{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:33:53Z","timestamp":1781613233715,"version":"3.54.5"},"reference-count":205,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T00:00:00Z","timestamp":1575244800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T00:00:00Z","timestamp":1575244800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2020,8]]},"DOI":"10.1007\/s10462-019-09794-5","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T16:03:44Z","timestamp":1576512224000},"page":"4335-4385","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":714,"title":["Sentiment analysis using deep learning architectures: a review"],"prefix":"10.1007","volume":"53","author":[{"given":"Ashima","family":"Yadav","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1026-0047","authenticated-orcid":false,"given":"Dinesh Kumar","family":"Vishwakarma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,12,2]]},"reference":[{"issue":"3","key":"9794_CR1","doi-asserted-by":"crossref","first-page":"12:1","DOI":"10.1145\/1361684.1361685","volume":"26","author":"A Abbasi","year":"2008","unstructured":"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\u201312:34","journal-title":"ACM Trans Inf Syst"},{"key":"9794_CR2","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.eswa.2018.05.010","volume":"109","author":"A Abdi","year":"2018","unstructured":"Abdi A, Shamsuddin SM, Hasan S (2018) Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment. Expert Syst Appl 109:66\u201385","journal-title":"Expert Syst Appl"},{"issue":"4","key":"9794_CR3","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1016\/j.ipm.2019.02.018","volume":"56","author":"A Abdi","year":"2019","unstructured":"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\u20131259","journal-title":"Inf Process Manag"},{"key":"9794_CR4","doi-asserted-by":"crossref","unstructured":"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\u201323","DOI":"10.1109\/BCD.2019.8885108"},{"key":"9794_CR5","unstructured":"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\u2013649"},{"key":"9794_CR6","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.jocs.2017.11.006","volume":"27","author":"M Al-Smadi","year":"2017","unstructured":"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\u2019 reviews. J Comput Sci 27:386","journal-title":"J Comput Sci"},{"key":"9794_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-018-0799-4","author":"M Al-Smadi","year":"2018","unstructured":"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","journal-title":"Int J Mach Learn Cybern"},{"key":"9794_CR8","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.18653\/v1\/P19-2045"},{"issue":"6","key":"9794_CR9","first-page":"252","volume":"4","author":"K Arun","year":"2017","unstructured":"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\u2013258","journal-title":"Int J Comput Eng Res Trends"},{"key":"9794_CR10","doi-asserted-by":"crossref","unstructured":"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\u20131710","DOI":"10.1109\/ICACCI.2015.7275858"},{"key":"9794_CR11","doi-asserted-by":"crossref","unstructured":"Baccianella S, Esuli A, Sebastiani F (2009) Multi-facet rating of product reviews. In: European conference on information retrieval. Springer, Berlin, pp 461\u2013472","DOI":"10.1007\/978-3-642-00958-7_41"},{"key":"9794_CR12","unstructured":"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\u201910), pp 2200\u20132204"},{"key":"9794_CR13","doi-asserted-by":"crossref","unstructured":"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\u20132050","DOI":"10.1109\/ICCSP.2017.8286763"},{"key":"9794_CR14","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.inffus.2015.06.002","volume":"27","author":"JA Balazs","year":"2016","unstructured":"Balazs JA, Vel\u00e1squez JD (2016) Opinion mining and information fusion: a survey. Inf Fusion 27:95\u2013110","journal-title":"Inf Fusion"},{"issue":"4","key":"9794_CR15","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1145\/3086576","volume":"16","author":"R Baly","year":"2017","unstructured":"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","journal-title":"ACM Trans Asian Low-Resour Lang Inf Process"},{"key":"9794_CR16","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/978-3-319-30319-2_13","volume":"639","author":"G Beigi","year":"2016","unstructured":"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\u2013340","journal-title":"Stud Comput Intell"},{"key":"9794_CR17","doi-asserted-by":"crossref","unstructured":"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\u201391","DOI":"10.1016\/j.procs.2015.10.043"},{"issue":"1","key":"9794_CR18","first-page":"440","volume":"45","author":"J Blitzer","year":"2007","unstructured":"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","journal-title":"Annu Meet Comput Linguist"},{"issue":"1","key":"9794_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","volume":"2","author":"J Bollen","year":"2011","unstructured":"Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1\u20138","journal-title":"J Comput Sci"},{"key":"9794_CR20","doi-asserted-by":"crossref","unstructured":"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\u2014MM\u201913, pp 223\u2013232","DOI":"10.1145\/2502081.2502282"},{"key":"9794_CR21","unstructured":"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\u2013812"},{"issue":"2","key":"9794_CR22","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MIS.2016.31","volume":"31","author":"E Cambria","year":"2016","unstructured":"Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102\u2013107","journal-title":"IEEE Intell Syst"},{"key":"9794_CR23","doi-asserted-by":"crossref","unstructured":"Campos V, Salvador A, Jou