{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T23:28:27Z","timestamp":1790292507980,"version":"4.1.0"},"reference-count":184,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>Humans are increasingly integrated with devices that enable the collection of vast unstructured opinionated data. Accurately analysing subjective information from this data is the task of sentiment analysis (an actively researched area in NLP). Deep learning provides a diverse selection of architectures to model sentiment analysis tasks and has surpassed other machine learning methods as the foremast approach for performing sentiment analysis tasks. Recent developments in deep learning architectures represent a shift away from Recurrent and Convolutional neural networks and the increasing adoption of Transformer language models. Utilising pre-trained Transformer language models to transfer knowledge to downstream tasks has been a breakthrough in NLP.<\/jats:p>\n                  <jats:p>This survey applies a task-oriented taxonomy to recent trends in architectures with a focus on the theory, design and implementation. To the best of our knowledge, this is the only survey to cover state-of-the-art Transformer-based language models and their performance on the most widely used benchmark datasets. This survey paper provides a discussion of the open challenges in NLP and sentiment analysis. The survey covers five years from 1st July 2017 to 1st July 2022.<\/jats:p>","DOI":"10.1145\/3548772","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T08:11:17Z","timestamp":1658218277000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":86,"title":["Deep Learning in Sentiment Analysis: Recent Architectures"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4189-4007","authenticated-orcid":false,"given":"Tariq","family":"Abdullah","sequence":"first","affiliation":[{"name":"University of Derby, United Kingdom, Kedleston Rd, Derby, Derbyshire, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8518-1709","authenticated-orcid":false,"given":"Ahmed","family":"Ahmet","sequence":"additional","affiliation":[{"name":"University of Derby, United Kingdom, Kedleston Rd, Derby, Derbyshire, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,12,23]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataSE50710.2020.00014"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1216-2_2"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.semeval-1.206"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2690342"},{"key":"e_1_3_1_6_2","first-page":"2020","article-title":"A deep dive into multilingual hate speech classification. In Machine Learning and Knowledge Discovery in Databases","volume":"2020","author":"Aluru S. S.","year":"2021","unstructured":"S. S. Aluru, B. Mathew, P. Saha, and A. Mukherjee. 2021. A deep dive into multilingual hate speech classification. In Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track: European Conference, ECML PKDD 2020 (2021), 2020.","journal-title":"Applied Data Science and Demo Track: European Conference, ECML PKDD"},{"key":"e_1_3_1_7_2","article-title":"An autoencoder approach to learning bilingual word representations","volume":"27","author":"AP S. Chandar","year":"2014","unstructured":"S. Chandar AP, S. Lauly, H. Larochelle, M. Khapra, B. Ravindran, V. C. Raykar, and A. Saha. 2014. An autoencoder approach to learning bilingual word representations. Advances in Neural Information Processing Systems 27 (2014).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1073"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00288"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2018.8508646"},{"key":"e_1_3_1_11_2","unstructured":"D. Bahdanau K. Cho and Y. Bengio. 2014. Neural machine translation by jointly learning to align and translate. (2014)."},{"key":"e_1_3_1_12_2","unstructured":"P. Bajaj C. Xiong G. Ke X. Liu D. He S. Tiwary T.-Y. Liu P. Bennett X. Song and J. Gao. 2022. METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals. (2022)."},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.11561"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S18-1100"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S17-2126"},{"key":"e_1_3_1_16_2","volume-title":"The Thirtieth International FLAIRS Conference","author":"Becker W.","year":"2017","unstructured":"W. Becker, J. Wehrmann, H. E. L. Cagnini, and R. C. Barros. 2017. An efficient deep neural architecture for multilingual sentiment analysis in Twitter. In The Thirtieth International FLAIRS Conference."