANATHAN S. Thumbs up? sentiment classification using machine learning techniques[C]// Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2002:79-86. 10.3115/1118693.1118704 |
2 | ABBASI A, FRANCE S, ZHANG Z, et al. Selecting attributes for sentiment classification using feature relation networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(3):447-462. 10.1109/tkde.2010.110 |
3 | HU X, TANG L, TANG J, et al. Exploiting social relations for sentiment analysis in microblogging[C]// Proceedings of the 6th ACM International Conference on Web Search and Data Mining. New York: ACM, 2013:537-546. 10.1145/2433396.2433465 |
4 | KIM Y. Convolutional neural networks for sentence classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2014:1746-1751. 10.3115/v1/d14-1181 |
5 | HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780. 10.1162/neco.1997.9.8.1735 |
6 | TANG D Y, QIN B, LIU T. Learning semantic representations of users and products for document level sentiment classification[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2015: 1014-1023. 10.3115/v1/p15-1098 |
7 | CHEN H M, SUN M S, TU C C, et al. Neural sentiment classification with user and product attention[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2016: 1650-1659. 10.18653/v1/d16-1171 |
8 | KIM J, AMPLAYO R K, LEE K, et al. Categorical metadata representation for customized text classification[J]. Transactions of the Association for Computational Linguistics, 2019, 7: 201-215. 10.1162/tacl_a_00263 |
9 | 蒋宗礼,张静. 融合用户和产品信息的多头注意力情感分类模型[J]. 计算机系统应用, 2020,29(7):131-138. 10.15888/j.cnki.csa.007447] |
JIANG Z L, ZHANG J. Multi-head attention model with user and product information for sentiment classification[J]. Computer Systems and Applications, 2020, 29(7):131-138. 10.15888/j.cnki.csa.007447] | |
10 | LIN J Y, SUN X, MA S M, et al. Global encoding for abstractive summarization[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA: Association for Computational Linguistics, 2018: 163-169. 10.18653/v1/p18-2027 |
11 | DAUPHIN Y N, FAN A, AULI M, et al. Language modeling with gated convolutional networks[C]// Proceedings of the 34th International Conference on Machine Learning. New York: JMLR.org, 2017: 933-941. |
12 | MANNING C D, SURDEANU M, BAUER J, et al. The Stanford CoreNLP natural language processing toolkit[C]// Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2014: 55-60. 10.3115/v1/p14-5010 |
13 | PENNINGTON J, SOCHER R, MANNING C D. GloVe: global vectors for word representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2014:1532-1543. 10.3115/v1/d14-1162 |
14 | ZEILER M D. ADADELTA: an adaptive learning rate method[EB/OL]. (2012-12-22) [2021-11-07].. |
15 | GUI L, XU R F, HE Y L, et al. Intersubjectivity and sentiment: from language to knowledge[C]// Proceedings of the 25th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2016: 2789-2795. 10.1142/9789813223615_0010 |
16 | CHEN T, XU R. Learning user and product representations for sentiment analysis using sequence modeling[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics,2016: 1-11. |
17 | ZHU P C, YANG Y J. Parallel multi-feature attention on neural sentiment classification[C]// Proceedings of the 8th International Symposium on Information and Communication Technology. New York: ACM, 2017: 181-188. 10.1145/3155133.3155193 |
18 | MA D H, LI S J, ZHANG X D, et al. Cascading multiway attention for document-level sentiment classification[C]// Proceedings of the 8th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). [S.l.]: Asian Federation of Natural Language Processing, 2017: 634-643. |
19 | DOU Z Y. Capturing user and product information for document level sentiment analysis with deep memory network[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2017: 521-526. 10.18653/v1/d17-1054 |
20 | LONG Y F, MA M Y, LU Q, et al. Dual memory network model for biased product review classification[C]// Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Stroudsburg, PA: Association for Computational Linguistics, 2018: 140-148. 10.18653/v1/w18-6220 |
21 | AMPLAYO R K, KIM J, SUNG S, et al. Cold-start aware user and product attention for sentiment classification[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2018: 2535-2544. 10.18653/v1/p18-1236 |
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