B, Gir\u00f3-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\u201362","DOI":"10.1145\/2813524.2813530"},{"key":"9794_CR24","doi-asserted-by":"crossref","unstructured":"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\u201326","DOI":"10.18653\/v1\/W16-1603"},{"key":"9794_CR25","doi-asserted-by":"crossref","unstructured":"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\u20136","DOI":"10.1109\/IC3.2017.8284327"},{"key":"9794_CR26","doi-asserted-by":"crossref","unstructured":"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\u2013361","DOI":"10.1109\/CAST.2016.7914994"},{"key":"9794_CR27","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.inffus.2017.12.006","volume":"44","author":"I Chaturvedi","year":"2018","unstructured":"Chaturvedi I, Cambria E, Welsch RE, Herrera F (2018) Distinguishing between facts and opinions for sentiment analysis: survey and challenges. Inf Fusion 44:65\u201377","journal-title":"Inf Fusion"},{"key":"9794_CR28","doi-asserted-by":"crossref","unstructured":"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\u2013171","DOI":"10.1145\/3136755.3136801"},{"key":"9794_CR29","doi-asserted-by":"crossref","unstructured":"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\u2013556","DOI":"10.18653\/v1\/P19-1052"},{"key":"9794_CR30","doi-asserted-by":"crossref","unstructured":"Chen X, Wang Y, Liu Q (2017a) Visual and textual sentiment analysis using deep fusion convolutional neural networks. arXiv preprint arXiv:1711.07798","DOI":"10.1109\/ICIP.2017.8296543"},{"key":"9794_CR31","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.eswa.2016.10.065","volume":"72","author":"T Chen","year":"2017","unstructured":"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\u2013230","journal-title":"Expert Syst Appl"},{"key":"9794_CR32","doi-asserted-by":"crossref","unstructured":"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\u2013106","DOI":"10.1145\/3132847.3133037"},{"issue":"4","key":"9794_CR33","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1109\/TMM.2017.2757769","volume":"20","author":"F Chen","year":"2018","unstructured":"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\u20131007","journal-title":"IEEE Trans Multimed"},{"key":"9794_CR34","doi-asserted-by":"crossref","first-page":"14938","DOI":"10.1109\/ACCESS.2019.2892140","volume":"7","author":"B Chen","year":"2019","unstructured":"Chen B et al (2019) Embedding logic rules into recurrent neural networks. IEEE Access 7:14938\u201314946","journal-title":"IEEE Access"},{"key":"9794_CR35","doi-asserted-by":"crossref","unstructured":"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\u2013167","DOI":"10.1145\/1390156.1390177"},{"key":"9794_CR36","unstructured":"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\u2013388"},{"key":"9794_CR37","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.eswa.2018.10.003","volume":"118","author":"HH Do","year":"2019","unstructured":"Do HH, Prasad PWC, Maag A, Alsadoon A (2019) Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst Appl 118:272\u2013299","journal-title":"Expert Syst Appl"},{"key":"9794_CR38","doi-asserted-by":"crossref","unstructured":"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\u2013802","DOI":"10.1007\/978-3-030-15712-8_55"},{"issue":"4","key":"9794_CR39","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TAFFC.2017.2717879","volume":"8","author":"M Dragoni","year":"2017","unstructured":"Dragoni M, Petrucci G (2017) A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans Affect Comput 8(4):457\u2013470","journal-title":"IEEE Trans Affect Comput"},{"key":"9794_CR40","unstructured":"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\u2013272"},{"key":"9794_CR41","doi-asserted-by":"crossref","unstructured":"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\u2013465","DOI":"10.18653\/v1\/D19-1043"},{"key":"9794_CR42","doi-asserted-by":"crossref","unstructured":"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\u20135501","DOI":"10.18653\/v1\/D19-1551"},{"key":"9794_CR43","doi-asserted-by":"crossref","first-page":"39321","DOI":"10.1109\/ACCESS.2019.2906398","volume":"7","author":"Y Du","year":"2019","unstructured":"Du Y, Zhao X, He M, Guo W (2019c) A novel capsule based hybrid neural network for sentiment classification. IEEE Access 7:39321\u201339328","journal-title":"IEEE Access"},{"key":"9794_CR44","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1109\/ACCESS.2018.2886583","volume":"7","author":"J Du","year":"2019","unstructured":"Du J, Gui L, He Y, Xu R, Wang X (2019d) Convolution-based neural attention with applications to sentiment classification. IEEE Access 7:2169\u20133536","journal-title":"IEEE Access"},{"key":"9794_CR45","doi-asserted-by":"crossref","unstructured":"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\u2013115","DOI":"10.1109\/RoboMech.2017.8261132"},{"key":"9794_CR46","unstructured":"Facebook Statistics (2019). https:\/\/www.statista.com\/statistics\/264810\/number-of-monthly-active-facebook-users-worldwide\/"},{"key":"9794_CR47","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Gavilanes M, Alvarez-L\u00f3pez T, Juncal-Mart\u00ednez J, Costa-Montenegro E, Gonz\u00e1lez-Cast\u00e3 FJ (2015) GTI: an unsupervised approach for sentiment analysis in twitter. In: Proceedings of 9th international workshop on semantic evaluation (SemEval 2015), pp 533\u2013538","DOI":"10.18653\/v1\/S15-2089"},{"key":"9794_CR48","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.eswa.2016.03.031","volume":"58","author":"M Fern\u00e1ndez-Gavilanes","year":"2016","unstructured":"Fern\u00e1ndez-Gavilanes M, \u00c1lvarez-L\u00f3pez T, Juncal-Mart\u00ednez J, Costa-Montenegro E, Javier Gonz\u00e1lez-Casta\u00f1o F (2016) Unsupervised method for sentiment analysis in online texts. Expert Syst Appl 