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/info11090437"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944966"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"e_1_3_1_20_2","unstructured":"S. Biere and S. Bhulai. 2018. Hate Speech Detection Using Natural Language Processing Techniques. (2018)."},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107134"},{"key":"e_1_3_1_22_2","volume-title":"Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics","author":"Blitzer J.","year":"2007","unstructured":"J. Blitzer, M. Dredze, and F. Pereira. 2007. Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/681"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394231.3397890"},{"key":"e_1_3_1_26_2","first-page":"1","article-title":"Deep transfer learning mechanism for fine-grained cross-domain sentiment classification","author":"Cao Z.","year":"2021","unstructured":"Z. Cao, Y. Zhou, A. Yang, and S. Peng. 2021. Deep transfer learning mechanism for fine-grained cross-domain sentiment classification. Connection Science (2021), 1\u201318.","journal-title":"Connection Science"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1047"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1111"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00039"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313600"},{"key":"e_1_3_1_31_2","volume-title":"Association for Computational Linguistics","author":"Cho K.","year":"2014","unstructured":"K. Cho, B. Van Merri\u00ebnboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Association for Computational Linguistics. Doha, Qatar."},{"key":"e_1_3_1_32_2","article-title":"PaLM: Scaling language modeling with pathways","author":"Chowdhery Aakanksha","year":"2022","unstructured":"Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et\u00a0al. 2022. PaLM: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311 (2022).","journal-title":"arXiv preprint arXiv:2204.02311"},{"key":"e_1_3_1_33_2","unstructured":"K. Clark M.-T. Luong Q. V. Le and C. D. Manning. 2003. ELECTRA: Pre-training text encoders as discriminators rather than generators. (2003)."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454921"},{"key":"e_1_3_1_35_2","volume-title":"6th International Conference on Learning Representations","author":"Conneau A.","year":"2018","unstructured":"A. Conneau, G. Lample, M. Ranzato, L. Denoyer, and H. J\u00e9gou. 2018. Word translation without parallel data. In 6th International Conference on Learning Representations. Vancouver, Canada."},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2021.3124365"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.semeval-1.290"},{"key":"e_1_3_1_38_2","volume-title":"Proceedings of NAACL-HLT","author":"Devlin J.","year":"2019","unstructured":"J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT. 2019, Minneapolis, Minnesota."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.5555\/2886521.2886653"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1658"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/K15-1012"},{"key":"e_1_3_1_42_2","volume-title":"Association for Computational Linguistics, Hong Kong","author":"Eisenschlos J.","year":"2019","unstructured":"J. Eisenschlos, S. Ruder, P. Czapla, M. Kardas, S. Gugger, and J. Howard. 2019. MultiFiT: Efficient multi-lingual language model fine-tuning. In Association for Computational Linguistics, Hong Kong."},{"issue":"4686","key":"e_1_3_1_43_2","article-title":"Zero-shot cross-lingual classification using multilingual neural machine translation","volume":"1809","author":"Eriguchi A.","year":"2018","unstructured":"A. Eriguchi, M. Johnson, O. Firat, H. Kazawa, and W. Macherey. 2018. Zero-shot cross-lingual classification using multilingual neural machine translation. ArXiv 1809, 4686 (2018).","journal-title":"ArXiv"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/E14-1049"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107366"},{"issue":"1","key":"e_1_3_1_46_2","first-page":"22","article-title":"Coarse- and fine-grained sentiment analysis of social media text","volume":"30","author":"Fink Clayton R.","year":"2011","unstructured":"Clayton R. Fink, Danielle S. Chou, Jonathon J. Kopecky, and Ashley J. Llorens. 2011. Coarse- and fine-grained sentiment analysis of social media text. Johns Hopkins Apl. Technical Digest 30, 1 (2011), 22\u201330.","journal-title":"Johns Hopkins Apl. Technical Digest"},{"key":"e_1_3_1_47_2","unstructured":"Gartner. 