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SCIENCE CHINA Information Sciences
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SCIENCE CHINA Information Sciences, Volume 63 , Issue 1 : 111102(2020) https://doi.org/10.1007/S11432-018-9941-6

Sentiment analysis using deep learning approaches: an overview

Olivier HABIMANA 1, Yuhua LI 1,*, Ruixuan LI 1,*, Xiwu GU 1, Ge YU 2
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  • ReceivedAug 18, 2018
  • AcceptedJun 4, 2019
  • PublishedDec 26, 2019
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Abstract


Acknowledgment

This work was supported by National Key Research and Development Program of China (Grant Nos. 2016YFB0800402, 2016QY01W0202), National Natural Science Foundation of China (Grant Nos. U1836204, 61572221, 61433006, U1401258, 61572222, 61502185), Major Projects of the National Social Science Foundation (Grant No. 16ZDA092), and Guangxi High Level 1043 Innovation Team in Higher Education Institutions Innovation Team of ASEAN Digital Cloud Big Data Security and Mining Technology.


References

[1] Pozzi F A, Fersini E, Messina E, et al. Challenges of sentiment analysis in social networks: an overview. Sentiment Anal Soc Netw, 2017, 1: 1--11. Google Scholar

[2] Liu B. Sentiment Analysis and Opinion Mining. Williston: Morgan & Claypool, 2012. Google Scholar

[3] Pang B, Lee L. Opinion Mining and Sentiment Analysis. FNT Inf Retrieval, 2008, 2: 1-135 CrossRef Google Scholar

[4] Liu B. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge: Cambridge University Press. 2015. Google Scholar

[5] Yang M, Tu W T, Wang J X, et al. Attention-based LSTM for target-dependent sentiment classification. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, California, 2017. 5013--5014. Google Scholar

[6] Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modeling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Maryland, 2014. 655--665. Google Scholar

[7] Stone P J, Bales R F, Namenwirth J Z. The general inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information. Syst Res, 2007, 7: 484-498 CrossRef Google Scholar

[8] Pang B, Lee L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia, 2002. 79--86. Google Scholar

[9] Zhang L, Wang S, Liu B. Deep learning for sentiment analysis: A survey. WIREs Data Min Knowl Discov, 2018, 8: 1-25 CrossRef Google Scholar

[10] Rojas-Barahona L M. Deep learning for sentiment analysis. Language Linguistics Compass, 2016, 10: 701-719 CrossRef Google Scholar

[11] Jin Y, Zhang H, Du D L. Incorporating positional information into deep belief networks for sentiment classification. In: Proceedings of Industrial Conference on Data Mining, 2017. 1--15. Google Scholar

[12] Amplayo R K, Kim J, Sung S, et al. Cold-start aware user and product attention for sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, 2018. 2535--2544. Google Scholar

[13] Dou Z-Y. Capturing user and product information for document level sentiment analysis with deep memory network. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017. 521--526. Google Scholar

[14] 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 32nd AAAI Conference on Artificial Intelligence, New Orleans, 2018. 5989--5995. Google Scholar

[15] Salinca A. Convolutional neural networks for sentiment classification on business reviews. In: Proceedings of IJCAI Workshop on Semantic Machine Learning, Melbourne, 2017. Google Scholar

[16] Alashri S, Kandala S S, Bajaj V, et al. An analysis of sentiments on facebook during the 2016 U.S. presidential election. In: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, San Francisco, 2016. 795--802. Google Scholar

[17] Poria S, Cambria E, Gelbukh A. Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Syst, 2016, 108: 42-49 CrossRef Google Scholar

[18] Wang Z Q, Zhang Y. Opinion recommendation using neural memory model. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017. 1626--1637. Google Scholar

[19] Mooney R J, Bunescu R. Mining knowledge from text using information extraction. SIGKDD Explor Newsl, 2005, 7: 3-10 CrossRef Google Scholar