58:57\u201375","journal-title":"Expert Syst Appl"},{"issue":"1","key":"9794_CR49","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.dss.2014.02.003","volume":"61","author":"MS Gerber","year":"2014","unstructured":"Gerber MS (2014) Predicting crime using Twitter and kernel density estimation. Decis Support Syst 61(1):115\u2013125","journal-title":"Decis Support Syst"},{"key":"9794_CR50","unstructured":"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\u20138"},{"issue":"2","key":"9794_CR51","doi-asserted-by":"crossref","first-page":"28:3","DOI":"10.1145\/2938640","volume":"49","author":"A Giachanou","year":"2016","unstructured":"Giachanou A, Crestani F (2016) Like it or not: a survey of twitter sentiment analysis methods. ACM Comput Surv 49(2):28:3\u201328:40","journal-title":"ACM Comput Surv"},{"issue":"12","key":"9794_CR52","first-page":"1","volume":"1","author":"A Go","year":"2009","unstructured":"Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224\u00a0N Proj Rep Stanf 1(12):1\u20136","journal-title":"CS224\u00a0N Proj Rep Stanf"},{"key":"9794_CR53","doi-asserted-by":"crossref","unstructured":"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\u2013378","DOI":"10.1109\/INTELCIS.2017.8260065"},{"key":"9794_CR54","doi-asserted-by":"crossref","unstructured":"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\u2013402","DOI":"10.1109\/COMPTELIX.2017.8004002"},{"key":"9794_CR55","unstructured":"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\u201360"},{"key":"9794_CR56","doi-asserted-by":"crossref","unstructured":"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\u201315","DOI":"10.1109\/TKDE.2019.2934464"},{"key":"9794_CR57","doi-asserted-by":"crossref","unstructured":"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\u20136","DOI":"10.1109\/ICIRD.2018.8376299"},{"issue":"6","key":"9794_CR58","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1007\/s11135-016-0412-4","volume":"51","author":"M Haselmayer","year":"2017","unstructured":"Haselmayer M, Jenny M (2017) Sentiment analysis of political communication: combining a dictionary approach with crowdcoding. Qual Quant 51(6):2623\u20132646","journal-title":"Qual Quant"},{"key":"9794_CR59","doi-asserted-by":"crossref","unstructured":"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\u20131113","DOI":"10.1109\/ICMLA.2017.00009"},{"key":"9794_CR60","doi-asserted-by":"crossref","unstructured":"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\u2013710","DOI":"10.1109\/ICCAR.2017.7942788"},{"key":"9794_CR61","first-page":"2169","volume":"6","author":"A Hassan","year":"2018","unstructured":"Hassan A, Mahmood A (2018) Convolutional recurrent deep learning model for sentence classification. IEEE Access 6:2169\u20133536","journal-title":"IEEE Access"},{"key":"9794_CR62","doi-asserted-by":"crossref","unstructured":"Hedge Y, Padma SK (2017) Sentiment analysis using random forest ensemble for mobile product reviews in Kannada. In: IEEE 7th international advance computing conference","DOI":"10.1109\/IACC.2017.0160"},{"key":"9794_CR63","first-page":"1","volume":"2017","author":"F Hemmatian","year":"2017","unstructured":"Hemmatian F, Sohrabi M (2017) A survey on classification techniques for opinion mining and sentiment analysis. Artif Intell Rev 2017:1\u201351","journal-title":"Artif Intell Rev"},{"key":"9794_CR64","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.dss.2014.03.004","volume":"62","author":"A Hogenboom","year":"2014","unstructured":"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\u201353","journal-title":"Decis Support Syst"},{"key":"9794_CR65","doi-asserted-by":"crossref","unstructured":"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\u201333","DOI":"10.1109\/ICGI.2017.45"},{"key":"9794_CR66","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.neucom.2018.02.082","volume":"308","author":"W Huang","year":"2018","unstructured":"Huang W, Rao G, Feng Z, Cong Q (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308:49","journal-title":"Neurocomputing"},{"key":"9794_CR67","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.knosys.2019.01.019","volume":"167","author":"F Huang","year":"2019","unstructured":"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\u201337","journal-title":"Knowl Based Syst"},{"key":"9794_CR68","doi-asserted-by":"crossref","unstructured":"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\u2013130","DOI":"10.1109\/BDCloud-SocialCom-SustainCom.2016.29"},{"key":"9794_CR69","doi-asserted-by":"crossref","unstructured":"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\u201393","DOI":"10.1007\/978-3-319-11289-3_9"},{"key":"9794_CR70","first-page":"42","volume":"214","author":"M Jiang","year":"2014","unstructured":"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\u201354","journal-title":"Int Conf Knowl Sci Eng Manag"},{"key":"9794_CR71","unstructured":"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\u2013414"},{"key":"9794_CR72","doi-asserted-by":"crossref","unstructured":"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\u2013168","DOI":"10.1145\/2733373.2806246"},{"issue":"11","key":"9794_CR73","first-page":"975","volume":"139","author":"VA Kharde","year":"2016","unstructured":"Kharde VA, Sonawane SS (2016) Sentiment analysis of twitter data: a survey of techniques. Int J Comput Appl 139(11):975\u20138887","journal-title":"Int J Comput Appl"},{"key":"9794_CR74","doi-asserted-by":"crossref","unstructured":"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\u201329, Doha, Qatar, pp 1746\u20131751","DOI":"10.3115\/v1\/D14-1181"},{"key":"9794_CR75","first-page":"247","volume":"118","author":"J Kim","year":"2019","unstructured":"Kim J, Jang S, Park E, Choi S (2019) Text classification using capsules. Neurocomputing 