Skip to main content
SpringerLink
Log in

Recent advances in deep learning based sentiment analysis

  • Review
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Sentiment analysis is one of the most popular research areas in natural language processing. It is extremely useful in many applications, such as social media monitoring and e-commerce. Recent application of deep learning based methods has dramatically changed the research strategies and improved the performance of many traditional sentiment analysis tasks, such as sentiment classification and aspect based sentiment analysis. Moreover, it also pushed the boundary of various sentiment analysis task, including sentiment classification of different text granularities and in different application scenarios, implicit sentiment analysis, multimodal sentiment analysis and generation of sentiment-bearing text. In this paper, we give a brief introduction to the recent advance of the deep learning-based methods in these sentiment analysis tasks, including summarizing the approaches and analyzing the dataset. This survey can be well suited for the researchers studying in this field as well as the researchers entering the field.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436–444

    Google Scholar 

  2. Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT Press, 2016

    MATH  Google Scholar 

  3. Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis: Association for Computational Linguistics, 2019. 4171–4186

    Google Scholar 

  4. Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst, 2016, 31: 102–107

    Google Scholar 

  5. Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha: Association for Computational Linguistics, 2014. 1746–1751

    Google Scholar 

  6. Wang B. Disconnected recurrent neural networks for text categorization. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne: Association for Computational Linguistics, 2018. 2311–2320

    Google Scholar 

  7. Lin Z, Feng M, Santos C N d, et al. A structured self-attentive sentence embedding. ArXiv: 1703.03130

  8. Wang Y, Sun A, Han J, et al. Sentiment analysis by capsules. In: Proceedings of the 2018 World Wide Web Conference. Lyon: International World Wide Web Conferences Steering Committee, 2018. 1165–1174

    Google Scholar 

  9. Teng Z, Vo D T, Zhang Y. Context-sensitive lexicon features for neural sentiment analysis. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austion: Association for Computational Linguistics, 2016. 1629–1638

    Google Scholar 

  10. Tay Y, Luu A T, Hui S C, et al. Attentive gated lexicon reader with contrastive contextual co-attention for sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics, 2018. 3443–3453

    Google Scholar 

  11. Socher R, Perelygin A, Wu J, et al. Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Seattle: Association for Computational Linguistics, 2013. 1631–1642

    Google Scholar 

  12. Tai K S, Socher R, Manning C D. Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Beijing: Association for Computational Linguistics, 2015. 1556–1566

    Google Scholar 

  13. Zhu X, Sobihani P, Guo H. Long short-term memory over recursive structures. In: Proceedings of the International Conference on Machine Learning. Lille: PMLR, 2015. 1604–1612

    Google Scholar 

  14. Qian Q, Huang M, Lei J, et al. Linguistically regularized LSTM for sentiment classification. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver: Association for Computational Linguistics, 2017. 1679–1689

    Google Scholar 

  15. Zhang T, Huang M, Zhao L. Learning structured representation for text classification via reinforcement learning. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans: AAAI Press, 2018. 6053–6060

    Google Scholar 

  16. Tang D, Qin B, Liu T. Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics, 2015. 1422–1432

    Google Scholar 

  17. Yang Z, Yang D, Dyer C, et al. Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego: Association for Computational Linguistics, 2016. 1480–1489

    Google Scholar 

  18. Tang D, Qin B, Liu T. Learning semantic representations of users and products for document level sentiment classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Beijing: Association for Computational Linguistics, 2015. 1014–1023

    Google Scholar 

  19. Chen H, Sun M, Tu C, et al. Neural sentiment classification with user and product attention. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin: Association for Computational Linguistics, 2016. 1650–1659

    Google Scholar 

  20. Dou Z Y. Capturing user and product information for document level sentiment analysis with deep memory network. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics, 2017. 521–526

    Google Scholar 

  21. Wu Z, Dai X Y, Yin C, et al. Improving review representations with user attention and product attention for sentiment classification. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans: AAAI Press, 2018. 5989–5996