[20] Hu M Q, Liu B. Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, 2004. 168--177. Google Scholar

[21] Turney P D. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, 2002, 417--424. Google Scholar

[22] Kim S M, Hovy E. Determining the sentiment of opinions. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING), Geneva, 2004. Google Scholar

[23] Alm C O, Roth D, Sproat R. Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, 2005. 579--586. Google Scholar

[24] Jindal N, Liu B. Opinion spam and analysis. In: Proceedings of Conference on Web Search and Web Data Mining (WSDM), Palo Alto, 2008. 219--230. Google Scholar

[25] Boiy E, Moens M F. A machine learning approach to sentiment analysis in multilingual Web texts. Inf Retrieval, 2009, 12: 526-558 CrossRef Google Scholar

[26] Morency L-P, Mihalcea R. Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of International Conference on Multimodal Interfaces, Alicante, 2011. 169--176. Google Scholar

[27] He Y L, Lin C H, Gao W, et al. Tracking sentiment and topic dynamics from social media. In: Proceedings of the 6th International AAAI Conference on Weblogs and Social Media, 2012. 483--486. Google Scholar

[28] Zhao Y, Qin B, Liu T. Encoding syntactic representations with a neural network for sentiment collocation extraction. Sci China Inf Sci, 2017, 60: 110101 CrossRef Google Scholar

[29] Lakkaraju H, Socher R, Manning C D. Aspect specific sentiment analysis using hierarchical deep learning. In: Proceedings of the NIPS, Workshop on Deep Learning and Representation Learning, 2014. Google Scholar

[30] Xue W, Li T. Aspect based sentiment analysis with gated convolutional networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, 2018. 2514--2523. Google Scholar

[31] Taboada M, Brooke J, Tofiloski M. Lexicon-Based Methods for Sentiment Analysis. Comput Linguistics, 2011, 37: 267-307 CrossRef Google Scholar

[32] Akter S, Aziz M T. Sentiment analysis on facebook group using lexicon based approach. In: Proceedings of International Conference on Electrical Engineering and Information Communication Technology, Bangladesh, 2016. Google Scholar

[33] Diamantini C, Mircoli A, Potena D. A negation handling technique for sentiment analysis. In: Proceedings of International Conference on Collaboration Technologies and Systems (CTS), Orlando, 2016. 188--195. Google Scholar

[34] Mukherjee S, Joshi S. Author-specific sentiment aggregation for polarity prediction of reviews. In: Proceedings of Language Resources and Evaluation Conference, Reykjavik, 2014. 3092--3099. Google Scholar

[35] Perikos I, Hatzilygeroundis I. Aspect based sentiment analysis in social media with classifier ensembles. In: Proceedings of IEEE/ACIS International Conference on Computer and Information Science, 2017. 273--278. Google Scholar

[36] Musto C, Semeraro G, Polignano M. A comparison of lexicon-based approaches for sentiment analysis of microblog posts. In: Proceedings of the 8th International Workshop on Information Filtering and Retrieval co-located with XIII AI*IA Symposium on Artificial Intelligence, Pisa, 2014. 59--68. Google Scholar

[37] Khan F H, Qamar U, Bashir S. Lexicon based semantic detection of sentiments using expected likelihood estimate smoothed odds ratio. Artif Intell Rev, 2017, 48: 113-138 CrossRef Google Scholar

[38] Aydogan E, Akcayol M A. A comprehensive survey for sentiment analysis tasks using machine learning techniques. In: Proceedings of International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), Sinaia, 2016. Google Scholar

[39] Peng W, Park D H. Generate adjective sentiment dictionary for social media sentiment analysis using constrained nonnegative matrix factorization. In: Proceedings of International AAAI Conference on Weblogs and Social Media, Barcelona, 2011. 273--280. Google Scholar