118:247\u2013261","journal-title":"Neurocomputing"},{"key":"9794_CR76","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1613\/jair.4272","volume":"50","author":"S Kiritchenko","year":"2014","unstructured":"Kiritchenko S, Zhu X, Mohammad S (2014) Sentiment analysis of short informal texts. J Artif Intell Res 50:723\u2013762","journal-title":"J Artif Intell Res"},{"key":"9794_CR77","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.eswa.2018.10.002","volume":"118","author":"M Kraus","year":"2019","unstructured":"Kraus M, Feuerriegel S (2019) Sentiment analysis based on rhetorical structure theory: learning deep neural networks from discourse trees. Expert Syst Appl 118:65\u201379","journal-title":"Expert Syst Appl"},{"key":"9794_CR78","unstructured":"Krejzl P, Hourov\u00e1 B, Steinberger J (2017) Stance detection in online discussions. arXiv preprint arXiv:1701.00504"},{"key":"9794_CR79","doi-asserted-by":"crossref","unstructured":"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\u20136","DOI":"10.1109\/ICCCNT.2017.8204059"},{"key":"9794_CR80","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.osnem.2017.12.002","volume":"5","author":"E Ku\u0161en","year":"2017","unstructured":"Ku\u0161en 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\u201350","journal-title":"Online Soc Netw Media"},{"key":"9794_CR81","unstructured":"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\u20139"},{"key":"9794_CR82","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.knosys.2018.04.006","volume":"152","author":"G Lee","year":"2018","unstructured":"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\u201382","journal-title":"Knowl Based Syst"},{"key":"9794_CR83","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1609\/aaai.v33i01.33019963"},{"key":"9794_CR84","doi-asserted-by":"crossref","unstructured":"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\u2013workshops, WI-IAT 2014, pp 180\u2013185","DOI":"10.1109\/WI-IAT.2014.96"},{"issue":"6","key":"9794_CR85","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1007\/s12559-017-9492-2","volume":"9","author":"Y Li","year":"2017","unstructured":"Li Y, Pan Q, Yang T, Wang S, Tang J, Cambria E (2017a) Learning word representations for sentiment analysis. Cognit Comput 9(6):843\u2013851","journal-title":"Cognit Comput"},{"key":"9794_CR86","doi-asserted-by":"crossref","unstructured":"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\u2013680","DOI":"10.1145\/3018661.3018714"},{"key":"9794_CR87","doi-asserted-by":"crossref","unstructured":"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\u201360","DOI":"10.1109\/ICASSP.2019.8683812"},{"key":"9794_CR88","unstructured":"Liu B (2010) Sentiment analysis and subjectivity. In: Handbook of natural language processing, vol 1, pp 1\u201338"},{"key":"9794_CR89","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.inffus.2016.11.012","volume":"36","author":"Y Liu","year":"2017","unstructured":"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\u2013161","journal-title":"Inf Fusion"},{"issue":"4","key":"9794_CR90","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s10462-016-9508-4","volume":"48","author":"S Lo","year":"2017","unstructured":"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\u2013527","journal-title":"Artif Intell Rev"},{"key":"9794_CR91","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.29007\/7mhj"},{"key":"9794_CR92","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s12559-018-9549-x","volume":"10","author":"Y Ma","year":"2018","unstructured":"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\u2013650","journal-title":"Cognit Comput"},{"key":"9794_CR93","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.future.2018.10.041","volume":"93","author":"X Ma","year":"2019","unstructured":"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\u2013311","journal-title":"Futur Gener Comput Syst"},{"key":"9794_CR94","unstructured":"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\u2013150"},{"key":"9794_CR95","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1109\/ACCESS.2018.2886583","volume":"7","author":"Y Manshu","year":"2019","unstructured":"Manshu Y, Bing W (2019) Adding prior knowledge in hierarchical attention neural network for cross domain sentiment classification. IEEE Access 7:2169\u20133536","journal-title":"IEEE Access"},{"key":"9794_CR96","doi-asserted-by":"crossref","unstructured":"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\u2013285","DOI":"10.1007\/978-3-319-06028-6_23"},{"key":"9794_CR97","doi-asserted-by":"crossref","unstructured":"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\u20138","DOI":"10.3115\/v1\/S14-2001"},{"key":"9794_CR98","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1109\/ICNLSP.2018.8374373"},{"key":"9794_CR99","unstructured":"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\u20133119"},{"key":"9794_CR100","doi-asserted-by":"crossref","unstructured":"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\u20131828","DOI":"10.1145\/1871437.1871739"},{"key":"9794_CR101","unstructured":"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\u2013327"},{"issue":"1","key":"9794_CR102","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.knosys.2014.05.007","volume":"69","author":"A Montejo-R\u00e1ez","year":"2014","unstructured":"Montejo-R\u00e1ez A, D\u00edaz-Galiano MC, Mart\u00ednez-Santiago F, Ure\u00f1a-L\u00f8pez LA (2014) Crowd explicit sentiment analysis. Knowl Based Syst 69(1):134\u2013139","journal-title":"Knowl Based Syst"},{"key":"9794_CR103","unstructured":"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\u2014ICMI\u201911, pp 169\u2013176"},{"key":"9794_CR104","unstructured":"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\u2013320"},{"key":"9794_CR105","doi-asserted-by":"crossref","unstructured":"Napitu