{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:22:24Z","timestamp":1773807744950,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:00:00Z","timestamp":1609545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:00:00Z","timestamp":1609545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s12652-020-02800-7","type":"journal-article","created":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T08:02:53Z","timestamp":1609574573000},"page":"10267-10287","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Bidirectional transfer learning model for sentiment analysis of natural language"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4353-2125","authenticated-orcid":false,"given":"Shivani","family":"Malhotra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinay","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alpana","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,2]]},"reference":[{"key":"2800_CR1","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.comcom.2020.04.002","volume":"157","author":"F Abid","year":"2020","unstructured":"Abid F, Li C, Alam M (2020) Multi-source social media data sentiment analysis using bidirectional recurrent convolutional neural networks. Comput Commun 157:102\u2013115","journal-title":"Comput Commun"},{"key":"2800_CR2","volume-title":"Neural machine translation by jointly learning to align and translate","author":"D Bahdanau","year":"2015","unstructured":"Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. ICLR, San Diego"},{"key":"2800_CR3","first-page":"1137","volume":"3","author":"Y Bengio","year":"2003","unstructured":"Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3:1137\u20131155","journal-title":"J Mach Learn Res"},{"key":"2800_CR4","volume-title":"Natural language processing with Python: analyzing text with the natural language toolkit","author":"S Bird","year":"2009","unstructured":"Bird S, Klein E, Loper E (2009) Natural language processing with Python: analyzing text with the natural language toolkit. O\u2019Reilly Media, Inc., Newton"},{"key":"2800_CR5","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135\u2013146","journal-title":"Trans Assoc Comput Linguist"},{"key":"2800_CR6","doi-asserted-by":"publisher","first-page":"20617","DOI":"10.1109\/ACCESS.2017.2740982","volume":"5","author":"M Bouazizi","year":"2017","unstructured":"Bouazizi M, Ohtsuki T (2017) A pattern-based approach for multi-class sentiment analysis in twitter. IEEE Access 5:20617\u201320639","journal-title":"IEEE Access"},{"key":"2800_CR7","doi-asserted-by":"publisher","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 31:102\u2013107","journal-title":"IEEE Intell Syst"},{"key":"2800_CR8","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/MIS.2017.4531228","volume":"32","author":"E Cambria","year":"2017","unstructured":"Cambria E, Poria S, Gelbukh A, Thelwall M (2017) Sentiment analysis is a big suitcase. IEEE Intell Syst 32:74\u201380","journal-title":"IEEE Intell Syst"},{"key":"2800_CR9","doi-asserted-by":"publisher","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 (2017) Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst Appl 72:221\u2013230","journal-title":"Expert Syst Appl"},{"key":"2800_CR10","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493\u20132537","journal-title":"J Mach Learn Res"},{"key":"2800_CR11","unstructured":"Crowdflower (2016) Airline Twitter Sentiment. https:\/\/data.world\/crowdflower\/airline-twitter-sentiment. Online accessed 01 December 2019"},{"key":"2800_CR12","first-page":"76","volume-title":"Inferring the source of official texts: can SVM beat ULMFiT?","author":"PHL de Araujo","year":"2020","unstructured":"de Araujo PHL, de Campos TE, de Sousa MMS (2020) Inferring the source of official texts: can SVM beat ULMFiT?. Springer, Evora, pp 76\u201386"},{"key":"2800_CR13","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding, NAACL-HLT, Association for Computational Linguistics"},{"key":"2800_CR14","first-page":"513","volume-title":"Domain adaptation for large-scale sentiment classification: a deep learning approach","author":"X Glorot","year":"2011","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. ICML, Bellevue, pp 513\u2013520"},{"key":"2800_CR15","first-page":"661","volume-title":"A novel approach to feature hierarchy in aspect based sentiment analysis using OWA operator","author":"C Gupta","year":"2019","unstructured":"Gupta C, Jain A, Joshi N (2019) A novel approach to feature hierarchy in aspect based sentiment analysis using OWA operator. Springer, Chandigarh, pp 661\u2013667"},{"key":"2800_CR16","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s10115-016-0924-1","volume":"3","author":"M Haddoud","year":"2016","unstructured":"Haddoud M, Mokhtari A, Lecroq T, Abdedda\u00efm S (2016) Combining supervised term-weighting metrics for SVM text classification with extended term representation. Knowl Inf Syst 3:909\u2013931","journal-title":"Knowl Inf Syst"},{"key":"2800_CR17","first-page":"894","volume-title":"The