Sentiment analysis using deep learning architectures: a review

Artificial Intelligence Review - Tập 53 Số 6 - Trang 4335-4385 - 2020
Ashima Yadav1, Dinesh Kumar Vishwakarma1
1Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, New Delhi 110042, India

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Abbasi A, Chen H, Salem A (2008) Sentiment analysis in multiple languages: feature selection for opinion classification in web forums. ACM Trans Inf Syst 26(3):12:1–12:34

Abdi A, Shamsuddin SM, Hasan S (2018) Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment. Expert Syst Appl 109:66–85

Abdi A, Mariyam S, Hasan S, Piran J (2019) Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion. Inf Process Manag 56(4):1245–1259

Agarwal A, Yadav A, Vishwakarma DK (2019) Multimodal sentiment analysis via RNN variants. In IEEE international conference on big data, cloud computing, data science and engineering (BCD), pp 19–23

Al-Smadi M, Al-Ayyoub M, Al-Sarhan H, Jararwell Y (2016) Using aspect-based sentiment analysis to evaluate Arabic news affect on readers. In: IEEE/ACM 8th international conference on utility and cloud computing, vol 22, no 5, pp 630–649

Al-Smadi M, Qawasmeh O, Al-Ayyoub M, Jararweh Y, Gupta B (2017) Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. J Comput Sci 27:386

Al-Smadi M, Talafha B, Al-Ayyoub M, Jararweh Y (2018) Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-018-0799-4

Aly R, Remus S, Biemann C (2018) Hierarchical multi-label classification of text with capsule networks. In: Proceedings of the 35th international conference on machine learning, Sweden

Arun K, Srinagesh A, Ramesh M (2017) Twitter sentiment analysis on demonetization tweets in India using R language. Int J Comput Eng Res Trends 4(6):252–258

Azeez J, Aravindhar DJ (2015) Hybrid approach to crime prediction using deep learning. In: International conference on advances in computing, communications and informatics (ICACCI), pp 1701–1710

Baccianella S, Esuli A, Sebastiani F (2009) Multi-facet rating of product reviews. In: European conference on information retrieval. Springer, Berlin, pp 461–472

Baccianella S, Esuli A, Sebastiani F (2010) SentiwordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the seventh conference on international language resources and evaluation (LREC’10), pp 2200–2204

Baktha K, Tripathy BK (2017) Investigation of recurrent neural networks in the field of sentiment analysis. In: Proceedings of the 2017 IEEE international conference on communication and signal processing, ICCSP 2017, pp 2047–2050

Balazs JA, Velásquez JD (2016) Opinion mining and information fusion: a survey. Inf Fusion 27:95–110

Baly R, Hajj H, Habash N, Shaban KB, El-Hajj W (2017) A sentiment treebank and morphologically enriched recursive deep models for effective sentiment analysis in Arabic. ACM Trans Asian Low-Resour Lang Inf Process 16(4):23

Beigi G, Maciejewski R, Liu H (2016) an overview of sentiment analysis in social media and its applications in disaster relief. Stud Comput Intell 639:313–340

Bhardwaj A, Narayan Y, Vanraj P, Dutta M (2015) Sentiment analysis for indian stock market prediction using sensex and nifty. In: Procedia computer science, vol 70, pp 85–91

Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. Annu Meet Comput Linguist 45(1):440

Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8

Borth D, Ji R, Chen T, Breuel T, Chang S-F (2013) Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: Proceedings of 21st ACM international conference on multimedia—MM’13, pp 223–232

Brody S, Elhadad N (2010) An unsupervised aspect-sentiment model for online reviews. In: The 2010 annual conference of the North American chapter of the Association for Computational Linguistics, pp 804–812

Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102–107

Campos V, Salvador A, Jou B, Giró-i-nieto X (2015) Diving deep into sentiment: understanding fine-tuned CNNs for visual sentiment prediction. In: Proceedings of the 1st international workshop on affect and sentiment in multimedia. ACM, pp 57–62

Cao K, Rei M (2016) A joint model for word embedding and word morphology. In: Proceedings of the 1st workshop on representation learning for NLP, pp 18–26

Chachra A, Mehndiratta P, Gupta M (2017) Sentiment analysis of text using deep convolution neural networks. In: Tenth international conference on contemporary computing, pp 1–6