F, Bijaksana MA, Trisetyarso A, Heryadi Y (2017) Twitter opinion mining predicts broadband internet\u2019s customer churn rate. In: IEEE international conference on cybernetics and computational intelligence (CyberneticsCom), pp 141\u2013146","DOI":"10.1109\/CYBERNETICSCOM.2017.8311699"},{"key":"9794_CR106","unstructured":"Narr S, H\u00fclfenhaus M, Albayrak S (2012) Language-independent twitter sentiment analysis. In: Workshop on knowledge discovery, data mining and machine learning (KDML-2012), Dortmund, Germany"},{"key":"9794_CR107","unstructured":"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\u201378"},{"key":"9794_CR108","doi-asserted-by":"crossref","unstructured":"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\u2013189","DOI":"10.1109\/ICTAI.2016.0037"},{"key":"9794_CR109","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3115\/1218955.1218990"},{"key":"9794_CR110","unstructured":"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\u201386"},{"issue":"13","key":"9794_CR111","doi-asserted-by":"crossref","first-page":"5995","DOI":"10.1016\/j.eswa.2014.03.022","volume":"41","author":"I Pe\u00f1alver-Martinez","year":"2014","unstructured":"Pe\u00f1alver-Martinez I et al (2014) Feature-based opinion mining through ontologies. Expert Syst Appl 41(13):5995\u20136008","journal-title":"Expert Syst Appl"},{"key":"9794_CR112","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.knosys.2018.02.034","volume":"148","author":"H Peng","year":"2018","unstructured":"Peng H, Ma Y, Li Y, Cambria E (2018) Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowl Based Syst 148:55\u201365","journal-title":"Knowl Based Syst"},{"key":"9794_CR113","doi-asserted-by":"crossref","unstructured":"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\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"9794_CR114","unstructured":"P\u2019erez-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\u2013982"},{"key":"9794_CR115","doi-asserted-by":"crossref","unstructured":"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\u201335","DOI":"10.3115\/v1\/S14-2004"},{"key":"9794_CR116","doi-asserted-by":"crossref","unstructured":"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\u2013349","DOI":"10.18653\/v1\/S16-1002"},{"key":"9794_CR117","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neucom.2015.01.095","volume":"174","author":"S Poria","year":"2016","unstructured":"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\u201359","journal-title":"Neurocomputing"},{"key":"9794_CR118","doi-asserted-by":"crossref","unstructured":"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\u2013448","DOI":"10.1109\/ICDM.2016.0055"},{"key":"9794_CR119","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.knosys.2016.06.009","volume":"108","author":"S Poria","year":"2016","unstructured":"Poria S, Cambria E, Gelbukh A (2016c) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst 108:42\u201349","journal-title":"Knowl Based Syst"},{"key":"9794_CR120","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.inffus.2017.02.003","volume":"37","author":"S Poria","year":"2017","unstructured":"Poria S, Cambria E, Bajpai R, Hussain A (2017a) A review of affective computing: from unimodal analysis to multimodal fusion. Inf Fusion 37:98\u2013125","journal-title":"Inf Fusion"},{"key":"9794_CR121","doi-asserted-by":"crossref","unstructured":"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\u2013883","DOI":"10.18653\/v1\/P17-1081"},{"key":"9794_CR122","doi-asserted-by":"crossref","unstructured":"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\u20131038","DOI":"10.1109\/ICDM.2017.134"},{"issue":"6","key":"9794_CR123","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MIS.2018.2882362","volume":"33","author":"S Poria","year":"2018","unstructured":"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\u201325","journal-title":"IEEE Intell Syst"},{"key":"9794_CR124","doi-asserted-by":"crossref","unstructured":"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\u2013145","DOI":"10.1109\/HICSS.2016.25"},{"issue":"May","key":"9794_CR125","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ijinfomgt.2018.05.004","volume":"42","author":"JR Ragini","year":"2018","unstructured":"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\u201324","journal-title":"Int J Inf Manag"},{"key":"9794_CR126","unstructured":"Rain C (2013) Sentiment analysis in Amazon reviews using probabilistic machine learning. Swarthmore College"},{"issue":"4","key":"9794_CR127","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10462-016-9472-z","volume":"46","author":"TA Rana","year":"2016","unstructured":"Rana TA, Cheah Y-N (2016) Aspect extraction in sentiment analysis: comparative analysis and survey. Artif Intell Rev 46(4):459\u2013483","journal-title":"Artif Intell Rev"},{"key":"9794_CR128","unstructured":"Rana R et al (2016) Gated recurrent unit (GRU) for emotion classification from noisy speech. arXiv preprint arXiv:1612.07778"},{"issue":"2","key":"9794_CR129","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1007\/s10462-018-9670-y","volume":"52","author":"S Rani","year":"2019","unstructured":"Rani S, Kumar P (2019) A journey of Indian languages over sentiment analysis: a systematic review. Artif Intell Rev 52(2):1415\u20131462","journal-title":"Artif Intell Rev"},{"key":"9794_CR130","unstructured":"Rao T, Srivastava S (2012) Analyzing stock market movements using Twitter sentiment analysis. In: ASONAM\u201912 Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012), pp 119\u2013123"},{"key":"9794_CR131","volume-title":"A survey on opinion mining and sentiment analysis: tasks, approaches and applications","author":"K