References

[1] Pang B, Lee L. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2008, 2(1-2):1-135. Google Scholar https://scholar.google.com/scholar?&q=Pang B, Lee L. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2008, 2(1-2):1-135&

[2] Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 2012, 5(1):1-167. Google Scholar https://scholar.google.com/scholar?&q=Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 2012, 5(1):1-167&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=Vo D, Zhang Y. Target-dependent twitter sentiment classification with rich automatic features. In: Proceedings of the IJCAI, Buenos Aires, 2015. 1347-1353&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=Mikolov T, Karafi'at M, Burget L, et al. Recurrent neural network based language model. In: Proceedings of the Interspeech, Makuhari, 2010. 1045--1048&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&

[18] Huang B X, Ou Y L, Carley K M. Aspect level sentiment classification with attention-over-attention neural networks. 2018,. arXiv Google Scholar https://scholar.google.com/scholar?&q=Huang B X, Ou Y L, Carley K M. Aspect level sentiment classification with attention-over-attention neural networks. 2018,&

[19] Zheng S L, Xia R. Left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention. 2018,. arXiv Google Scholar https://scholar.google.com/scholar?&q=Zheng S L, Xia R. Left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention. 2018,&

[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 https://scholar.google.com/scholar?&q=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&

[21] Xiong C M, Zhong V, Socher R. Dynamic coattention networks for question answering. 2017,. arXiv Google Scholar https://scholar.google.com/scholar?&q=Xiong C M, Zhong V, Socher R. Dynamic coattention networks for question answering. 2017,&

[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 https://scholar.google.com/scholar?&q=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&

[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 https://scholar.google.com/scholar?&q=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&