    Google Scholar 

  22. Yao L, Mao C, Luo Y. Graph convolutional networks for text classification. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, the Thirty-First Innovative Applications of Artificial Intelligence Conference, and the Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu: AAAI Press, 2019. 7370–7377

    Google Scholar 

  23. Tang D, Wei F, Yang N, et al. Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Baltimore: Association for Computational Linguistics, 2014. 1555–1565

    Google Scholar 

  24. Li Z, Zhang Y, Wei Y, et al. End-to-end adversarial memory network for cross-domain sentiment classification. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne: IJCAI Organization, 2017. 2237–2243

    Google Scholar 

  25. Yu J, Jiang J. Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin: Association for Computational Linguistics, 2016. 236–246

    Google Scholar 

  26. Li Z, Wei Y, Zhang Y, et al. Hierarchical attention transfer network for cross-domain sentiment classification. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans: AAAI Press, 2018. 5852–5859

    Google Scholar 

  27. Zhang K, Zhang H, Liu Q, et al. Interactive attention transfer network for cross-domain sentiment classification. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, the Thirty-First Innovative Applications of Artificial Intelligence Conference, and the Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu: AAAI Press, 2019. 5773–5780

    Google Scholar 

  28. Liu P, Qiu X, Huang X. Adversarial multi-task learning for text classification. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver: Association for Computational Linguistics, 2017. 1–10

    Google Scholar 

  29. Chen X, Cardie C. Multinomial adversarial networks for multi-domain text classification. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans: Association for Computational Linguistics, 2018. 1226–1240

    Google Scholar 

  30. Liu Q, Zhang Y, Liu J. Learning domain representation for multi-domain sentiment classification. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans: Association for Computational Linguistics, 2018. 541–550

    Google Scholar 

  31. Zheng R, Chen J, Qiu X. Same representation, different attentions: Shareable sentence representation learning from multiple tasks. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Stockholm: IJCAI Organization, 2018. 4616–4622

    Google Scholar 

  32. Cai Y, Wan X. Multi-domain sentiment classification based on domain-aware embedding and attention. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Macco: IJCAI Organization, 2019. 4904–4910

    Google Scholar 

  33. Zhou X, Wan X, Xiao J. Cross-lingual sentiment classification with bilingual document representation learning. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Berlin: Association for Computational Linguistics, 2016. 1403–1412

    Google Scholar 

  34. Chen X, Sun Y, Athiwaratkun B, et al. Adversarial deep averaging networks for cross-lingual sentiment classification. Trans Association Comput Linguist, 2018, 6: 557–570

    Google Scholar 

  35. Chen X, Awadallah A H, Hassan H, et al. Multi-source cross-lingual model transfer: Learning what to share. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019. 3098–3112

    Google Scholar 

  36. Akhtar M S, Sawant P, Sen S, et al. Solving data sparsity for aspect based sentiment analysis using cross-linguality and multi-linguality. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans: Association for Computational Linguistics, 2018. 572–582

    Google Scholar 

  37. Yu J, Marujo L, Jiang J, et al. Improving multi-label emotion classification via sentiment classification with dual attention transfer network. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics, 2018. 1097–1102

    Google Scholar 

  38. Felbo B, Mislove A, Søgaard A, et al. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics, 2017. 1615–1625

    Google Scholar 

  39. Kim Y, Lee H, Jung K. AttnConvnet at SemEval-2018 Task 1: Attention-based convolutional neural networks for multi-label emotion classification. In: Proceedings of The 12th International Workshop on Semantic Evaluation. New Orleans: Association for Computational Linguistics, 2018. 141–145

    Google Scholar 

  40. Abdul-Mageed M, Ungar L. EmoNet: Fine-grained emotion detection with gated recurrent neural networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver: Association for Computational Linguistics, 2017. 718–728

    Google Scholar 

  41. Wang Z, Lee S, Li S, et al. Emotion detection in code-switching texts via bilingual and sentimental information. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Beijing: Association for Computational Linguistics, 2015. 763–768