[40] Povoda L. Sentiment analysis based on support vector machine and big data. In: Proceedings of International Conference on Telecommunications and Signal Processing (TSP), Vienna, 2016. 543--545. Google Scholar

[41] Zainuddin N, Selamat A. Sentiment analysis using support vector machine. In: Proceedings of International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, 2014. Google Scholar

[42] Pannal N U, Nawarathna C P, Jayakody J T K, et al. Supervised learning based approach to aspect based sentiment analysis. In: Proceedings of IEEE International Conference on Computer and Information Technology (CIT), Nadi, 2016. 662--666. Google Scholar

[43] Appel O, Chiclana F, Carter J, et al. A hybrid approach to sentiment analysis. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), Vancouver, 2016. 4950--4957. Google Scholar

[44] Mukwazvure A, Supreethi K P. A hybrid approach to sentiment analysis of news comments. In: Proceedings of International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), Noida, 2015. Google Scholar

[45] Goel A, Gautam J, Kumar S. Real time sentiment analysis of tweets using naive bayes. In: Proceedings of International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016. 257--261. Google Scholar

[46] Zharmagambetov A S, Pak A A. Sentiment analysis of a document using deep learning approach and decision trees. In: Proceedings of International Conference on Electronics Computer and Computation, Kazakhstan, 2015. Google Scholar

[47] Ouyang X, Zhou P, Li C H, et al. Sentiment analysis using convolutional neural network. In: Proceedings of IEEE International Conference on Computer and Information Technology, 2015. 2359--2364. Google Scholar

[48] Kowsari K, Brown D E, Heidarysafa M. HDLTex: hierarchical deep learning for text classification. In: Proceedings of IEEE International Conference on Machine Learning and Applications, Cancun, 2017. 363--371. Google Scholar

[49] Deng L, Yu D. Deep Learning: Methods and Applications. FNT Signal Processing, 2013, 7: 197-387 CrossRef Google Scholar

[50] Mikolov T, Karafiat M, Burget L, et al. Recurrent neural network based language model. In: Proceedings of the 11th Annual Conference of the International Speech Communication Association, 2010. Google Scholar

[51] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. In: Proceedings of International Conference on Neural Information Processing Systems, Lake Tahoe, 2013. 3111--3119. Google Scholar

[52] Luebke D, Humphreys G. How GPUs Work. Computer, 2007, 40: 96-100 CrossRef Google Scholar

[53] Yu T, Hidey C, Rambow O, et al. Leveraging sparse and dense feature combinations for sentiment classification. 2017,. arXiv Google Scholar

[54] Giatsoglou M, Vozalis M G, Diamantaras K. Sentiment analysis leveraging emotions and word embeddings. Expert Syst Appl, 2017, 69: 214-224 CrossRef Google Scholar

[55] Harris Z S. Distributional structure. WORD, 1954, 10: 146--162. Google Scholar

[56] Bengio Y, Ducharme R, Vincent P, et al. A neural probabilistic language model. J Mach Learn Res, 2003, 3: 1137--1155. Google Scholar

[57] Collobert R, Weston J. A unified architecture for natural language processing. In: Proceedings of International Conference on Machine Learning, Helsinki, 2008. 160--167. Google Scholar

[58] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space. In: Proceedings of International Conference on Learning Representations (ICLR), Scottsdale, 2013. Google Scholar

[59] Pennington J, Socher R, Manning D C. Glove: global vectors for word representation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, 2014. 1532--1543. Google Scholar

[60] Joulin A, Grave E, Bojanowski P, et al. Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2017. 427--431. Google Scholar

[61] Zou W Y, Socher R, Cer D, et al. Bilingual word embeddings for phrase-based machine translation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Seattle, 2013. 1393--1398. Google Scholar

[62] Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, 2019. 4171--4186. Google Scholar

[63] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Proceedings of the 31st Annual Conference on Neural Information Processing Systems, Long Beach, 2017. 6000--6010. Google Scholar