2019. Gartner Identifies the Top 10 Strategic Technology Trends for 2018. (2019). https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2017-10-04-gartner-identifies-the-top-10-strategic-technology-trends-for-2018."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102262"},{"key":"e_1_3_1_49_2","volume-title":"International Conference on Machine Learning","author":"Gouws S.","year":"2015","unstructured":"S. Gouws, Y. Bengio, and G. Corrado. 2015. BilBOWA: Fast bilingual distributed representations without word alignments. In International Conference on Machine Learning."},{"key":"e_1_3_1_50_2","unstructured":"A. Graves G. Wayne and I. Danihelka. 2014. Neural Turing Machines . (2014)."},{"key":"e_1_3_1_51_2","unstructured":"J. C. Hay B. E. Lynch and D. R. Smith. 1960. Mark I Perceptron Operators\u2019 Manual . (1960)."},{"key":"e_1_3_1_52_2","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics","author":"Hazarika D.","year":"2018","unstructured":"D. Hazarika, S. Poria, S. Gorantla, E. Cambria, R. Zimmermann, and R. Mihalcea. 2018. CASCADE: Contextual sarcasm detection in online discussion forums. In Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe, New Mexico, USA."},{"key":"e_1_3_1_53_2","article-title":"DeBERTa: Decoding-enhanced BERT with disentangled attention","author":"He Pengcheng","year":"2020","unstructured":"Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020. DeBERTa: Decoding-enhanced BERT with disentangled attention. arXiv preprint arXiv:2006.03654 (2020).","journal-title":"arXiv preprint arXiv:2006.03654"},{"key":"e_1_3_1_54_2","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics","author":"He R.","year":"2018","unstructured":"R. He, W. S. Lee, H. T. Ng, and D. Dahlmeier. 2018. Effective attention modeling for aspect-level sentiment classification. In Proceedings of the 27th International Conference on Computational Linguistics."},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-2092"},{"key":"e_1_3_1_56_2","volume-title":"Proceedings of The 12th International Workshop on Semantic Evaluation","author":"Hee C. Van","year":"2018","unstructured":"C. Van Hee, E. Lefever, and V. Hoste. 2018. Semeval-2018 task 3: Irony detection in English tweets. In Proceedings of The 12th International Workshop on Semantic Evaluation."},{"issue":"6173","key":"e_1_3_1_57_2","article-title":"Multilingual distributed representations without word alignment","volume":"1312","author":"Hermann K. M.","year":"2013","unstructured":"K. M. Hermann and P. Blunsom. 2013. Multilingual distributed representations without word alignment. ArXiv 1312, 6173 (2013).","journal-title":"ArXiv"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1006"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2491127"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1031"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1136"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93372-6_22"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-6202"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S19-2009"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-020-00696-x"},{"key":"e_1_3_1_67_2","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Jebbara S.","year":"2019","unstructured":"S. Jebbara and P. Cimiano. 2019. Zero-shot cross-lingual opinion target extraction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota."},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2017.09.009"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93034-3_54"},{"key":"e_1_3_1_70_2","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Jiang H.","year":"2019","unstructured":"H. Jiang, P. He, W. Chen, X. Liu, J. Gao, and T. Zhao. 2019. Smart: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics."},{"key":"e_1_3_1_71_2","first-page":"2020","volume-title":"2020 25th International Conference on Pattern Recognition (ICPR)","author":"Karimi A.","year":"2020","unstructured":"A. Karimi, L. Rossi, and A. Prati. 2020. Adversarial training for aspect-based sentiment analysis with BERT. In 2020 25th International Conference on Pattern Recognition (ICPR). 2020."},{"key":"e_1_3_1_72_2","doi-asserted-by":"crossref","unstructured":"C. Karouzos G. Paraskevopoulos and A. Potamianos. 2021. UDALM: Unsupervised Domain Adaptation through Language Modeling. (2021).","DOI":"10.18653\/v1\/2021.naacl-main.203"},{"key":"e_1_3_1_73_2","doi-asserted-by":"crossref","unstructured":"Y. Kim. 2014. Convolutional neural networks for sentence classification. In Association for Computational Linguistics . (2014).","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_3_1_74_2","volume-title":"Proceedings of COLING","author":"Klementiev A.","year":"2012","unstructured":"A. Klementiev, I. Titov, and B. Bhattarai. 