Chandankhede C, Devle P, Waskar A, Chopdekar N, Patil S (2016) ISAR: implicit sentiment analysis of user reviews. In: International conference on computing, analytics and security trends (CAST), College of Engineering Pune, India, pp 357–361

Chaturvedi I, Cambria E, Welsch RE, Herrera F (2018) Distinguishing between facts and opinions for sentiment analysis: survey and challenges. Inf Fusion 44:65–77

Chen M (2017) Multimodal sentiment analysis with word-level fusion and reinforcement learning. In: Proceedings of the 19th ACM international conference on multimodal interaction. ACM, pp 163–171

Chen Z, Qian T (2019) Transfer capsule network for aspect level sentiment classification. In: Proceedings oft he 57th annual meeting of the Association for Computational Linguistics, pp 547–556

Chen X, Wang Y, Liu Q (2017a) Visual and textual sentiment analysis using deep fusion convolutional neural networks. arXiv preprint arXiv:1711.07798

Chen T, Xu R, He Y, Wang X (2017b) Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst Appl 72:221–230

Cheng J, Zhao S, Zhang J, King I, Zhang X, Wang H (2017c) Aspect-level sentiment classification with HEAT (hierarchical attention) network. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 97–106

Chen F, Ji R, Su J, Cao D, Gao Y (2018) Predicting microblog sentiments via weakly supervised multimodal deep learning. IEEE Trans Multimed 20(4):997–1007

Chen B et al (2019) Embedding logic rules into recurrent neural networks. IEEE Access 7:14938–14946

Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning. ACM, pp 160–167

Day MY, Da Lin Y (2017) Deep learning for sentiment analysis on google play consumer review. In: Proceedings of 2017 IEEE international conference on information reuse and integration, IRI, pp 382–388

Do HH, Prasad PWC, Maag A, Alsadoon A (2019) Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst Appl 118:272–299

Donnelly J, Roegiest A (2019) On interpretability and feature representations: an analysis of the sentiment neuron. In: European conference on information retrieval. Springer, Cham, pp 795–802

Dragoni M, Petrucci G (2017) A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans Affect Comput 8(4):457–470

Dragoni M, Tettamanzi AGB, Pereira CDC (2016) DRANZIERA: an evaluation protocol for multi-domain opinion mining. In: Tenth international conference on language resources and evaluation, LREC, pp 267–272

Du C et al (2019a) Investigating capsule network and semantic feature on hyperplanes for text classification. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 456–465

Du C et al (2019b) Capsule network with interactive attention for aspect-level sentiment classification. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 5492–5501

Du Y, Zhao X, He M, Guo W (2019c) A novel capsule based hybrid neural network for sentiment classification. IEEE Access 7:39321–39328

Du J, Gui L, He Y, Xu R, Wang X (2019d) Convolution-based neural attention with applications to sentiment classification. IEEE Access 7:2169–3536

Dufourq E, Bassett BA (2017) EDEN: evolutionary deep networks for efficient machine learning. In: IEEE pattern recognition association of South Africa and robotics and mechatronics international conference, pp 110–115

Facebook Statistics (2019). https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/

Fernández-Gavilanes M, Alvarez-López T, Juncal-Martínez J, Costa-Montenegro E, González-Castã FJ (2015) GTI: an unsupervised approach for sentiment analysis in twitter. In: Proceedings of 9th international workshop on semantic evaluation (SemEval 2015), pp 533–538

Fernández-Gavilanes M, Álvarez-López T, Juncal-Martínez J, Costa-Montenegro E, Javier González-Castaño F (2016) Unsupervised method for sentiment analysis in online texts. Expert Syst Appl 58:57–75

Gerber MS (2014) Predicting crime using Twitter and kernel density estimation. Decis Support Syst 61(1):115–125

Ghosh R, Ravi K, Ravi V (2017) A novel deep learning architecture for sentiment classification. In: 3rd International conference on recent advances in information technology|RAIT-2016|, pp 3–8

Giachanou A, Crestani F (2016) Like it or not: a survey of twitter sentiment analysis methods. ACM Comput Surv 49(2):28:3–28:40

Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224 N Proj Rep Stanf 1(12):1–6

Hafez G, Ismail R, Karam O (2017) Temporal sentiment analysis and time tags for opinions. In: The 8th IEEE international conference on intelligent computing and information systems (ICICIS 2017), pp 373–378

Hakak NM, Mohd M, Kirmani M, Mohd M (2017) Emotion analysis: a survey. In: International conference on computer, communications and electronics, COMPTELIX 2017, pp 397–402

Halin AA (2017) The importance of multimodality in sarcasm detection for sentiment analysis. In: IEEE 15th student conference on research and development (SCOReD), pp 56–60

Hao Y, Mu T, Hong R, Wang M, Liu X, Goulermas JY (2019) Cross-domain sentiment encoding through stochastic word embedding. IEEE Trans Knowl Data Eng 1–15

Haque TU, Saber NN, Shah FM (2018) Sentiment analysis on large scale amazon product reviews. In: IEEE international conference on innovative research and development (ICIRD), pp 1–6

Haselmayer M, Jenny M (2017) Sentiment analysis of political communication: combining a dictionary approach with crowdcoding. Qual Quant 51(6):2623–2646

Hassan A, Mahmood A (2017a) Efficient deep learning model for text classification based on recurrent and convolutional layers. In: 16th IEEE international conference on machine learning and applications (ICMLA), pp 1108–1113

Hassan A, Mahmood A (2017b) Deep learning approach for sentiment analysis of short texts. In: 3rd International conference on control, automation and robotics (ICCAR), pp 705–710

Hassan A, Mahmood A (2018) Convolutional recurrent deep learning model for sentence classification. IEEE Access 6:2169–3536

Hedge Y, Padma SK (2017) Sentiment analysis using random forest ensemble for mobile product reviews in Kannada. In: IEEE 7th international advance computing conference

Hemmatian F, Sohrabi M (2017) A survey on classification techniques for opinion mining and sentiment analysis. Artif Intell Rev 2017:1–51

Hogenboom A, Heerschop B, Frasincar F, Kaymak U, De Jong F (2014) Multi-lingual support for lexicon-based sentiment analysis guided by semantics. Decis Support Syst 62:43–53

Huang Q, Chen R, Zheng X, Dong Z (2017) Deep sentiment representation based on CNN and LSTM. In: Proceedings of 2017 international conference on green informatics, ICGI 2017, pp 30–33

Huang W, Rao G, Feng Z, Cong Q (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308:49

Huang F, Zhang X, Zhao Z, Xu J, Li Z (2019) Image-text sentiment analysis via deep multimodal attentive fusion. Knowl Based Syst 167:26–37

Islam J, Zhang Y (2016) Visual sentiment analysis for social images using transfer learning approach. In: IEEE international conferences on big data and cloud computing (BDCloud), social computing and networking (SocialCom), sustainable computing and communications (SustainCom) (BDCloud-SocialCom-SustainCom), pp 124–130

Jaffali S, Jamoussi S, Ben Hamadou A (2014) Grouping like-minded users based on text and sentiment analysis. In: International conference on computational collective intelligence. Springer, Cham, pp 83–93

Jiang M, Wang J, Lan M, Wu Y (2014) An effective gated and attention-based neural network model for fine-grained financial target-dependent sentiment analysis. Int Conf Knowl Sci Eng Manag 214:42–54

Jin Y, Zhang H, Du D (2017) Improving deep belief networks via delta rule for sentiment classification. In: Proceedings of 2016 IEEE 28th international conference on tools with artificial intelligence, ICTAI 2016, pp 410–414

Jou B, Chen T, Pappas N, Redi M, Topkara M, Chang SF (2015) Visual affect around the world: a large-scale multilingual visual sentiment ontology. In: Proceedings of the 23rd ACM international conference on multimedia. ACM, pp 159–168

Kharde VA, Sonawane SS (2016) Sentiment analysis of twitter data: a survey of techniques. Int J Comput Appl 139(11):975–8887

Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), October 25–29, Doha, Qatar, pp 1746–1751

Kim J, Jang S, Park E, Choi S (2019) Text classification using capsules. Neurocomputing 118:247–261