    Google Scholar 

  42. Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002). Association for Computational Linguistics, 2002. 79–86

  43. Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery

  44. Rosenthal S, Farra N, Nakov P. SemEval-2017 task 4: Sentiment analysis in twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Vancouver: Association for Computational Linguistics, 2017. 502–518

    Google Scholar 

  45. Maas A L, Daly R E, Pham P T, et al. Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland: Association for Computational Linguistics, 2011. 142–150

    Google Scholar 

  46. Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems 28. Montreal: Curran Associates, Inc., 2015. 649–657

    Google Scholar 

  47. Blitzer J, Dredze M, Pereira F. Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Prague: Association for Computational Linguistics, 2007. 440–447

    Google Scholar 

  48. Strapparava C, Mihalcea R. SemEval-2007 task 14: Affective text. In: Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007). Prague: Association for Computational Linguistics, 2007. 70–74

    Google Scholar 

  49. Liu P, Joty S, Meng H. Fine-grained opinion mining with recurrent neural networks and word embeddings. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics, 2015. 1433–1443

    Google Scholar 

  50. Li X, Bing L, Li P, et al. Aspect term extraction with history attention and selective transformation. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Stockholm: IJCAI Organization, 2018. 4194–4200

    Google Scholar 

  51. Ma D, Li S, Wu F, et al. Exploring sequence-to-sequence learning in aspect term extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019. 3538–3547

    Google Scholar 

  52. Liao M, Li J, Zhang H, et al. Coupling global and local context for unsupervised aspect extraction. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong: Association for Computational Linguistics, 2019. 4579–4589

    Google Scholar 

  53. Shu L, Xu H, Liu B. Lifelong learning CRF for supervised aspect extraction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Vancouver: Association for Computational Linguistics, 2017. 148–154

    Google Scholar 

  54. Xu H, Liu B, Shu L, et al. Double embeddings and CNN-based sequence labeling for aspect extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Melbourne: Association for Computational Linguistics, 2018. 592–598

    Google Scholar 

  55. Yin Y, Wei F, Dong L, et al. Unsupervised word and dependency path embeddings for aspect term extraction. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. New York: IJCAI/AAAI Press, 2016. 2979–2985

    Google Scholar 

  56. He R, Lee W S, Ng H T, et al. An unsupervised neural attention model for aspect extraction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver: Association for Computational Linguistics, 2017. 388–397

    Google Scholar 

  57. Karamanolakis G, Hsu D, Gravano L. Leveraging just a few keywords for fine-grained aspect detection through weakly supervised co-training. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong: Association for Computational Linguistics, 2019. 4611–4621

    Google Scholar 

  58. Wang W, Pan S J. Transition-based adversarial network for cross-lingual aspect extraction. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Stockholm: IJCAI Organization, 2018. 4475–4481

    Google Scholar 

  59. Jebbara S, Cimiano P. Zero-shot cross-lingual opinion target extraction. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis: Association for Computational Linguistics, 2019. 2486–2495

    Google Scholar 

  60. Irsoy O, Cardie C. Opinion mining with deep recurrent neural networks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha: Association for Computational Linguistics, 2014. 720–728

    Google Scholar 

  61. 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 (SemEval 2014). Dublin: Association for Computational Linguistics, 2014. 27–35

    Google Scholar 

  62. Pontiki M, Galanis D, Papageorgiou H, et al. Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Denver: Association for Computational Linguistics, 2015. 486–495

    Google Scholar 

  63. Tang D, Qin B, Feng X, et al. Effective LSTMs for target-dependent sentiment classification. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. Osaka: The COLING 2016 Organizing Committee, 2016. 3298–3307

    Google Scholar 

  64. Tang D, 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: Association for Computational Linguistics, 2016. 214–224

    Google Scholar 

  65. Chen P, Sun Z, Bing L, et al. Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics, 2017. 452–461

    Google Scholar 

  66. Wang Y, Huang M, Zhu X, et al. Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Austin: Association for Computational Linguistics, 2016. 606–615