2012. Inducing crosslingual distributed representations of words. In Proceedings of COLING."},{"key":"e_1_3_1_75_2","volume-title":"Mapping the Development of the Artificial Intelligence General Purpose Technology","author":"Klinger J.","year":"2018","unstructured":"J. Klinger, J. C. Mateos-Garcia, and K. Stathoulopoulos. 2018. Deep learning, deep change? Mapping the development of the artificial intelligence general purpose technology. In Mapping the Development of the Artificial Intelligence General Purpose Technology."},{"key":"e_1_3_1_76_2","doi-asserted-by":"crossref","unstructured":"P. Koehn and R. Knowles. 2017. Six challenges for neural machine translation. (2017).","DOI":"10.18653\/v1\/W17-3204"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-2012"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2899260"},{"key":"e_1_3_1_79_2","article-title":"Bridging the domain gap in cross-lingual document classification","author":"Lai Guokun","year":"2019","unstructured":"Guokun Lai, Barlas Oguz, Yiming Yang, and Veselin Stoyanov. 2019. Bridging the domain gap in cross-lingual document classification. arXiv preprint arXiv:1909.07009 (2019).","journal-title":"arXiv preprint arXiv:1909.07009"},{"key":"e_1_3_1_80_2","unstructured":"Z. Lan M. Chen S. Goodman K. Gimpel P. Sharma and R. Soricut. 2019. ALBERT: A lite BERT for self-supervised learning of language representations. In Eighth International Conference on Learning Representations Addis Ababa . (2019)."},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_1_82_2","doi-asserted-by":"crossref","unstructured":"M. Lewis Y. Liu N. Goyal M. Ghazvininejad A. Mohamed O. Levy V. Stoyanov and L. Zettlemoyer. 2019. BART: Denoising sequence-to-sequence pre-training for natural language generation translation and comprehension. (2019).","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/K18-1018"},{"key":"e_1_3_1_84_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12055"},{"key":"e_1_3_1_85_2","doi-asserted-by":"crossref","unstructured":"Z. Li Y. Zhang Y. Wei Y. Wu and Q. Yang. 2017. End-to-end adversarial memory network for cross-domain sentiment classification. In IJCAI . (2017).","DOI":"10.24963\/ijcai.2017\/311"},{"key":"e_1_3_1_86_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139084789"},{"key":"e_1_3_1_87_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2045"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105010"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1050"},{"key":"e_1_3_1_90_2","unstructured":"Y. Liu M. Ott N. Goyal J. Du M. Joshi D. Chen O. Levy M. Lewis L. Zettlemoyer and V. Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. (2019)."},{"key":"e_1_3_1_91_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W15-1521"},{"key":"e_1_3_1_92_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/568"},{"key":"e_1_3_1_94_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12048"},{"key":"e_1_3_1_95_2","doi-asserted-by":"publisher","DOI":"10.5555\/2002472.2002491"},{"issue":"7264","key":"e_1_3_1_96_2","article-title":"Low-resource languages: A review of past work and future challenges","volume":"2006","author":"Magueresse A.","year":"2020","unstructured":"A. Magueresse, V. Carles, and E. Heetderks. 2020. Low-resource languages: A review of past work and future challenges. CoRR 2006, 7264 (2020).","journal-title":"CoRR"},{"key":"e_1_3_1_97_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2901929"},{"key":"e_1_3_1_98_2","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2967210"},{"key":"e_1_3_1_99_2","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"issue":"4168","key":"e_1_3_1_100_2","article-title":"Exploiting similarities among languages for machine translation","volume":"1309","author":"Mikolov T.","year":"2013","unstructured":"T. Mikolov, Q. V. Le, and I. Sutskever. 2013. Exploiting similarities among languages for machine translation. CoRR 1309, 4168 (2013).","journal-title":"CoRR"},{"key":"e_1_3_1_101_2","unstructured":"T. Mikolov I. Sutskever K. Chen G. S. Corrado and J. Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems . (2013)."},{"key":"e_1_3_1_102_2","doi-asserted-by":"publisher","DOI":"10.5555\/3013558.3013562"},{"key":"e_1_3_1_103_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2952360"},{"key":"e_1_3_1_104_2","doi-asserted-by":"crossref","unstructured":"V. Nahar S. Unankard X. Li and C. Pang. 2012. Sentiment analysis for effective detection of cyber bullying. In Asia-Pacific Web Conference . (2012).","DOI":"10.1007\/978-3-642-29253-8_75"},{"key":"e_1_3_1_105_2","unstructured":"nlpprogress. 