[64] Tang D Y, Wei F R, 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, Baltimore, 2014. 1555--1565. Google Scholar

[65] Zhou H W, Chen L, Shi F L, et al. Learning bilingual sentiment word embeddings for cross-language 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 of the Asian Federation of Natural Language Processing, Beijing, 2015. 430--440. Google Scholar

[66] Fu P, Lin Z, Yuan F C, et al. Learning sentiment-specific word embedding via global sentiment representation. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, 2018. 4808--4815. Google Scholar

[67] Erhan D, Courville A, Vincent P. Why does unsupervised pre-training help deep learning? J Mach Learn Res, 2010, 11: 625--660. Google Scholar

[68] Patterson J, Gibson A. Deep Learning. Sebastopol: O'Reilly Media, 2017. Google Scholar

[69] Ballard D H. Modular learning in neural networks. In: Proceeding the 6th National Conference on Artificial Intelligence, Seattle, 1987. 279--284. Google Scholar

[70] Bengio Y. Learning Deep Architectures for AI. FNT Machine Learning, 2009, 2: 1-127 CrossRef Google Scholar

[71] Vincent P, Larochelle H, Bengio Y, et al. Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, Helsinki, 2008. 1096--1103. Google Scholar

[72] Rong W, Nie Y, Ouyang Y. Auto-encoder based bagging architecture for sentiment analysis. J Visual Languages Computing, 2014, 25: 840-849 CrossRef Google Scholar

[73] Zhou H W, Chen L, Huang D G. Cross-lingual sentiment classification based on denoising autoencoder. In: Proceedings of the 3rd CCF Conference on Natural Language Processing and Chinese Computing, Shenzhen, 2014. 181--192. Google Scholar

[74] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets.. Neural Computation, 2006, 18: 1527-1554 CrossRef PubMed Google Scholar

[75] Jin Y, Zhang H, Du D L. Improving deep belief networks via delta rule for sentiment classification. In: Proceedings of the 28th International Conference on Tools with Artificial Intelligence, San Jose, 2016. 410--414. Google Scholar

[76] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, Montréal, 2014. 2672--2680. Google Scholar

[77] Goodfellow I J. Tutorial: generative adversarial networks. In: Proceedings of Neural Information Processing Systems, 2016. Google Scholar

[78] Li Y, Pan Q, Wang S. A Generative Model for category text generation. Inf Sci, 2018, 450: 301-315 CrossRef Google Scholar

[79] Vlachostergiou A, Caridakis G, Mylonas P. Learning Representations of Natural Language Texts with Generative Adversarial Networks at Document, Sentence, and Aspect Level. Algorithms, 2018, 11: 164 CrossRef Google Scholar

[80] Li J, Madry A, Peebles J, el al. On the limitations of first-order approximation in GAN dynamics. In: Proceedings of the 35th International Conference on Machine Learning, Stockholm, 2018. 3011--3019. Google Scholar

[81] Zhang Y Z, Gan Z, Fan K, et al. Adversarial feature matching for text generation. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, 2017. 4006--4015. Google Scholar

[82] Lecun Y, Bottou L, Bengio Y. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86: 2278-2324 CrossRef Google Scholar

[83] Collobert R, Weston J, Bottou L, et al. Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research, 2011, 12:2493--2537. Google Scholar

[84] Wang J, Sun J, Lin H. Convolutional neural networks for expert recommendation in community question answering. Sci China Inf Sci, 2017, 60: 110102 CrossRef Google Scholar

[85] Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, 2014. 1746--1751. Google Scholar

[86] Conneau A, Schwenk H, Barrault L, et al. Very deep convolutional networks for text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, 2017. 1107--1116. Google Scholar

[87] Johnson R, Zhang T. Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), Vancouver, 2017. 562--570. Google Scholar

[88] Elman J L. Finding Structure in Time. Cognitive Sci, 1990, 14: 179-211 CrossRef Google Scholar