    Google Scholar 

  67. Vo D, Zhang Y. Target-dependent twitter sentiment classification with rich automatic features. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Buenos Aires: AAAI Press, 2015. 1347–1353

    Google Scholar 

  68. Liu J, Zhang Y. Attention modeling for targeted sentiment. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2. Short Papers. Valencia: Association for Computational Linguistics, 2017. 572–577

    Google Scholar 

  69. Wang B, Lu W. Learning latent opinions for aspect-level sentiment classification. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, the 30th innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence. New Orleans: AAAI Press, 2018. 5537–5544

    Google Scholar 

  70. Ma D, Li S, Zhang X, et al. Interactive attention networks for aspect-level sentiment classification. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne: IJCAI Organization, 2017. 4068–4074

    Google Scholar 

  71. Sun C, Huang L, Qiu X. Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis: Association for Computational Linguistics, 2019. 380–385

    Google Scholar 

  72. Dong L, Wei F, Tan C, et al. Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Baltimore: Association for Computational Linguistics, 2014. 49–54

    Google Scholar 

  73. He R, Lee W S, Ng H T, et al. Effective attention modeling for aspect-level sentiment classification. In: Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe: Association for Computational Linguistics, 2018. 1121–1131

    Google Scholar 

  74. Ruder S, Ghaffari P, Breslin J G. A hierarchical model of reviews for aspect-based sentiment analysis. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin: Association for Computational Linguistics, 2016. 999–1005

    Google Scholar 

  75. Hazarika D, Poria S, Vij P, et al. Modeling inter-aspect dependencies for aspect-based sentiment analysis. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). New Orleans: Association for Computational Linguistics, 2018. 266–270

    Google Scholar 

  76. Majumder N, Poria S, Gelbukh A, et al. IARM: Inter-aspect relation modeling with memory networks in aspect-based sentiment analysis. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics, 2018. 3402–3411

    Google Scholar 

  77. Ma Y, Peng H, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive lstm. In: Proceedings of the Thirty-second AAAI Conference on Artificial Intelligence. New Orleans: AAAI Press, 2018.

    Google Scholar 

  78. He R, Lee W S, Ng H T, et al. Exploiting document knowledge for aspect-level sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Melbourne: Association for Computational Linguistics, 2018. 579–585

    Google Scholar 

  79. Chen Z, Qian T. Transfer capsule network for aspect level sentiment classification. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019. 547–556

    Google Scholar 

  80. Li Z, Wei Y, Zhang Y, et al. Exploiting coarse-to-fine task transfer for aspect-level sentiment classification. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, the Thirty-First Innovative Applications of Artificial Intelligence Conference, and the Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu: AAAI Press, 2019. 4253–4260

    Google Scholar 

  81. Wang W, Pan S J, Dahlmeier D, et al. Recursive neural conditional random fields for aspect-based sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Austin: Association for Computational Linguistics, 2016. 616–626

    Google Scholar 

  82. Li X, Lam W. Deep multi-task learning for aspect term extraction with memory interaction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics, 2017. 2886–2892

    Google Scholar 

  83. Wang W, Pan S J, Dahlmeier D, et al. Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco: AAAI Press, 2017. 3316-3322

    Google Scholar 

  84. Wang W, Pan S J. Recursive neural structural correspondence network for cross-domain aspect and opinion co-extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne: Association for Computational Linguistics, 2018. 2171–2181

    Google Scholar 

  85. Wang W, Pan S J. Transferable interactive memory network for domain adaptation in fine-grained opinion extraction. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, the Thirty-First Innovative Applications of Artificial Intelligence Conference, and the Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu: AAAI Press, 2019. 7192–7199

    Google Scholar 

  86. Luo H, Li T, Liu B, et al. DOER: Dual cross-shared RNN for aspect term-polarity co-extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019. 591–601

    Google Scholar 

  87. He R, Lee W S, Ng H T, et al. An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019. 504–515

    Google Scholar 

  88. Peng H, Xu L, Bing L, et al. Knowing what, how and why: A near complete solution for aspect-based sentiment analysis. In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence. New York: AAAI Press, 2020. 8600–8607