role of syntax in vector space models of compositional semantics","author":"KM Hermann","year":"2013","unstructured":"Hermann KM, Blunsom P (2013) The role of syntax in vector space models of compositional semantics. Association for Computational Linguistics, Sofia, pp 894\u2013904"},{"key":"2800_CR18","first-page":"328","volume":"1","author":"J Howard","year":"2018","unstructured":"Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. Assoc Comput Linguist 1:328\u2013339","journal-title":"Assoc Comput Linguist"},{"key":"2800_CR19","unstructured":"Jean-Fran\u00e7ois P (2017) Feature engineering for deep learning. https:\/\/medium.com\/inside-machine-learning\/feature-engineering-for-deep-learning-2b1fc7605ace. Online accessed 15 December 2019"},{"key":"2800_CR20","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s00521-016-2401-x","volume":"29","author":"M Jiang","year":"2018","unstructured":"Jiang M, Liang Y, Feng X, Fan X, Pei Z, Xue Yu, Guan R (2018) Text classification based on deep belief network and softmax regression. Neural Comput Appl 29:61\u201370","journal-title":"Neural Comput Appl"},{"key":"2800_CR21","doi-asserted-by":"publisher","first-page":"2870","DOI":"10.1109\/ACCESS.2017.2672677","volume":"5","author":"Z Jianqiang","year":"2017","unstructured":"Jianqiang Z, Xiaolin G (2017) Comparison research on text pre-processing methods on twitter sentiment analysis. IEEE Access 5:2870\u20132879","journal-title":"IEEE Access"},{"key":"2800_CR22","first-page":"427","volume-title":"Bag of tricks for efficient text classification","author":"A Joulin","year":"2017","unstructured":"Joulin A, Grave E, Bojanowski P, Mikolov T (2017) Bag of tricks for efficient text classification. Association for Computational Linguistics, Valencia, pp 427\u2013431"},{"key":"2800_CR23","unstructured":"Krishnamurthy G, Majumder N, Poria S, Cambria E (2018) A deep learning approach for multimodal deception detection. arXiv preprint arXiv:1803.00344"},{"key":"2800_CR24","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of machine learning research, pp 1188\u20131196, Beijing"},{"key":"2800_CR25","doi-asserted-by":"publisher","first-page":"85401","DOI":"10.1109\/ACCESS.2019.2925059","volume":"7","author":"R Liu","year":"2019","unstructured":"Liu R, Shi Y, Ji C, Jia M (2019) A survey of sentiment analysis based on transfer learning. IEEE Access 7:85401\u201385412","journal-title":"IEEE Access"},{"key":"2800_CR26","unstructured":"Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis, Portland, Association for Computational Linguistics, pp 142\u2013150"},{"key":"2800_CR27","volume-title":"Foundations of statistical natural language processing","author":"CD Manning","year":"1999","unstructured":"Manning CD, Manning CD, Sch\u00fctze H (1999) Foundations of statistical natural language processing. MIT press, Cambridge"},{"key":"2800_CR28","unstructured":"McCann B, Bradbury J, Xiong C, Socher R (2017) Learned in translation: contextualized word vectors. Association for Computing Machinery, California, pp 6294\u20136305"},{"key":"2800_CR29","unstructured":"Merity S, Keskar NS, Socher R (2018) Regularizing and optimizing LSTM language models. ICLR"},{"key":"2800_CR30","first-page":"3111","volume-title":"Distributed representations of words and phrases and their compositionality","author":"T Mikolov","year":"2013","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013a) Distributed representations of words and phrases and their compositionality. Association for Computing Machinery, New York, pp 3111\u20133119"},{"key":"2800_CR31","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013b) Efficient estimation of word representations in vector space. In: Proceedings of workshop at ICLR"},{"key":"2800_CR32","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.eswa.2018.03.058","volume":"106","author":"MM Miro\u0144czuk","year":"2018","unstructured":"Miro\u0144czuk MM, Protasiewicz J (2018) A recent overview of the state-of-the-art elements of text classification. Expert Syst Appl 106:36\u201354","journal-title":"Expert Syst Appl"},{"key":"2800_CR33","first-page":"1059","volume-title":"Efficient non-parametric estimation of multiple embeddings per word in vector space","author":"A Neelakantan","year":"2015","unstructured":"Neelakantan A, Shankar J, Passos A, McCallum A (2015) Efficient non-parametric estimation of multiple embeddings per word in vector space. Association for Computational Linguistics, Doha, pp 1059\u20131069"},{"key":"2800_CR34","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans knowl Data Eng 22:1345\u20131359","journal-title":"IEEE Trans knowl Data Eng"},{"key":"2800_CR35","first-page":"1","volume-title":"Application of deep learning approaches for sentiment analysis","author":"AR Pathak","year":"2020","unstructured":"Pathak AR, Agarwal B, Pandey M, Rautaray S (2020) Application of deep learning approaches for sentiment analysis. Springer, Singapore, pp 1\u201331"},{"key":"2800_CR36","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. Association