2022. NLP-progress. (2022). http:\/\/nlpprogress.com\/english\/sentiment_analysis.html."},{"key":"e_1_3_1_106_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v4i1.14031"},{"key":"e_1_3_1_107_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.semeval-1.288"},{"key":"e_1_3_1_108_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102360"},{"key":"e_1_3_1_109_2","article-title":"Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales","author":"Pang Bo","year":"2005","unstructured":"Bo Pang and Lillian Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint cs\/0506075 (2005).","journal-title":"arXiv preprint cs\/0506075"},{"key":"e_1_3_1_110_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68560-1_36"},{"key":"e_1_3_1_111_2","doi-asserted-by":"crossref","unstructured":"paperswithcode. 2020. Aspect-Based Sentiment Analysis on SemEval 2014 Task 4 Sub Task 2. (2020). https:\/\/paperswithcode.com\/sota\/aspect-based-sentiment-analysis-on-semeval.","DOI":"10.15520\/ajcsit.v4i8.9"},{"key":"e_1_3_1_112_2","unstructured":"paperswithcode. 2022. Natural Language Processing. (2022). https:\/\/paperswithcode.com\/area\/natural-language-processing."},{"key":"e_1_3_1_113_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_1_114_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"e_1_3_1_115_2","unstructured":"G. K. Pitsilis H. Ramampiaro and H. Langseth. 2018. Detecting offensive language in tweets using deep learning. (2018)."},{"key":"e_1_3_1_116_2","volume-title":"International Workshop on Semantic Evaluation","author":"Pontiki M.","year":"2016","unstructured":"M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Manandhar, M. Al-Smadi, M. Al-Ayyoub, Y. Zhao, B. Qin, and O. De Clercq. 2016. Semeval-2016 task 5: Aspect based sentiment analysis. In International Workshop on Semantic Evaluation."},{"key":"e_1_3_1_117_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/S14-2004"},{"key":"e_1_3_1_118_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05102-3"},{"key":"e_1_3_1_119_2","doi-asserted-by":"publisher","DOI":"10.1145\/2700171.2791033"},{"key":"e_1_3_1_120_2","doi-asserted-by":"publisher","DOI":"10.5555\/1858681.1858795"},{"key":"e_1_3_1_121_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.semeval-1.205"},{"key":"e_1_3_1_122_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S19-2008"},{"key":"e_1_3_1_123_2","doi-asserted-by":"publisher","DOI":"10.3390\/su11164459"},{"key":"e_1_3_1_124_2","article-title":"When and why are pre-trained word embeddings useful for neural machine translation?","author":"Qi Ye","year":"2018","unstructured":"Ye Qi, Devendra Singh Sachan, Matthieu Felix, Sarguna Janani Padmanabhan, and Graham Neubig. 2018. When and why are pre-trained word embeddings useful for neural machine translation? arXiv preprint arXiv:1804.06323 (2018).","journal-title":"arXiv preprint arXiv:1804.06323"},{"key":"e_1_3_1_125_2","unstructured":"A. Radford K. Narasimhan T. Salimans and I. Sutskever. 2018. Improving language understanding by generative pre-training. (2018). https:\/\/s3-us-west-2.amazonaws.com\/openai-assets\/researchcovers\/languageunsupervised\/language."},{"key":"e_1_3_1_126_2","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel C.","year":"2019","unstructured":"C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21 (2019), 1\u201367.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_127_2","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00024"},{"key":"e_1_3_1_128_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.10.326"},{"key":"e_1_3_1_129_2","unstructured":"A. Rietzler S. Stabinger P. Opitz and S. Engl. 2019. Adapt or get left behind: Domain adaptation through BERT language model finetuning for aspect-target sentiment classification. (2019)."},{"key":"e_1_3_1_130_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358040"},{"key":"e_1_3_1_131_2","doi-asserted-by":"crossref","unstructured":"D. E. Rumelhart G. E. Hinton and R. J. Williams. 1985. Learning internal representations by error propagation. (1985).","DOI":"10.21236\/ADA164453"},{"key":"e_1_3_1_132_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.11640"},{"key":"e_1_3_1_133_2","volume-title":"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka","author":"Saeidi M.","year":"2016","unstructured":"M. Saeidi, G. Bouchard, M. Liakata, and S. Riedel. 