[89] Ko W J, Tseng B H, Lee H Y. Recurrent neural network based language modeling with controllable external memory. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, 2017. 5705--5709. Google Scholar

[90] Hochreiter S. The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions. Int J Unc Fuzz Knowl Based Syst, 1998, 06: 107-116 CrossRef Google Scholar

[91] Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation, 1997, 9: 1735-1780 CrossRef Google Scholar

[92] 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 (EMNLP), Doha, 2014. 1724--1734. Google Scholar

[93] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. In: Proceedings of International Conference on Learning Representations (ICLR), San Diego, 2015. Google Scholar

[94] Mishra A, Tamilselvam S, Dasgupta R, Cognition-cognizant sentiment analysis with multitask subjectivity summarization based on annotators' gaze behavior. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, 2018, 5884--5891. Google Scholar

[95] Long Y F, Lu Q, Xiang R, et al. A cognition based attention model for sentiment analysis. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017. 462--471. Google Scholar

[96] Chen H M, Sun M S, Tu C C, et al. Neural sentiment classification with user and product attention. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Austin, 2016. 1650--1659. Google Scholar

[97] 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 31st AAAI Conference on Artificial Intelligence, San Francisco, 2017. Google Scholar

[98] Giannakopoulos A, Musat C, Hossmann A. Unsupervised aspect term extraction with B-LSTM & CRF using automatically labelled datasets. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Copenhagen, 2017. 180--188. Google Scholar

[99] Ghosaly D, Akhtary M S, Chauhany D, et al. Contextual inter-modal attention for multi-modal sentiment analysis. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Brussels, 2018. 3454--3466. Google Scholar

[100] Goller C, Küchler A. Learning task dependent distributed representations by backpropagation through structure. IEEE Trans Neur Netw, 1996, 1: 347--352. Google Scholar

[101] Socher R, Pennington J, Huang E H, et al. Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of Conference on EMNLP, Scotland, 2011. 151--161. Google Scholar

[102] Socher R, Perelygin A, Wu J, et al. Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Seattle, 2013. 1631--1642. Google Scholar

[103] Socher R, Manning D C, Ng Y. A. Learning continuous phrase representations and syntactic parsing with recursive neural networks. In: Proceedings of Neural Information Processing Systems, Deep Learning and Unsupervised Feature Learning Workshop, 2010. Google Scholar

[104] Socher R, Manning D C, Huval B, et al. Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, 2012. 1201--1211. Google Scholar

[105] Mnih V, Kavukcuoglu K, Silver D, et al. Playing atari with deep reinforcement learning. 2013,. arXiv Google Scholar

[106] Li Y. Deep reinforcement learning: an overview. 2017,. arXiv Google Scholar

[107] Mousavi S S, Schukat M, Howley E. Deep reinforcement learning: an overview. In: Proceedings of SAI Intelligent Systems Conference, 2018. 426--440. Google Scholar

[108] Sutton R S, Barto A G. Reinforcement Learning: An Introduction. Cambridge: MIT Press, 2018. Google Scholar

[109] Mnih V, Kavukcuoglu K, Silver D. Human-level control through deep reinforcement learning. Nature, 2015, 518: 529-533 CrossRef PubMed ADS Google Scholar

[110] Williams R J. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn, 1992, 8: 229-256 CrossRef Google Scholar

[111] Zhang T Y, Huang M L, Zhao L. Learning structured representation for text classification via reinforcement learning. In: Proceedings of AAAI Conference on Artificial Intelligence, New Orleans, 2018. Google Scholar

[112] Yogatama D, Blunsom P, Dyer C, et al. Learning to compose words into sentences with reinforcement learning. In: International Conference on Learning Representations (ICLR), Toulon, 2017. Google Scholar

[113] 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 (ICMI), Glasgow, 2017. 163--171. Google Scholar

