国家自然科学基金(61672126)
[1] Pang B, Lee L. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2008, 2(1-2):1-135. Google Scholar
[2] Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 2012, 5(1):1-167. Google Scholar
[3] Pontiki M, Galanis D, Pavlopoulos J, et al. Semeval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation, Dublin, 2014. 27--35. Google Scholar
[4] Wagner J, Arora P, Cortes S, et al. Dcu: aspect-based polarity classification for semeval task 4. In: Proceedings of the 8th International Workshop on Semantic Evaluation, Dublin, 2014. 223--229. Google Scholar
[5] Kiritchenko S, Zhu X D, Cherry C, et al. NRC-Canada-2014: detecting aspects and sentiment in customer reviews. In: Proceedings of the 8th International Workshop on Semantic Evaluation, Dublin, 2014. 437--442. Google Scholar
[6] Vo D, Zhang Y. Target-dependent twitter sentiment classification with rich automatic features. In: Proceedings of the IJCAI, Buenos Aires, 2015. 1347-1353. Google Scholar
[7] Tang D Y, Qin B, Feng X C, et al. Effective lstms for target-dependent sentiment classification. In: Proceedings of COLING, Osaka, 2016. 3298--3307. Google Scholar
[8] Wang Y Q, Huang M L, Zhao L, et al. Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2016. 606--615. Google Scholar
[9] Tang D Y, Qin B, Liu T. Aspect level sentiment classification with deep memory network. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Austin, 2016. 214--224. Google Scholar
[10] Chen P, Sun Z Q, Bing L D, et al. Recurrent Attention Network on Memory for Aspect Sentiment Analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017. 452--461. Google Scholar
[11] Socher R, Pennington J, Huang H, et al. Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Edinburgh, 2011. 151--161. Google Scholar
[12] Dong L, Wei F R, Tan C Q, et al. Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52th Annual Meeting of the Association for Computational Linguistics, Baltimore, 2014. 49--54. Google Scholar
[13] Qian Q, Tian B, Huang M L, et al. Learning tag embeddings and tag-specific composition functions in recursive neural network. In: Proceedings of the 53th Annual Meeting of the Association for Computational Linguistics, Beijing, 2015. 1365--1374. Google Scholar
[14] Mikolov T, Karafi'at M, Burget L, et al. Recurrent neural network based language model. In: Proceedings of the Interspeech, Makuhari, 2010. 1045--1048. Google Scholar
[15] Tai K S, Socher R, Manning C D. Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, Beijing, 2015. 1556--1566. Google Scholar
[16] Jiang L, Yu M, Zhou M, et al. Target-dependent twitter sentiment classification. In: Proceedings of Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, 2011. 151--160. Google Scholar
[17] Ma D H, Li S J, Zhang X D, et al. Interactive attention networks for aspect-level sentiment classification. In: Proceedings of the IJCAI, Melbourne, 2017. 4068--4074. Google Scholar
[18] Huang B X, Ou Y L, Carley K M. Aspect level sentiment classification with attention-over-attention neural networks. 2018,. arXiv Google Scholar
[19] Zheng S L, Xia R. Left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention. 2018,. arXiv Google Scholar
[20] Cho K, Merrienboer B V, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Doha, 2014. 1724--1734. Google Scholar
[21] Xiong C M, Zhong V, Socher R. Dynamic coattention networks for question answering. 2017,. arXiv Google Scholar
[22] Pennington J, Socher R, Manning C D. Glove: global vectors for word representation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Doha, 2014. 1532--1543. Google Scholar
[23] Tieleman T, Hinton G. Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 2012, 4: 26--30. Google Scholar
Figure 1
(Color online) The architecture of DAGRU model
Figure 2
(Color online) DAGRU unit
Sentence | Target entity | Sentiment polarity |
(a) Great food but the service was dreadful | Food | Positive |
Service | Negative | |
(b) Except Patrick, all other actors don't play well | Patrick | Positive |
Sentiment polarity | Laptop | Restaurant | ||
Train | Test | Train | Test | |
Positive | 994 | 341 | 2164 | 728 |
Neutral | 464 | 169 | 637 | 196 |
Negative | 870 | 128 | 807 | 196 |
Total | 2328 | 638 | 3608 | 1120 |
Parameter | Value |
BiGRU hidden units | 64 |
DAGRU hidden units | 128 |
Dropout | 0.5 |
Recurrent dropout | 0.5 |
DAGRU layers $K$ | 5 |
Batch size | 64 |
Learning rate | 0.001 |
Layers | Laptop (%) | Restaurant (%) | ||
Joint encoding | Individual encoding | Joint encoding | Individual encoding | |
0 | 74.92 | 56.27 | 80.09 | 67.50 |
1 | 75.70 | 72.57 | 80.80 | 80.18 |
2 | 76.02 | 72.88 | 81.07 | 80.18 |
3 | 75.86 | 72.88 | 81.16 | 80.80 |
4 | $\boldsymbol{76.49}$ | 73.04 | 81.25 | $\boldsymbol{81.61}$ |
5 | 76.33 | $\boldsymbol{73.51}$ | $\boldsymbol{81.96}$ | 81.07 |
a) The blod number represents the highest result.
Model | Laptop (%) | Restaurant (%) |
Majority | 53.45 | 65.00 |
Simple-SVM | 66.97 | 73.22 |
Feature-enhauced SVM (Kiritchenko et al.) | 72.10 | 80.89 |
TD-LSTM (Tang et al.) | 68.13 | 75.63 |
AE-LSTM (Wang et al.) | 68.90 | 76.60 |
ATAE-LSTM (Wang et al.) | 68.70 | 77.20 |
MemNet (Tang et al.) | 70.33 | 79.98 |
IAN (Ma et al.) | 72.10 | 78.60 |
RAM (Chen et al.) | 74.49 | 80.23 |
AOA-LSTM (Huang et al.) | 74.50 | 81.20 |
LCR-Rot (Zheng and Xia) | 75.24 | 81.34 |