    Google Scholar 

  89. Li Z, Li X, Wei Y, et al. Transferable end-to-end aspect-based sentiment analysis with selective adversarial learning. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong: Association for Computational Linguistics, 2019. 4590–4600

    Google Scholar 

  90. Mohammad S, Kiritchenko S, Sobhani P, et al. SemEval-2016 task 6: Detecting stance in tweets. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). San Diego: Association for Computational Linguistics, 2016. 31–41

    Google Scholar 

  91. Xu R, Zhou Y, Wu D, et al. Overview of NLPCC shared task 4: Stance detection in Chinese microblogs. In: Natural Language Understanding and Intelligent Applications. Kunming: Springer, 2016. 907–916

    Google Scholar 

  92. Augenstein I, Rocktäschel T, Vlachos A, et al. Stance detection with bidirectional conditional encoding. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Austin: Association for Computational Linguistics, 2016. 876–885

    Google Scholar 

  93. Du J, Xu R, He Y, et al. Stance classification with target-specific neural attention. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne: IJCAI Organization, 2017. 3988–3994

    Google Scholar 

  94. Yuan J, Zhao Y, Xu J, et al. Exploring answer stance detection with recurrent conditional attention. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, the Thirty-First Innovative Applications of Artificial Intelligence Conference, and the Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu: AAAI Press, 2019. 7426–7433

    Google Scholar 

  95. Pontiki M, Galanis D, Papageorgiou H, et al. Semeval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016). San Diego: Association for Computational Linguistics, 2016. 19–30

    Google Scholar 

  96. Saeidi M, Bouchard G, Liakata M, et al. SentiHood: Targeted aspect based sentiment analysis dataset for urban neighbourhoods. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. Osaka: The COLING 2016 Organizing Committee, 2016. 1546–1556

    Google Scholar 

  97. Liao J, Wang S, Li D. Identification of fact-implied implicit sentiment based on multi-level semantic fused representation. Knowledge-Based Syst, 2019, 165: 197–207

    Google Scholar 

  98. Wei J, Liao J, Yang Z, et al. BiLSTM with multi-polarity orthogonal attention for implicit sentiment analysis. Neurocomputing, 2020, 383: 165–173

    Google Scholar 

  99. Chen H Y, Chen H H. Implicit polarity and implicit aspect recognition in opinion mining. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Berlin: Association for Computational Linguistics, 2016. 20–25

    Google Scholar 

  100. Li H, Mukherjee A, Si J, et al. Extracting verb expressions implying negative opinions. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Austin: AAAI Press, 2015. 2411–2417

    Google Scholar 

  101. Ding H, Riloff E. Acquiring knowledge of affective events from blogs using label propagation. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016. 2935–2942

  102. Ding H, Riloff E. Human needs categorization of affective events using labeled and unlabeled data. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans: Association for Computational Linguistics, 2018. 1919–1929

    Google Scholar 

  103. Ptáček T, Habernal I, Hong J. Sarcasm detection on czech and english twitter. In: Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers. Dublin: Dublin City University and Association for Computational Linguistics, 2014. 213–223

    Google Scholar 

  104. Zhang M, Zhang Y, Fu G. Tweet sarcasm detection using deep neural network. In: Proceedings of The 26th International Conference on Computational Linguistics: Technical Papers. Osaka: The COLING 2016 Organizing Committee, 2016. 2449–2460

    Google Scholar 

  105. Riloff E, Qadir A, Surve P, et al. Sarcasm as contrast between a positive sentiment and negative situation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Seattle: Association for Computational Linguistics, 2013 704–714

    Google Scholar 

  106. Tay Y, Luu A T, Hui S C, et al. Reasoning with sarcasm by reading in-between. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne: Association for Computational Linguistics, 2018. 1010–1020

    Google Scholar 

  107. Oraby S, Harrison V, Reed L, et al. Creating and characterizing a diverse corpus of sarcasm in dialogue. In: Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Los Angeles: Association for Computational Linguistics, 2016. 31–41