[114] Su P-H, Gašić M, Mrkšić N, et al. On-line active reward learning for policy optimisation in spoken dialogue systems. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 2016. 2431--2441. Google Scholar

[115] Kamthe S, Deisenroth M P. Data-efficient reinforcement learning with probabilistic model predictive control. In: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Lanzarote, 2018. 1701--1710. Google Scholar

[116] Feng X, Qin B, Liu T. A language-independent neural network for event detection. Sci China Inf Sci, 2018, 61: 092106 CrossRef Google Scholar

[117] Gonzlez J, Pla F, Hurtado L. ELiRF-UPV at SemEval-2017 task 4: sentiment analysis using deep learning. In: Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval), Vancouver, 2017. 723--727. Google Scholar

[118] Tang D Y, Qin B, Liu T. Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Lisbon, 2015. 1422--1432. Google Scholar

[119] 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, 2011. 142--150. Google Scholar

[120] Diao Q, Qiu M, Wu C-Y. Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In: Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, 2014. 193--202. Google Scholar

[121] McAuley J, Leskovec J. Addressing complex and subjective product-related queries with customer reviews. In: Proceedings of the 25th International World Wide Web Conference, Montréal, 2016. 625--635. Google Scholar

[122] Li X, Bing L D, Lam W, et al. Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, 2018. 946--956. Google Scholar

[123] Go A, Bhayani R, Huang L. Twitter sentiment classification using distant supervision. 2009. http://pdfs.semanticscholar.org/52e2/bd533323ddf97073d034bae40a46eda55f34.pdf. Google Scholar

[124] Saif H, Ferndez M, He Y, et al. Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold. In: Proceedings of the 1st International Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI, Turin, 2013. 9--21. Google Scholar

[125] Zadeh A, Zellers R, Pincus E, et al. MOSI: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos. 2016,. arXiv Google Scholar

[126] Zadeh A, Liang P P, Vanbriesen J, 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, Melbourne, 2018. 2236--2246. Google Scholar

[127] Pang B, Lee L. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the Association for Computational Linguistics, 2005. 115--124. Google Scholar

[128] Sanders N J. Sanders-Twitter sentiment corpus. Sanders Analytics LLC, 2011. Google Scholar

[129] Wojatzki M, Ruppert E, Holschneider S, et al. GermEval 2017: shared task on aspect-based sentiment in social media customer Feedback. In: Proceedings of the GermEval 2017 Shared Task, Berlin, 2017. Google Scholar

[130] Nabil M, Aly M, Atiya A F. ASTD: arabic sentiment tweets dataset. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Lisbon, 2015. 2515--2519. Google Scholar

[131] Yang Z C, Yang D Y, Dyer C. Hierarchical attention networks for document classification. In: Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, 2016. 1480--1489. Google Scholar

[132] Ganin Y, Ustinova E, Ajakan H, et al. Domain-Adversarial Training of Neural Networks. Journal of Machine Learning Research, 2016, 27:1--35. Google Scholar

[133] Li Z, Zhang Y, Wei Y, et al. End-to-end adversarial memory network for cross-domain sentiment classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, 2017. 2237--2243. Google Scholar

[134] Zhai S F, Zhang Z F. Semisupervised autoencoder for sentiment analysis. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, 2016. 1394--1400. Google Scholar

[135] Yin R C, Li P, Wang B. Sentiment lexical-augmented convolutional neural networks for sentiment analysis. In: Proceedings of the 2nd International Conference on Data Science in Cyberspace (DSC), Shenzhen, 2017. 630--635. Google Scholar

[136] 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, Beijing, 2015. 1556--1566. Google Scholar

[137] Kokkinos F, Potamianos A. Structural attention neural networks for improved sentiment analysis. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, 2017. 586--591. Google Scholar

[138] Mousa A, Schuller B. Contextual bidirectional long short-term memory recurrent neural network language models: a generative approach to sentiment analysis. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, 2017. 1023--1032. Google Scholar