for Computational Linguistics, Qatar, Valencia, pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"2800_CR37","first-page":"59","volume-title":"Deception detection using real-life trial data","author":"V P\u00e9rez-Rosas","year":"2015","unstructured":"P\u00e9rez-Rosas V, Abouelenien M, Mihalcea R, Burzo M (2015) Deception detection using real-life trial data. Association for Computing Machinery, Seattle, pp 59\u201366"},{"key":"2800_CR38","first-page":"1756","volume-title":"Semi-supervised sequence tagging with bidirectional language models","author":"ME Peters","year":"2017","unstructured":"Peters ME, Ammar W, Bhagavatula C, Power R (2017) Semi-supervised sequence tagging with bidirectional language models. Association for Computational Linguistics, Vancouver, pp 1756\u20131765"},{"key":"2800_CR39","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Proceedings of NAACL-HLT, pp 2227\u20132237","DOI":"10.18653\/v1\/N18-1202"},{"key":"2800_CR40","first-page":"769","volume-title":"Sentiment classification system of twitter data for US airline service analysis","author":"A Rane","year":"2018","unstructured":"Rane A, Kumar A (2018) Sentiment classification system of twitter data for US airline service analysis. IEEE, Tokyo, pp 769\u2013773"},{"key":"2800_CR41","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.ipm.2015.01.005","volume":"52","author":"H Saif","year":"2016","unstructured":"Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of Twitter. Inf Process Manag 52:5\u201319","journal-title":"Inf Process Manag"},{"key":"2800_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-019-1926-x","volume":"2","author":"Z Shaukat","year":"2020","unstructured":"Shaukat Z, Zulfiqar AA, Xiao C, Azeem M, Mahmood T (2020) Sentiment analysis on IMDB using lexicon and neural networks. SN Appl Sci 2:1\u201310","journal-title":"SN Appl Sci"},{"key":"2800_CR43","doi-asserted-by":"publisher","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 S-F, Pantic M (2017) A survey of multimodal sentiment analysis. Image Vis Comput 65:3\u201314","journal-title":"Image Vis Comput"},{"key":"2800_CR44","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1613\/jair.2934","volume":"37","author":"PD Turney","year":"2010","unstructured":"Turney PD, Pantel P (2010) From frequency to meaning: vector space models of semantics. J Artif Intell Res 37:141\u2013188","journal-title":"J Artif Intell Res"},{"key":"2800_CR45","unstructured":"Vaswani A, Shazeer N, Parmar N, Jakob U, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"key":"2800_CR46","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1007\/s13042-020-01069-8","volume":"11","author":"Y Wang","year":"2020","unstructured":"Wang Y, Hou Y, Che W, Liu T (2020) From static to dynamic word representations: a survey. Int J Mach Learn Cybern 11:1611\u20131630","journal-title":"Int J Mach Learn Cybern"},{"key":"2800_CR47","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2020.01.064","volume":"390","author":"Y Wu","year":"2020","unstructured":"Wu Y, Li J, Wu J, Chang J (2020) Siamese capsule networks with global and local features for text classification. Neurocomputing 390:88\u201398","journal-title":"Neurocomputing"},{"key":"2800_CR48","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhutdinov R, Zemel R, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the 32nd international conference on machine learning, PMLR, vol 37, pp 2048\u20132057"},{"key":"2800_CR49","first-page":"3320","volume-title":"How transferable are features in deep neural networks?","author":"J Yosinski","year":"2014","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks?. Association for Computing Machinery, Montreal, pp 3320\u20133328"},{"key":"2800_CR50","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","volume":"13","author":"T Young","year":"2018","unstructured":"Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag 13:55\u201375","journal-title":"IEEE Comput Intell Mag"},{"key":"2800_CR51","doi-asserted-by":"publisher","first-page":"102215","DOI":"10.1016\/j.ipm.2020.102215","volume":"57","author":"J Zheng","year":"2020","unstructured":"Zheng J, Cai F, Chen H, de Rijke M (2020) Pre-train, Interact, Fine-tune: a novel interaction representation for text classification. Inf Process Manag 57:102215","journal-title":"Inf Process Manag"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-020-02800-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-020-02800-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-020-02800-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T05:47:08Z","timestamp":1632203228000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-020-02800-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,2]]},"references-count":51,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["2800"],"URL":"https:\/\/doi.org\/10.1007\/s12652-020-02800-7","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,2]]},"assertion":[{"value":"29 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}