Kiritchenko S, Zhu X, Mohammad S (2014) Sentiment analysis of short informal texts. J Artif Intell Res 50:723–762

Kraus M, Feuerriegel S (2019) Sentiment analysis based on rhetorical structure theory: learning deep neural networks from discourse trees. Expert Syst Appl 118:65–79

Krejzl P, Hourová B, Steinberger J (2017) Stance detection in online discussions. arXiv preprint arXiv:1701.00504

Kumari S, Babu CN (2017) Real time analysis of social media data to understand people emotions towards national parties. In: 8th International conference on computing, communication and networking technologies (ICCCNT), pp 1–6

Kušen E, Strembeck M (2017) Politics, sentiments, and misinformation: an analysis of the Twitter discussion on the 2016 Austrian presidential elections. Online Soc Netw Media 5:37–50

Lakkaraju H, Socher R, Manning CD (2014) Aspect specific sentiment analysis using hierarchical deep learning. In: NIPS workshop on deep learning and representation learning, pp 1–9

Lee G, Jeong J, Seo S, Kim CY, Kang P (2018) Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network. Knowl Based Syst 152:70–82

Li H, Xu H (2019) Video-based sentiment analysis with hvnLBP-TOP feature and bi-LSTM. In: Association for the Advancement of Artificial Intelligence (AAAI)

Li C, Xu B, Wu G, He S, Tian G, Hao H (2014) Recursive deep learning for sentiment analysis over social data. In: Proceedings of 2014 IEEE/WIC/ACM international joint conference on web intelligence and intelligent agent technology–workshops, WI-IAT 2014, pp 180–185

Li Y, Pan Q, Yang T, Wang S, Tang J, Cambria E (2017a) Learning word representations for sentiment analysis. Cognit Comput 9(6):843–851

Li C, Guo X, Mei Q (2017b) Deep memory networks for attitude identification. In: Proceedings of the tenth ACM international conference on web search and data mining, WSDM 2017, Cambridge, United Kingdom, pp 671–680

Li B, Cheng Z, Xu Z, Ye W, Lukasiewicz T, Zhang S (2019) Long text analysis using sliced recurrent neural networks with breaking point information enrichment. In: Proceedings of the 2019 IEEE international conference on acoustics, speech and signal processing, ICASSP 2019, Brighton, UK, vol 124, pp 51–60

Liu B (2010) Sentiment analysis and subjectivity. In: Handbook of natural language processing, vol 1, pp 1–38

Liu Y, Bi J-W, Fan Z-P (2017) Ranking products through online reviews: a method based on sentiment analysis technique and intuitionistic fuzzy set theory. Inf Fusion 36:149–161

Lo S, Cambria E, Chiong R, Cornforth D (2017) Multilingual sentiment analysis: from formal to informal and scarce resource languages. Artif Intell Rev 48(4):499–527

Luo Z, Xu H, Chen F (2019) Audio sentiment analysis by heterogeneous signal features learned from utterance-based parallel neural network. In: Proceedings of the AAAI-19 workshop on affective content analysis, Honolulu, USA

Ma Y, Peng H, Khan T, Cambria E, Hussain A (2018) Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cognit Comput 10:639–650

Ma X, Zeng J, Peng L, Fortino G, Zhang Y (2019) Modeling multi-aspects within one opinionated sentence simultaneously for aspect-level sentiment analysis. Futur Gener Comput Syst 93:304–311

Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of 49th annual meeting of the Association for Computational Linguistics: Human Language and Technology, pp 142–150

Manshu Y, Bing W (2019) Adding prior knowledge in hierarchical attention neural network for cross domain sentiment classification. IEEE Access 7:2169–3536

Marcheggiani D, Oscar T (2014) Hierarchical multi-label conditional random fields for aspect-oriented opinion mining. In: European conference on information retrieval. Springer, Cham, pp 273–285

Marelli M, Bentivogli L, Baroni M, Bernardi R, Menini S, Zamparelli R (2014) SemEval-2014 task 1: evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), no 1, pp 1–8

Mataoui M, Hacine T, Tellache I, Bakhtouchi A, Zelmati O (2018) A new syntax-based aspect detection approach for sentiment analysis in Arabic reviews. In: 2nd international conference on natural language and speech processing (ICNLSP)

Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

Moghaddam S, Ester M (2010) Opinion digger: an unsupervised opinion miner from unstructured product reviews. In: Proceedings of the 19th ACM international conference on information and knowledge management, pp 1825–1828

Mohammad SM, Kiritchenko S, Zhu X (2013) NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the seventh international workshop on semantic evaluation, pp 321–327

Montejo-Ráez A, Díaz-Galiano MC, Martínez-Santiago F, Ureña-Løpez LA (2014) Crowd explicit sentiment analysis. Knowl Based Syst 69(1):134–139

Morency L-P, Mihalcea R, Doshi P (2011) Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of 13th international conference on multimodal interfaces—ICMI’11, pp 169–176

Nakov P, Rosenthal S, Kozareva Z, Stoyanov V, Ritter A, Wilson T (2013) SemEval-2013 task 2: sentiment analysis in Twitter. In: Joint conference on lexical and computational semantics (SEM). Volume 2: Proceedings of the international workshop on semantic evaluation (SemEval 2013), vol 2, no SemEval, pp 312–320

Napitu F, Bijaksana MA, Trisetyarso A, Heryadi Y (2017) Twitter opinion mining predicts broadband internet’s customer churn rate. In: IEEE international conference on cybernetics and computational intelligence (CyberneticsCom), pp 141–146

Narr S, Hülfenhaus M, Albayrak S (2012) Language-independent twitter sentiment analysis. In: Workshop on knowledge discovery, data mining and machine learning (KDML-2012), Dortmund, Germany

Nogueira C, Santos D, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of 25th international conference on computational linguistics, pp 69–78

Nozza D, Fersini E, Messina E (2016) Deep learning and ensemble methods for domain adaptation. In: IEEE 28th international conference on tools with artificial intelligence deep, pp 184–189

Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, p 271

Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Empirical methods in natural language processing (EMNLP), vol 10, pp 79–86

Peñalver-Martinez I et al (2014) Feature-based opinion mining through ontologies. Expert Syst Appl 41(13):5995–6008

Peng H, Ma Y, Li Y, Cambria E (2018) Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowl Based Syst 148:55–65

Pennington J, Socher R, Manning CD (2014) GloVe: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

P’erez-Rosas V, Mihalcea R, Morency L (2013) Utterance-level multimodal sentiment analysis. In: Proceedings of the 51st annual meeting of the Association for Computational Linguistics, pp 973–982

Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th international workshop on semantic evaluation, pp. 27–35

Pontiki M et al (2016) SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation, pp 342–349

Poria S, Cambria E, Howard N, Bin Huang G, Hussain A (2016a) Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174:50–59

Poria S, Chaturvedi I, Cambria E, Hussain A (2016b) Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: Proceedings-IEEE 16th international conference on data mining, ICDM, pp 439–448

Poria S, Cambria E, Gelbukh A (2016c) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst 108:42–49

Poria S, Cambria E, Bajpai R, Hussain A (2017a) A review of affective computing: from unimodal analysis to multimodal fusion. Inf Fusion 37:98–125

Poria S, Cambria E, Hazarika D, Majumder N, Zadeh A, Morency L-P (2017b) 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), pp 873–883

Poria S, Cambria E, Hazarika D, Mazumder N, Zadeh A, Morency LP (2017c) Multi-level multiple attentions for contextual multimodal sentiment analysis. In: Proceedings of IEEE international conference on data mining, ICDM, pp 1033–1038

Poria S, Majumder N, Hazarika D, Cambria E, Gelbukh A, Hussain A (2018) Multimodal sentiment analysis: addressing key issues and setting up the baselines. IEEE Intell Syst 33(6):17–25

Radianti J, Hiltz SR, Labaka L (2016) An overview of public concerns during the recovery period after a major earthquake: Nepal twitter analysis. In: Proceedings of the 49th annual Hawaii international conference on system sciences, pp 136–145

Ragini JR, Anand PMR, Bhaskar V (2018) Big data analytics for disaster response and recovery through sentiment analysis. Int J Inf Manag 42(May):13–24

Rain C (2013) Sentiment analysis in Amazon reviews using probabilistic machine learning. Swarthmore College