Ravi","year":"2015","unstructured":"Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications, vol 89. Elsevier, Amsterdam"},{"key":"9794_CR132","doi-asserted-by":"crossref","unstructured":"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\u2013221","DOI":"10.1609\/aaai.v30i1.9974"},{"issue":"4","key":"9794_CR133","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1111\/j.1745-9125.2007.00096.x","volume":"45","author":"R Rosenfeld","year":"2008","unstructured":"Rosenfeld R, Fornango R (2008) The impact of economic conditions on robbery and property crime: the role of consumer sentiment. Criminology 45(4):735\u2013769","journal-title":"Criminology"},{"issue":"4","key":"9794_CR134","first-page":"9","volume":"18","author":"K Roy","year":"2017","unstructured":"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\u201315","journal-title":"Inf Syst"},{"key":"9794_CR135","doi-asserted-by":"crossref","unstructured":"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\u201314","DOI":"10.1109\/ISMS.2016.9"},{"key":"9794_CR136","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in neural information processing systems, pp 3856\u20133866"},{"key":"9794_CR137","unstructured":"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\u201321"},{"key":"9794_CR138","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.inffus.2019.05.003","volume":"52","author":"JF S\u00e1nchez-rada","year":"2019","unstructured":"S\u00e1nchez-rada JF, Iglesias CA (2019) Social context in sentiment analysis: formal definition, overview of current trends and framework for comparison. Inf Fusion 52:344\u2013356","journal-title":"Inf Fusion"},{"key":"9794_CR139","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.knosys.2016.05.022","volume":"108","author":"RR Shah","year":"2016","unstructured":"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\u2013109","journal-title":"Knowl Based Syst"},{"issue":"4","key":"9794_CR140","doi-asserted-by":"crossref","first-page":"225","DOI":"10.14445\/22312803\/IJCTT-V36P139","volume":"36","author":"T Shaikh","year":"2016","unstructured":"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\u2013230","journal-title":"Int J Comput Trends Technol"},{"key":"9794_CR141","doi-asserted-by":"crossref","unstructured":"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\u2013382","DOI":"10.1109\/IALP.2017.8300622"},{"key":"9794_CR142","doi-asserted-by":"crossref","unstructured":"Singh P, Dave A, Dar K (2017) Demonetization: sentiment and retweet analysis. In: International conference on inventive computing and informatics (ICICI 2017), pp 894\u2013899","DOI":"10.1109\/ICICI.2017.8365265"},{"key":"9794_CR143","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.icte.2017.03.001","volume":"4","author":"P Singh","year":"2018","unstructured":"Singh P, Sawhney RS, Kahlon KS (2018) Sentiment analysis of demonetization of 500 & 1000 rupee banknotes by Indian government. ICT Express 4:124","journal-title":"ICT Express"},{"key":"9794_CR144","unstructured":"Singhal P, Bhattacharyya P (2016) Sentiment analysis and deep learning: a survey. In: Center for Indian Language Technology, Indian Institute of Technology, Bombay"},{"key":"9794_CR145","doi-asserted-by":"crossref","unstructured":"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\u20136","DOI":"10.1109\/ICCCNT.2017.8203960"},{"key":"9794_CR146","unstructured":"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\u20131642"},{"key":"9794_CR147","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.imavis.2017.08.003","volume":"65","author":"M Soleymani","year":"2017","unstructured":"Soleymani M, Garcia D, Jou B, Schuller B, Chang SF, Pantic M (2017) A survey of multimodal sentiment analysis. Image Vis Comput 65:3\u201314","journal-title":"Image Vis Comput"},{"key":"9794_CR148","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.neucom.2018.05.104","volume":"312","author":"K Song","year":"2018","unstructured":"Song K, Yao T, Ling Q, Mei T (2018) Boosting image sentiment analysis with visual attention. Neurocomputing 312:218\u2013228","journal-title":"Neurocomputing"},{"key":"9794_CR149","doi-asserted-by":"crossref","unstructured":"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\u2013737","DOI":"10.1007\/978-3-319-19644-2_60"},{"issue":"24","key":"9794_CR150","doi-asserted-by":"crossref","first-page":"32213","DOI":"10.1007\/s11042-018-6168-1","volume":"77","author":"D Stojanovski","year":"2018","unstructured":"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\u201332242","journal-title":"Multimed Tools Appl"},{"key":"9794_CR151","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.neucom.2016.02.077","volume":"210","author":"X Sun","year":"2016","unstructured":"Sun X, Li C, Ren F (2016) Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features. Neurocomputing 210:227\u2013236","journal-title":"Neurocomputing"},{"key":"9794_CR152","doi-asserted-by":"crossref","unstructured":"Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075","DOI":"10.3115\/v1\/P15-1150"},{"key":"9794_CR153","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.18653\/v1\/D15-1167"},{"key":"9794_CR154","doi-asserted-by":"crossref","unstructured":"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\u20131023","DOI":"10.3115\/v1\/P15-1098"},{"key":"9794_CR155","doi-asserted-by":"crossref","unstructured":"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\u2014CIKM\u201917, pp 107\u2013116","DOI":"10.1145\/3132847.3132936"},{"key":"9794_CR156","unstructured":"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\u20131017"},{"key":"9794_CR157","unstructured":"Twitter Statistics (2019). https:\/\/www.statista.com\/statistics\/282087\/number-of-monthly-active-twitter-users\/"},{"key":"9794_CR158","unstructured":"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\u201330"},{"key":"9794_CR159","doi-asserted-by":"crossref","unstructured":"van