    Google Scholar 

  108. Khodak M, Saunshi N, Vodrahalli K. A large self-annotated corpus for sarcasm. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). Miyazaki: European Language Resources Association (ELRA), 2018. 641–646

    Google Scholar 

  109. Yang D, Lavie A, Dyer C, et al. Humor recognition and humor anchor extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics, 2015. 2367–2376

    Google Scholar 

  110. Ahuja V, Bali T, Singh N. What makes us laugh? Investigations into automatic humor classification. In: Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media. New Orleans: Association for Computational Linguistics, 2018. 1–9

    Google Scholar 

  111. Chen P Y, Soo V W. Humor recognition using deep learning. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). New Orleans: Association for Computational Linguistics, 2018. 113–117

    Google Scholar 

  112. Weller O, Seppi K. Humor detection: A transformer gets the last laugh. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong: Association for Computational Linguistics, 2019. 3612–3616

    Google Scholar 

  113. Zhang D, Zhang H, Liu X, et al. Telling the whole story: A manually annotated chinese dataset for the analysis of humor in jokes. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong: Association for Computational Linguistics, 2019. 6403–6408

    Google Scholar 

  114. Yu J, Jiang J. Adapting bert for target-oriented multimodal sentiment classification. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macco: IJCAI Organization, 2019. 5408–5414

    Google Scholar 

  115. Xu N, Mao W, Chen G. Multi-interactive memory network for aspect based multimodal sentiment analysis. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, the Thirty-First Innovative Applications of Artificial Intelligence Conference, and the Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu: AAAI Press, 2019. 371–378

    Google Scholar 

  116. Zadeh A, Chen M, Poria S, et al. Tensor fusion network for multimodal sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics, 2017. 1103–1114

    Google Scholar 

  117. Liu Z, Shen Y, Lakshminarasimhan V B, et al. Efficient low-rank multimodal fusion with modality-specific factors. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne: Association for Computational Linguistics, 2018. 2247–2256

    Google Scholar 

  118. Chen M, Wang S, Liang P P, et al. Multimodal sentiment analysis with word-level fusion and reinforcement learning. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction. Glasgow: Association for Computing Machinery, 2017. 163–171

    Google Scholar 

  119. Zadeh A, Liang P P, Mazumder N, et al. Memory fusion network for multi-view sequential learning. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans: AAAI Press, 2018. 5634–5641

    Google Scholar 

  120. Liang P P, Liu Z, Zadeh A B, et al. Multimodal language analysis with recurrent multistage fusion. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics, 2018. 150–161

    Google Scholar 

  121. Morency L P, Mihalcea R, Doshi P. Towards multimodal sentiment analysis: Harvesting opinions from the web. In: Proceedings of the 13th international conference on multimodal interfaces. Alicante: Association for Computing Machinery, 2011. 169–176

    Google Scholar 

  122. Wollmer M, Weninger F, Knaup T, et al. YouTube movie reviews: Sentiment analysis in an audio-visual context. IEEE Intell Syst, 2013, 28: 46–53

    Google Scholar 

  123. Zadeh A. Micro-opinion sentiment intensity analysis and summarization in online videos. In: Proceedings of the ACM on International Conference on Multimodal Interaction. 2015. 587–591

  124. Zadeh A B, Liang P P, Poria S, et al. Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne: Association for Computational Linguistics, 2018. 2236–2246

    Google Scholar 

  125. Poria S, Cambria E, Hazarika D, et al. Context-dependent sentiment analysis in user-generated videos. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver: Association for Computational Linguistics, 2017. 873–883

    Google Scholar 

  126. Jiao W, Yang H, King I, et al. HiGRU: Hierarchical gated recurrent units for utterance-level emotion recognition. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis: Association for Computational Linguistics, 2019. 397–406

    Google Scholar 

  127. Zhong P, Wang D, Miao C. Knowledge-enriched transformer for emotion detection in textual conversations. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong: Association for Computational Linguistics, 2019. 165–176