Rana TA, Cheah Y-N (2016) Aspect extraction in sentiment analysis: comparative analysis and survey. Artif Intell Rev 46(4):459–483

Rana R et al (2016) Gated recurrent unit (GRU) for emotion classification from noisy speech. arXiv preprint arXiv:1612.07778

Rani S, Kumar P (2019) A journey of Indian languages over sentiment analysis: a systematic review. Artif Intell Rev 52(2):1415–1462

Rao T, Srivastava S (2012) Analyzing stock market movements using Twitter sentiment analysis. In: ASONAM’12 Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012), pp 119–123

Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications, vol 89. Elsevier, Amsterdam

Ren Y, Zhang Y, Zhang M, Ji D (2016) Context-sensitive twitter sentiment classification using neural network. In: Proceedings of the 30th conference on artificial intelligence (AAAI 2016), pp 215–221

Rosenfeld R, Fornango R (2008) The impact of economic conditions on robbery and property crime: the role of consumer sentiment. Criminology 45(4):735–769

Roy K, Kohli D, Kumar R, Sahgal R, Yu W-B (2017) Sentiment analysis of Twitter data for demonetization in India: a text mining approach. Inf Syst 18(4):9–15

Ruangkanokmas P, Achalakul T, Akkarajitsakul K (2016) Deep belief networks with feature selection for sentiment classification. In: 7th International conference on intelligent systems, modelling and simulation (ISMS), pp 9–14

Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in neural information processing systems, pp 3856–3866

Saif H, Fernandez M, He Y, Alani H (2013) Evaluation datasets for Twitter sentiment analysis A survey and a new dataset, the STS-Gold. In: Proceedings of 1st ESSEM work, Turin, Italy, vol 1096, pp 9–21

Sánchez-rada JF, Iglesias CA (2019) Social context in sentiment analysis: formal definition, overview of current trends and framework for comparison. Inf Fusion 52:344–356

Shah RR, Yu Y, Verma A, Tang S, Shaikh AD, Zimmermann R (2016) Leveraging multimodal information for event summarization and concept-level sentiment analysis. Knowl Based Syst 108:102–109

Shaikh T, Deshpande D (2016) Feature selection methods in sentiment analysis and sentiment classification of amazon product reviews. Int J Comput Trends Technol 36(4):225–230

Shi S, Zhao M, Guan J, Li Y, Huang H (2017) A hierarchical LSTM model with multiple features for sentiment analysis of sina weibo texts. In: International conference on Asian language processing (IALP), pp 379–382

Singh P, Dave A, Dar K (2017) Demonetization: sentiment and retweet analysis. In: International conference on inventive computing and informatics (ICICI 2017), pp 894–899

Singh P, Sawhney RS, Kahlon KS (2018) Sentiment analysis of demonetization of 500 & 1000 rupee banknotes by Indian government. ICT Express 4:124

Singhal P, Bhattacharyya P (2016) Sentiment analysis and deep learning: a survey. In: Center for Indian Language Technology, Indian Institute of Technology, Bombay

Singla Z, Randhawa S, Jain S (2017) Statistical and sentiment analysis of consumer product reviews. In: 8th International conference on computing, communication and networking technologies (ICCCNT), pp 1–6

Socher R, Perelygin A, Wu J (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of 2013 conference on empirical methods in natural language processing, pp 1631–1642

Soleymani M, Garcia D, Jou B, Schuller B, Chang SF, Pantic M (2017) A survey of multimodal sentiment analysis. Image Vis Comput 65:3–14

Song K, Yao T, Ling Q, Mei T (2018) Boosting image sentiment analysis with visual attention. Neurocomputing 312:218–228

Stojanovski D, Strezoski G, Madjarov G, Dimitrovski I (2015) Twitter sentiment analysis using deep convolutional neural network. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 726–737

Stojanovski D, Strezoski G, Madjarov G, Dimitrovski I, Chorbev I (2018) Deep neural network architecture for sentiment analysis and emotion identification of Twitter messages. Multimed Tools Appl 77(24):32213–32242

Sun X, Li C, Ren F (2016) Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features. Neurocomputing 210:227–236

Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075

Tang D, Qin B, Liu T (2015a) Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 conference on empirical methods in natural language processing

Tang D, Qin B, Liu T (2015b) 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, vol 1, pp 1014–1023