Hee C, Lefever E, Hoste V (2018) Exploring the fine-grained analysis and automatic detection of irony on Twitter. Lang Resour Eval 1\u201325","DOI":"10.1007\/s10579-018-9414-2"},{"key":"9794_CR160","doi-asserted-by":"crossref","unstructured":"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\u20136","DOI":"10.1109\/JCSSE.2016.7748849"},{"key":"9794_CR161","unstructured":"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\u20136"},{"key":"9794_CR162","unstructured":"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\u2013120"},{"key":"9794_CR163","doi-asserted-by":"crossref","unstructured":"Wang X, Gerber MS, Brown DE (2012b) Automatic crime prediction using events extracted from twitter posts","DOI":"10.1007\/978-3-642-29047-3_28"},{"key":"9794_CR164","doi-asserted-by":"crossref","unstructured":"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\u2013615","DOI":"10.18653\/v1\/D16-1058"},{"key":"9794_CR165","doi-asserted-by":"crossref","unstructured":"Wang H, Meghawat A, Morency L, Xing EP (2016b) Select-additive learning: improving generalization in multimodal sentiment analysis. arXiv preprint arXiv:1609.05244","DOI":"10.1109\/ICME.2017.8019301"},{"key":"9794_CR166","unstructured":"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\u20133490"},{"key":"9794_CR167","doi-asserted-by":"crossref","unstructured":"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\u2013315","DOI":"10.1109\/CBD.2018.00062"},{"key":"9794_CR168","doi-asserted-by":"crossref","unstructured":"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\u20131174","DOI":"10.1145\/3178876.3186015"},{"key":"9794_CR169","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.neucom.2018.09.049","volume":"322","author":"J Wang","year":"2018","unstructured":"Wang J, Peng B, Zhang X (2018c) Using a stacked residual LSTM model for sentiment intensity prediction. Neurocomputing 322:93\u2013101","journal-title":"Neurocomputing"},{"key":"9794_CR170","doi-asserted-by":"crossref","unstructured":"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\u20132044","DOI":"10.1145\/3308558.3313750"},{"key":"9794_CR171","doi-asserted-by":"crossref","unstructured":"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\u2013476","DOI":"10.1109\/CSIE.2009.754"},{"key":"9794_CR172","doi-asserted-by":"crossref","first-page":"16077","DOI":"10.1109\/ACCESS.2016.2647384","volume":"5","author":"D Wu","year":"2017","unstructured":"Wu D, Chi M (2017) Long short-term memory with quadratic connections in recursive neural networks for representing compositional semantics. IEEE Access 5:16077","journal-title":"IEEE Access"},{"key":"9794_CR173","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.dss.2018.04.005","volume":"111","author":"D Wu","year":"2018","unstructured":"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","journal-title":"Decis Support Syst"},{"key":"9794_CR174","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2018.02.034","volume":"297","author":"S Xiong","year":"2018","unstructured":"Xiong S, Wang K, Ji D, Wang B (2018a) A short text sentiment-topic model for product reviews. Neurocomputing 297:94\u2013102","journal-title":"Neurocomputing"},{"key":"9794_CR175","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1016\/j.neucom.2017.11.023","volume":"275","author":"S Xiong","year":"2018","unstructured":"Xiong S, Lv H, Zhao W, Ji D (2018b) Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings. Neurocomputing 275:2459\u20132466","journal-title":"Neurocomputing"},{"key":"9794_CR176","doi-asserted-by":"crossref","unstructured":"Xu F, Ke\u0161elj 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\u201367","DOI":"10.1109\/CBI.2014.37"},{"issue":"4","key":"9794_CR177","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.dss.2010.08.021","volume":"50","author":"K Xu","year":"2011","unstructured":"Xu K, Liao SS, Li J, Song Y (2011) Mining comparative opinions from customer reviews for competitive Intelligence. Decis Support Syst 50(4):743\u2013754","journal-title":"Decis Support Syst"},{"key":"9794_CR178","doi-asserted-by":"crossref","unstructured":"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\u2013249","DOI":"10.1109\/PACIFICVIS.2017.8031600"},{"key":"9794_CR179","doi-asserted-by":"crossref","unstructured":"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\u2013110","DOI":"10.1109\/ICIS.2013.6607825"},{"key":"9794_CR180","doi-asserted-by":"crossref","unstructured":"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\u20131489","DOI":"10.18653\/v1\/N16-1174"},{"key":"9794_CR181","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.neucom.2018.04.042","volume":"307","author":"M Yang","year":"2018","unstructured":"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\u201397","journal-title":"Neurocomputing"},{"issue":"3","key":"9794_CR182","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.ipm.2018.12.004","volume":"56","author":"C Yang","year":"2019","unstructured":"Yang C, Zhang H, Jiang B, Li K (2019a) Aspect-based sentiment analysis with alternating coattention networks. Inf Process Manag 56(3):463\u2013478","journal-title":"Inf Process Manag"},{"key":"9794_CR183","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.neunet.2019.06.014","volume":"118","author":"M Yang","year":"2019","unstructured":"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\u2013261","journal-title":"Neural Netw"},{"key":"9794_CR184","unstructured":"Yelp Dataset (2014)"},{"key":"9794_CR185","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.eswa.2018.03.055","volume":"105","author":"SY Yoo","year":"2018","unstructured":"Yoo SY, Song JI, Jeong OR (2018) Social media contents based sentiment analysis and prediction system. Expert Syst Appl 105:102\u2013111","journal-title":"Expert Syst Appl"},{"key":"9794_CR186","doi-asserted-by":"crossref","unstructured":"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\u2013388","DOI":"10.1609\/aaai.v29i1.9179"},{"key":"9794_CR187","doi-asserted-by":"crossref","unstructured":"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\u201322","DOI":"10.1145\/2835776.2835779"},{"key":"9794_CR188","doi-asserted-by":"crossref","unstructured":"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\u20131751","DOI":"10.1145\/3123266.3123413"},{"issue":"3","key":"9794_CR189","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/TASLP.2017.2788182","volume":"26","author":"L Yu","year":"2018","unstructured":"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\u2013681","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"1","key":"9794_CR190","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/TASLP.2018.2875170","volume":"27","author":"J Yu","year":"2019","unstructured":"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\u2013177","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"11","key":"9794_CR191","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1109\/TNNLS.2014.2303478","volume":"25","author":"M Yuan","year":"2014","unstructured":"Yuan M, Tang H, Li H (2014) Real-time keypoint recognition using restricted boltzmann machine. IEEE Trans Neural Netw Learn Syst 25(11):2119\u20132126","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9794_CR192","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2018.05.004","volume":"155","author":"Z Yuan","year":"2018","unstructured":"Yuan Z, Wu S, Wu F, Liu J, Huang Y (2018) Domain attention model for multi-domain sentiment classification. Knowl Based Syst 155:1\u201310","journal-title":"Knowl Based Syst"},{"key":"9794_CR193","unstructured":"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"},{"key":"9794_CR194","doi-asserted-by":"crossref","first-page":"10927","DOI":"10.1109\/ACCESS.2019.2891019","volume":"7","author":"J Zhang","year":"2019","unstructured":"Zhang J, Chow C (2019) MOCA: multi-objective, collaborative, and attentive sentiment analysis. IEEE Access 7:10927\u201310936","journal-title":"IEEE Access"},{"key":"9794_CR195","unstructured":"Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners\u2019 guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820"},{"issue":"6","key":"9794_CR196","doi-asserted-by":"crossref","first-page":"7674","DOI":"10.1016\/j.eswa.2010.12.147","volume":"38","author":"Z Zhang","year":"2011","unstructured":"Zhang Z, Ye Q, Zhang Z, Li Y (2011) Sentiment classification of internet restaurant reviews written in Cantonese. Expert Syst Appl 38(6):7674\u20137682","journal-title":"Expert Syst Appl"},{"key":"9794_CR197","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1016\/j.neucom.2017.09.080","volume":"275","author":"Z Zhang","year":"2017","unstructured":"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","journal-title":"Neurocomputing"},{"key":"9794_CR198","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.tcs.2018.04.029","volume":"752","author":"Y Zhang","year":"2018","unstructured":"Zhang Y et al (2018a) A quantum-inspired multimodal sentiment analysis framework. Theor Comput Sci 752:21","journal-title":"Theor Comput Sci"},{"key":"9794_CR199","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.neucom.2018.04.068","volume":"309","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Wang L, Zou Y, Gan C (2018b) The optimally designed dynamic memory networks for targeted sentiment classification. Neurocomputing 309:36","journal-title":"Neurocomputing"},{"key":"9794_CR200","doi-asserted-by":"crossref","first-page":"58284","DOI":"10.1109\/ACCESS.2018.2874623","volume":"6","author":"B Zhang","year":"2018","unstructured":"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\u201358294","journal-title":"IEEE Access"},{"key":"9794_CR201","doi-asserted-by":"crossref","unstructured":"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\u20131623","DOI":"10.3115\/v1\/D14-1169"},{"key":"9794_CR202","first-page":"1","volume":"4347","author":"W Zhao","year":"2017","unstructured":"Zhao W et al (2017) Weakly-supervised deep embedding for product review sentiment analysis. IEEE Trans Knowl Data Eng 4347:1\u201312","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9794_CR203","doi-asserted-by":"crossref","unstructured":"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\u20131559","DOI":"10.18653\/v1\/P19-1150"},{"key":"9794_CR204","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.future.2018.03.047","volume":"86","author":"K Zhou","year":"2018","unstructured":"Zhou K, Zeng J, Liu Y, Zou F (2018) Deep sentiment hashing for text retrieval in social CIoT. Futur Gener Comput Syst 86:362","journal-title":"Futur Gener Comput Syst"},{"key":"9794_CR205","doi-asserted-by":"crossref","unstructured":"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\u20134","DOI":"10.1109\/ICTAS.2018.8368746"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-019-09794-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10462-019-09794-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-019-09794-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T16:16:17Z","timestamp":1665245777000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10462-019-09794-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,2]]},"references-count":205,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["9794"],"URL":"https:\/\/doi.org\/10.1007\/s10462-019-09794-5","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,2]]},"assertion":[{"value":"2 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}