[139] Wang Y Q, Huang M L, Zhao L, et al. Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Austin, 2016. 606--615. Google Scholar

[140] Tay Y, Tuan L A, Hui S C. Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, 2018. 5956--5963. Google Scholar

[141] Zhang M S, Zhang Y, Vo D. Gated neural networks for targeted sentiment analysis. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, 2016. 3087--3093. Google Scholar

[142] Yang J, Yang R Q, Wang C J, et al. Multi-entity aspect-based sentiment analysis with context, entity and aspect memory. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, 2018. 6029--6036. Google Scholar

[143] 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, 2018. 1121--1131. Google Scholar

[144] Chen P, Sun Z Q, Bing L D, et al. Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, 2017. 452--461. Google Scholar

[145] Ma Y K, Peng H Y, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, 2018. 5876--5883. Google Scholar

[146] Huang B, Carley K M. Parameterized convolutional neural networks for aspect level sentiment classification. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Brussels, 2018. 1091--1096. Google Scholar

[147] Jakob N, Gurevych I. Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In: Proceedings of Empirical Methods in Natural Language Processing, Massachusetts, 2010. 1035--1045. Google Scholar

[148] Wang W, Pan S J, Dahlmeier D, et al. Recursive neural conditional random fields for aspect-based sentiment analysis. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Austin, 2016. 616--626. Google Scholar

[149] Guo L D, Jiang S Y, Du W J, et al. Recurrent neural CRF for aspect term extraction with dependency transmission. In: Proceedings of CCF International Conference on Natural Language Processing and Chinese Computing, 2018. 378--390. Google Scholar

[150] Xue W, Zhou W B, Li T, et al. MTNA: a neural multi-task model for aspect category classification and aspect term extraction on restaurant reviews. In: Proceedings of the 8th International Joint Conference on Natural Language Processing, Taipei, 2017. 151--156. Google Scholar

[151] Li X, Lam W. Deep multi-task learning for aspect term extraction with memory interaction. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017. 2886--2892. Google Scholar

[152] Li X, Bing L D, Li P J. Aspect term extraction with history attention and selective transformation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, 2018. 4194--4200. Google Scholar

[153] Ren Y, Ji D. Neural networks for deceptive opinion spam detection: An empirical study. Inf Sci, 2017, 385-386: 213-224 CrossRef Google Scholar

[154] Bandhakavi A, Wiratunga N, Padmanabhan D. Lexicon based feature extraction for emotion text classification. Pattern Recognition Lett, 2017, 93: 133-142 CrossRef Google Scholar

[155] Felbo B, Mislove A, Søaard A. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017. 1615--1625. Google Scholar

[156] Kim Y, Lee H. 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, 2018. 141--145. Google Scholar

[157] Yu J F, Marujo L, Jiang J, et al. Improving multi-label emotion classification via sentiment classification with dual attention transfer network. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Brussels, 2018. 1097--1102. Google Scholar

[158] Wang Y Q, Feng S, Wang D L, et al. Multi-label chinese microblog emotion classification via convolutional neural network. In: Proceedings of Asia-Pacific Web Conference, 2016. 567--580. Google Scholar

[159] 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, Vancouver, 2017. 718--728. Google Scholar

[160] Tafreshi S, Diab M. Emotion detection and classification in a multigenre corpus with joint multi-task deep learning. In: Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, 2018. 2905--2913. Google Scholar

[161] Chen X, Sun Y, Athiwaratkun B. Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification. Trans Association Comput Linguistics, 2018, 6: 557-570 CrossRef Google Scholar

[162] Ruder S, Ghaffari P, Breslin J G. INSIGHT-1 at SemEval-2016 task 5: deep learning for multilingual aspect-based sentiment analysis. In: Proceedings of SemEval, San Diego, 2016. 330--336. Google Scholar