    Google Scholar 

  128. Hazarika D, Poria S, Zadeh A, et al. Conversational memory network for emotion recognition in dyadic dialogue videos. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans: Association for Computational Linguistics, 2018. 2122–2132

    Google Scholar 

  129. Hazarika D, Poria S, Mihalcea R, et al. ICON: Interactive conversational memory network for multimodal emotion detection. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics, 2018. 2594–2604

    Google Scholar 

  130. Majumder N, Poria S, Hazarika D, et al. Dialoguernn: An attentive RNN for emotion detection in conversations. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, the Thirty-First Innovative Applications of Artificial Intelligence Conference, and the Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu: AAAI Press, 2019. 6818–6825

    Google Scholar 

  131. Zhang D, Wu L, Sun C, et al. Modeling both context- and speakersensitive dependence for emotion detection in multi-speaker conversations. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Macco: IJCAI Organization, 2019. 5415–5421

    Google Scholar 

  132. Ghosal D, Majumder N, Poria S, et al. DialogueGCN: A graph convolutional neural network for emotion recognition in conversation. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong: Association for Computational Linguistics, 2019. 154–164

    Google Scholar 

  133. Busso C, Bulut M, Lee C C, et al. IEMOCAP: Interactive emotional dyadic motion capture database. Lang Resources Evaluation, 2008, 42: 335–359

    Google Scholar 

  134. McKeown G, Valstar M F, Cowie R, et al. The SEMAINE database: Annotated multimodal records of emotionally colored conversations between a person and a limited agent. IEEE Trans Affective Comput, 2012, 3: 5–17

    Google Scholar 

  135. Schuller B W, Valstar M F, Eyben F, et al. AVEC 2012: The continuous audio/visual emotion challenge. In: Proceedings of the International Conference on Multimodal Interaction. Monica: ACM, 2012. 449–456

    Google Scholar 

  136. Li Y, Su H, Shen X, et al. DailyDialog: A manually labelled multi-turn dialogue dataset. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Taipei: Asian Federation of Natural Language Processing, 2017. 986–995

    Google Scholar 

  137. Hsu C, Chen S, Kuo C, et al. Emotionlines: An emotion corpus of multi-party conversations. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation. Miyazaki: European Language Resources Association (ELRA), 2018

    Google Scholar 

  138. Poria S, Hazarika D, Majumder N, et al. MELD: A multimodal multiparty dataset for emotion recognition in conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019. 527–536

    Google Scholar 

  139. Zhou H, Huang M, Zhang T, et al. Emotional chatting machine: Emotional conversation generation with internal and external memory. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18). New Orleans: AAAI Press, 2018. 730–739

    Google Scholar 

  140. Song Z, Zheng X, Liu L, et al. Generating responses with a specific emotion in dialog. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019. 3685–3695

    Google Scholar 

  141. Colombo P, Witon W, Modi A, et al. Affect-driven dialog generation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis: Association for Computational Linguistics, 2019. 3734–3743

    Google Scholar 

  142. Huang C, Zaïane O, Trabelsi A, et al. Automatic dialogue generation with expressed emotions. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). New Orleans: Association for Computational Linguistics, 2018. 49–54

    Google Scholar 

  143. Zhong P, Wang D, Miao C. An affect-rich neural conversational model with biased attention and weighted cross-entropy loss. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, the Thirty-First Innovative Applications of Artificial Intelligence Conference, the Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. Honolulu: AAAI Press, 2019

    Google Scholar 

  144. Zhou X, Wang W Y. MojiTalk: Generating emotional responses at scale. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne: Association for Computational Linguistics, 2018. 1128–1137

    Google Scholar 

  145. Lubis N, Sakti S, Yoshino K, et al. Eliciting positive emotion through affect-sensitive dialogue response generation: A neural network approach. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, the 30th innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence. New Orleans: AAAI Press, 2018. 5293–5300