Tay Y, Tuan LA, Hui SC (2017) Dyadic memory networks for aspect-based sentiment analysis. In: Proceedings of 2017 ACM conference on information and knowledge management—CIKM’17, pp 107–116

Trofimova TP, Pushin AN, Lys YI, Fedoseev VM (2016) Robust visual-textual sentiment analysis: when attention meets tree-structured recursive neural networks. In: Proceedings of the 2016 ACM on multimedia conference, pp 1008–1017

Twitter Statistics (2019). https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/

Uysal AK, Murphey YL (2017) Sentiment classification: feature selection based approaches versus deep learning. In: IEEE international conference on computer and information technology (CIT), pp 23–30

van Hee C, Lefever E, Hoste V (2018) Exploring the fine-grained analysis and automatic detection of irony on Twitter. Lang Resour Eval 1–25

Vateekul P, Koomsubha T (2016) A study of sentiment analysis using deep learning techniques on Thai Twitter data. In: 13th International joint conference on computer science and software engineering (JCSSE), pp 1–6

Verma S, Saini M, Sharan A (2018) Deep sequential model for review rating prediction. In: 10th international conference on contemporary computing, IC3 2017, vol 2018, pp 1–6

Wang H, Can D, Kazemzadeh A, Bar F, Narayanan S (2012a) A system for real-time twitter sentiment analysis of 2012 U.S. presidential election cycle. In: Proceedings of the 50th annual meeting of the Association for Computational Linguistics, pp 115–120

Wang X, Gerber MS, Brown DE (2012b) Automatic crime prediction using events extracted from twitter posts

Wang Y, Huang M, Zhu X, Zhao L (2016a) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 606–615

Wang H, Meghawat A, Morency L, Xing EP (2016b) Select-additive learning: improving generalization in multimodal sentiment analysis. arXiv preprint arXiv:1609.05244

Wang J, Fu J, Xu Y, Mei T (2016c) Beyond object recognition: visual sentiment analysis with deep coupled adjective and noun neural networks. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence (IJCAI-16), pp 3484–3490

Wang X, Li Y, Xu P (2018a) A hybrid BLSTM-C neural network proposed for chinese text classification. In: IEEE sixth international conference on advanced cloud and big data (CBD), pp 311–315

Wang Y, Sun A, Han J, Liu Y, Zhu X (2018b) Sentiment analysis by capsules. In: Proceedings of the 2018 world wide web conference, pp 1165–1174

Wang J, Peng B, Zhang X (2018c) Using a stacked residual LSTM model for sentiment intensity prediction. Neurocomputing 322:93–101

Wang Y, Sun A, Huang M, Zhu X (2019) Aspect-level sentiment analysis using AS-capsules. In: The world wide web conference. ACM, pp 2033–2044

Whitehead M, Yaeger L (2009) Building a general purpose cross-domain sentiment mining model. In: WRI world congress on computer science and information engineering, CSIE, vol 4, pp 472–476

Wu D, Chi M (2017) Long short-term memory with quadratic connections in recursive neural networks for representing compositional semantics. IEEE Access 5:16077

Wu D, Cui Y (2018) Disaster early warning and damage assessment analysis using social media data and geo-location information. Decis Support Syst 111:48

Xiong S, Wang K, Ji D, Wang B (2018a) A short text sentiment-topic model for product reviews. Neurocomputing 297:94–102

Xiong S, Lv H, Zhao W, Ji D (2018b) Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings. Neurocomputing 275:2459–2466

Xu F, Kešelj V (2014) Collective sentiment mining of microblogs in 24-hour stock price movement prediction. In: 16th IEEE conference on business informatics, CBI 2014, vol 2, pp 60–67

Xu K, Liao SS, Li J, Song Y (2011) Mining comparative opinions from customer reviews for competitive Intelligence. Decis Support Syst 50(4):743–754

Xu J, Tao Y, Lin H, Zhu R, Yan Y (2017) Exploring controversy via sentiment divergences of aspects in reviews. In: IEEE pacific visualization symposium (PacificVis), pp 240–249

Yanagimoto H, Shimada M, Yoshimura A (2013) Document similarity estimation for sentiment analysis using neural network. In: IEEE/ACIS 12th international conference on computer and information science (ICIS). IEEE, pp 105–110

Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Conference of the North American chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1480–1489

Yang M, Qu Q, Chen X, Guo C, Shen Y, Lei K (2018) Feature-enhanced attention network for target-dependent sentiment classification. Neurocomputing 307:91–97

Yang C, Zhang H, Jiang B, Li K (2019a) Aspect-based sentiment analysis with alternating coattention networks. Inf Process Manag 56(3):463–478

Yang M, Zhao W, Chen L, Qu Q, Zhao Z, Shen Y (2019b) Investigating the transferring capability of capsule networks for text classification. Neural Netw 118:247–261

Yelp Dataset (2014)

Yoo SY, Song JI, Jeong OR (2018) Social media contents based sentiment analysis and prediction system. Expert Syst Appl 105:102–111

You Q, Luo J, Jin H, Yang J (2015) Robust image sentiment analysis using progressively trained and domain transferred deep networks. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence, pp 381–388

You Q, Luo J, Jin H, Yang J (2016) Cross-modality consistent regression for joint visual-textual sentiment analysis of social multimedia. In: Proceedings of the ninth ACM international conference on web search and data mining, pp 13–22

Yu H, Gui L, Madaio M, Ogan A, Cassell J (2017) Temporally selective attention model for social and affective state recognition in multimedia content. In: Proceedings of the 2017 ACM on multimedia conference. ACM, pp 1743–1751

Yu L, Wang J, Lai KR, Zhang X (2018) Refining word embeddings using intensity scores for sentiment analysis. IEEE/ACM Trans Audio Speech Lang Process 26(3):671–681

Yu J, Jiang J, Xia R (2019) Global inference for aspect and opinion terms co-extraction based on multi-task neural networks. IEEE/ACM Trans Audio Speech Lang Process 27(1):168–177

Yuan M, Tang H, Li H (2014) Real-time keypoint recognition using restricted boltzmann machine. IEEE Trans Neural Netw Learn Syst 25(11):2119–2126

Yuan Z, Wu S, Wu F, Liu J, Huang Y (2018) Domain attention model for multi-domain sentiment classification. Knowl Based Syst 155:1–10

Zadeh A, Zellers R, Pincus E, Morency L (2016) MOSI: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos. IEEE Intell Syst

Zhang J, Chow C (2019) MOCA: multi-objective, collaborative, and attentive sentiment analysis. IEEE Access 7:10927–10936

Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820

Zhang Z, Ye Q, Zhang Z, Li Y (2011) Sentiment classification of internet restaurant reviews written in Cantonese. Expert Syst Appl 38(6):7674–7682

Zhang Z, Zou Y, Gan C (2017) Textual sentiment analysis via three different attention convolutional neural networks and cross-modality consistent regression. Neurocomputing 275:1407

Zhang Y et al (2018a) A quantum-inspired multimodal sentiment analysis framework. Theor Comput Sci 752:21

Zhang Z, Wang L, Zou Y, Gan C (2018b) The optimally designed dynamic memory networks for targeted sentiment classification. Neurocomputing 309:36

Zhang B, Xu X, Yang M, Chen X, Ye AY (2018c) Cross-domain sentiment classification by capsule network with semantic rules. IEEE Access 6:58284–58294

Zhao L, Huang M, Chen H, Cheng J, Zhu X (2014) Clustering aspect-related phrases by leveraging sentiment distribution consistency. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1614–1623

Zhao W et al (2017) Weakly-supervised deep embedding for product review sentiment analysis. IEEE Trans Knowl Data Eng 4347:1–12

Zhao W, Peng H, Eger S, Cambria E, Yang M (2019) Towards scalable and reliable capsule networks for challenging NLP applications. In: Proceedings of the 57th annual meeting of the Association for Computational Linguistics, pp 1549–1559

Zhou K, Zeng J, Liu Y, Zou F (2018) Deep sentiment hashing for text retrieval in social CIoT. Futur Gener Comput Syst 86:362

Zvarevashe K, Olugbara OO (2018) A framework for sentiment analysis with opinion mining of hotel reviews. In: Conference on information communications technology and society (ICTAS), pp 1–4