[163] Becker W, Wehrmann J, Cagnini H E L, et al. An efficient deep neural architecture for multilingual Sentiment analysis in twitter. In: Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference, Florida, 2017. 246--251. Google Scholar

[164] Attia M, Samih Y, ElKahky A, et al. Multilingual multi-class sentiment classification using convolutional neural networks. In: Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC), Miyazaki, 2018. Google Scholar

[165] Can E F, Ezen-Can A, Can F. Multilingual sentiment analysis: an RNN-based framework for limited data. In: Proceedings of ACM SIGIR 2018 Workshop on Learning from Limited or Noisy Data, Ann Arbor, 2018. Google Scholar

[166] 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 NAACL-HLT, New Orleans, 2018. 572--582. Google Scholar

[167] Wang W C, Feng S, Gao W. Personalized microblog sentiment classification via adversarial cross-lingual multi-task learning. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Brussels, 2018. 338--348. Google Scholar

[168] Cambria E, Hazarika D, Poria S, et al. Benchmarking multimodal sentiment analysis. In: Proceedings of International Conference on Computational Linguistics, Budapest, 2017. 166--179. Google Scholar

[169] Poria S, Cambria E, Hazarika D. Context-dependent sentiment analysis in user-generated videos. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, 2017. 873--883. Google Scholar

[170] Zadeh A, Chen M, Poria S. Tensor fusion network for multimodal sentiment analysis. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017. 1103--1114. Google Scholar

[171] Zadeh A, Liang P P, Poria S, et al. Multi-attention recurrent network for human communication comprehension. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, 2018. 5642--5649. Google Scholar

[172] Wu H B, Gu Y W, Sun S D, et al. Aspect-based opinion summarization with convolutional neural networks. In: Proceedings of International Joint Conference on Neural Networks, 2016. 3157--3163. Google Scholar

[173] Li Q, Jin Z, Wang C. Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems. Knowledge-Based Syst, 2016, 107: 289-300 CrossRef Google Scholar

[174] Wang L, Ling W. Neural network-based abstract generation for opinions and arguments. In: Proceedings of NAACL-HLT, San Diego, 2016. 47--57. Google Scholar

[175] Yang M, Qu Q, Zhu J, et al. Cross-domain aspect/sentiment-aware abstractive review summarization. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, 2018. 1531--1534. Google Scholar

[176] Yang M, Qu Q, Shen Y. Aspect and sentiment aware abstractive review summarization. In: Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, 2018. 1110--1120. Google Scholar

[177] Angelidis S, Lapata M. Summarizing opinions: aspect extraction meets sentiment prediction and they are both weakly supervised. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Brussels, 2018. 3675--3686. Google Scholar

[178] Zhao S, Xu Z, Liu L. Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN. Math Problems Eng, 2018, 2018(1): 1-9 CrossRef Google Scholar

[179] Li L, Qin B, Ren W. Document representation and feature combination for deceptive spam review detection. Neurocomputing, 2017, 254: 33-41 CrossRef Google Scholar

[180] Ren Y F, Yue Z. Deceptive opinion spam detection using neural network. In: Proceedings of the 26th International Conference on Computational Linguistics, Osaka, 2016. 140--150. Google Scholar

[181] Zhang W, Du Y, Yoshida T. DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network. Inf Processing Manage, 2018, 54: 576-592 CrossRef Google Scholar

[182] Yadav V, Bethard S. A survey on recent advances in named entity recognition from deep learning models. In: Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, 2018. 2145--2158. Google Scholar

[183] Qiu J Z, Tang J, Ma H, et al. DeepInf: social influence prediction with deep learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, 2018. 2110--2119. Google Scholar

[184] Wang H W, Zhang F Z, Hou M, et al. SHINE: signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, 2018. 592--600. Google Scholar

[185] Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: A survey. Ain Shams Eng J, 2014, 5: 1093-1113 CrossRef Google Scholar