    Google Scholar 

  146. Asghar N, Poupart P, Hoey J, et al. Affective neural response generation. In: Advances in Information Retrieval—40th European Conference on IR Research. Grenoble: Springer Lecture Notes in Computer Science, 2018. 154–166

    Google Scholar 

  147. Shang L, Lu Z, Li H. Neural responding machine for short-text conversation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Beijing: Association for Computational Linguistics, 2015. 1577–1586

    Google Scholar 

  148. Danescu-Niculescu-Mizil C, Lee L. Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. In: Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics. Portland: Association for Computational Linguistics, 2011. 76–87

    Google Scholar 

  149. Tiedemann J. News from opus-a collection of multilingual parallel corpora with tools and interfaces. In: Proceedings of the International Conference Recent advances in natural language processing. Borovets: Association for Computational Linguistics, 2009. 237–248

    Google Scholar 

  150. Wu H, Gu Y, Sun S, et al. Aspect-based opinion summarization with convolutional neural networks. In: Porceedings of the International Joint Conference on Neural Networks. Vancouver: IEEE, 2016. 3157–3163

    Google Scholar 

  151. Wang L, Ling W. Neural network-based abstract generation for opinions and arguments. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego: Association for Computational Linguistics, 2016. 47–57

    Google Scholar 

  152. Angelidis S, Lapata M. Summarizing opinions: Aspect extraction meets sentiment prediction and they are both weakly supervised. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics, 2018. 3675–3686

    Google Scholar 

  153. Huy Tien N, Tung Thanh L, Minh Le N. Opinions summarization: Aspect similarity recognition relaxes the constraint of predefined aspects. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019). Varna: INCOMA Ltd., 2019. 487–496

    Google Scholar 

  154. Zhao C, Chaturvedi S. Weakly-supervised opinion summarization by leveraging external information. In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence. New York: AAAI Press, 2020

    Google Scholar 

  155. Shen T, Lei T, Barzilay R, et al. Style transfer from non-parallel text by cross-alignment. In: Advances in Neural Information Processing Systems 30. Long Beach: Curran Associates, Inc., 2017. 6830–6841

    Google Scholar 

  156. Li J, Jia R, He H, et al. Delete, retrieve, generate: A simple approach to sentiment and style transfer. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans: Association for Computational Linguistics, 2018. 1865–1874

    Google Scholar 

  157. Yang Z, Hu Z, Dyer C, et al. Unsupervised text style transfer using language models as discriminators. In: Advances in Neural Information Processing Systems 31. Montreal: Curran Associates, Inc., 2018. 7287–7298

    Google Scholar 

  158. Fu Z, Tan X, Peng N, et al. Style transfer in text: Exploration and evaluation. In: McIlraith S A, Weinberger K Q, eds. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence. New Orleans: AAAI Press, 2018. 663–670

    Google Scholar 

  159. Prabhumoye S, Tsvetkov Y, Salakhutdinov R, et al. Style transfer through back-translation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne: Association for Computational Linguistics, 2018. 866–876

    Google Scholar 

  160. Xu J, Sun X, Zeng Q, et al. Unpaired sentiment-to-sentiment translation: A cycled reinforcement learning approach. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne: Association for Computational Linguistics, 2018. 979–988

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, 150001, China

    JianHua Yuan, Yang Wu, Xin Lu, YanYan Zhao, Bing Qin & Ting Liu

  2. Pengcheng Lab, Shenzhen, 518066, China

    Bing Qin

Authors
  1. JianHua Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. YanYan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bing Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ting Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to YanYan Zhao.

Additional information

This work was supported by the National Key R&D Program of China (Grant No. 2018YFB1005103), and the National Natural Science Foundation of China (Grant Nos. 61632011 and 61772153). The first author was supported by China Scholarship Council (CSC) during a visit to the University of Copenhagen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuan, J., Wu, Y., Lu, X. et al. Recent advances in deep learning based sentiment analysis. Sci. China Technol. Sci. 63, 1947–1970 (2020). https://doi.org/10.1007/s11431-020-1634-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-020-1634-3

Search

Navigation