2016. SentiHood: Targeted aspect based sentiment analysis dataset for urban neighbourhoods. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka."},{"key":"e_1_3_1_134_2","volume-title":"Proceedings of the 14th International Workshop on Semantic Evaluation","author":"Safaya A.","year":"2020","unstructured":"A. Safaya, M. Abdullatif, and D. Yuret. 2020. KUISAIL at SemEval-2020 task 12: BERT-CNN for offensive speech. In Proceedings of the 14th International Workshop on Semantic Evaluation. Barcelona, Spain."},{"key":"e_1_3_1_135_2","doi-asserted-by":"crossref","unstructured":"R. Satapathy S. Pardeshi and E. Cambria. 2022. Polarity and subjectivity detection with multitask learning and BERT embedding.","DOI":"10.3390\/fi14070191"},{"key":"e_1_3_1_136_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2012.6289079"},{"key":"e_1_3_1_137_2","volume-title":"Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)","author":"Schwenk H.","year":"2018","unstructured":"H. Schwenk and X. Li. 2018. A corpus for multilingual document classification in eight languages. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). Miyazaki."},{"key":"e_1_3_1_138_2","article-title":"An enhanced deep neural network for predicting workplace absenteeism","volume":"2020","author":"Shah S. A. Ali","year":"2020","unstructured":"S. A. Ali Shah, I. Uddin, F. Aziz, S. Ahmad, M. A. Al-Khasawneh, and M. Sharaf. 2020. An enhanced deep neural network for predicting workplace absenteeism. Complexity 2020, (2020).","journal-title":"Complexity"},{"key":"e_1_3_1_139_2","unstructured":"J. Shen P.-J. Chen M. Le J. He J. Gu M. Ott M. Auli and M. Ranzato. 2019. The source-target domain mismatch problem in machine translation. (2019)."},{"key":"e_1_3_1_140_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.semeval-1.270"},{"issue":"5","key":"e_1_3_1_141_2","article-title":"Digital 2022: Another Year of Bumper Growth","volume":"10","author":"Social We Are","year":"2022","unstructured":"We Are Social. 2022. Digital 2022: Another Year of Bumper Growth. We Are Social 10, 5 2022 (2022). https:\/\/wearesocial.com\/uk\/blog\/2022\/01\/digital-2022-another-year-of-bumper-growth-2\/.","journal-title":"We Are Social"},{"key":"e_1_3_1_142_2","unstructured":"Y. Song J. Wang T. Jiang Z. Liu and Y. Rao. 2019. Attentional encoder network for targeted sentiment classification. (2019)."},{"key":"e_1_3_1_143_2","volume-title":"Proceedings of NAACL-HLT","author":"Sun C.","year":"2019","unstructured":"C. Sun, L. Huang, and X. Qiu. 2019. Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In Proceedings of NAACL-HLT. 2019, Minneapolis, Minnesota."},{"key":"e_1_3_1_144_2","unstructured":"Y. Sun S. Wang S. Feng S. Ding C. Pang J. Shang J. Liu X. Chen Y. Zhao and Y. Lu. 2021. ERNIE 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation. (2021)."},{"key":"e_1_3_1_145_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1021"},{"key":"e_1_3_1_146_2","unstructured":"A. Torfi R. A. Shirvani Y. Keneshloo N. Tavaf and E. A. Fox. 2003. Natural language processing advancements by deep learning: A survey. (2003)."},{"key":"e_1_3_1_147_2","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani A.","year":"2017","unstructured":"A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, \u0141. Kaiser, and I. Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_1_148_2","volume-title":"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL","author":"Vulic I.","year":"2015","unstructured":"I. Vulic and M.-F. Moens. 2015. Bilingual word embeddings from non-parallel document-aligned data applied to bilingual lexicon induction. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL)"},{"key":"e_1_3_1_149_2","volume-title":"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing","author":"Vuli\u0107 I.","year":"2013","unstructured":"I. Vuli\u0107 and M. F. Moens. 2013. A study on bootstrapping bilingual vector spaces from non-parallel data (and nothing else). In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing."},{"key":"e_1_3_1_150_2","article-title":"Superglue: A stickier benchmark for general-purpose language understanding systems","volume":"32","author":"Wang Alex","year":"2019","unstructured":"Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019. Superglue: A stickier benchmark for general-purpose language understanding systems. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_151_2","article-title":"GLUE: A multi-task benchmark and analysis platform for natural language understanding","author":"Wang Alex","year":"2018","unstructured":"Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2018. GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461 (2018). https:\/\/gluebenchmark.com\/.","journal-title":"arXiv preprint arXiv:1804.07461"},{"key":"e_1_3_1_152_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1215"},{"key":"e_1_3_1_153_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12020"},{"key":"e_1_3_1_154_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.semeval-1.189"},{"key":"e_1_3_1_155_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1058"},{"key":"e_1_3_1_156_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186015"},{"key":"e_1_3_1_157_2","doi-asserted-by":"publisher","DOI":"10.1145\/3167132.3167325"},{"key":"e_1_3_1_158_2","doi-asserted-by":"crossref","unstructured":"D. Weiss C. Alberti M. Collins and S. Petrov. 2015. Structured training for neural network transition-based parsing. (2015).","DOI":"10.3115\/v1\/P15-1032"},{"key":"e_1_3_1_159_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S19-2137"},{"key":"e_1_3_1_160_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S18-1006"},{"key":"e_1_3_1_161_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10030270"},{"key":"e_1_3_1_162_2","first-page":"1","volume-title":"Association for the Advancement of Artificial Intelligence","author":"Wu Z.","year":"2020","unstructured":"Z. Wu and D.C. Ong. 2020. Context-guided BERT for targeted aspect-based sentiment analysis. In Association for the Advancement of Artificial Intelligence. 1\u20139."},{"key":"e_1_3_1_163_2","volume-title":"34th Conference on Neural Information Processing Systems (NeurIPS 2020)","author":"Xie Q.","year":"2019","unstructured":"Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and Q.V. Le. 2019. Unsupervised data augmentation. In 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Vancouver, Canada."},{"key":"e_1_3_1_164_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/N15-1104"},{"key":"e_1_3_1_165_2","unstructured":"H. Xu B. Liu L. Shu and P.S. Yu. 2019. BERT post-training for review reading comprehension and aspect-based sentiment analysis. (2019)."},{"key":"e_1_3_1_166_2","doi-asserted-by":"crossref","unstructured":"W. Xue and T. Li. 2018. Aspect based sentiment analysis with gated convolutional networks. (2018).","DOI":"10.18653\/v1\/P18-1234"},{"key":"e_1_3_1_167_2","unstructured":"H. Yang B. Zeng M. Xu and T. Wang. 2021. Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable Sentiment Dependency Learning. (2021)."},{"key":"e_1_3_1_168_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.08.001"},{"key":"e_1_3_1_169_2","article-title":"Neural attentive network for cross-domain aspect-level sentiment classification","author":"Yang M.","year":"2019","unstructured":"M. Yang, W. Yin, Q. Qu, W. Tu, Y. Shen, and X. Chen. 2019. Neural attentive network for cross-domain aspect-level sentiment classification. IEEE Transactions on Affective Computing (2019).","journal-title":"IEEE Transactions on Affective Computing"},{"key":"e_1_3_1_170_2","volume-title":"33rd Conference on Neural Information Processing Systems (NeurIPS 2019)","author":"Yang Z.","year":"2019","unstructured":"Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Q. V. Le. 2019. XLNet: Generalized autoregressive pretraining for language understanding. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada."},{"key":"e_1_3_1_171_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.599"},{"key":"e_1_3_1_172_2","unstructured":"Inc. Yelp. 2022. Yelp dataset. (Mar.2022). https:\/\/www.kaggle.com\/datasets\/yelp-dataset\/yelp-dataset."},{"key":"e_1_3_1_173_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1217"},{"key":"e_1_3_1_174_2","doi-asserted-by":"crossref","unstructured":"Z. Yu and G. Liu. 2018. Sliced recurrent neural networks. (2018).","DOI":"10.1186\/s13662-018-1872-8"},{"key":"e_1_3_1_175_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1115"},{"key":"e_1_3_1_176_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.semeval-1.188"},{"key":"e_1_3_1_177_2","doi-asserted-by":"crossref","unstructured":"K. Zhang H. Zhang Q. Liu H. Zhao H. Zhu and E. Chen. 2019. Interactive Attention Transfer Network for Cross-domain Sentiment Classification. (2019).","DOI":"10.1609\/aaai.v33i01.33015773"},{"key":"e_1_3_1_178_2","doi-asserted-by":"publisher","DOI":"10.1145\/3316481"},{"key":"e_1_3_1_179_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.04.006"},{"key":"e_1_3_1_180_2","doi-asserted-by":"crossref","unstructured":"W. Zhang X. Li Y. Deng L. Bing and W. Lam. 2022. A Survey on Aspect-Based Sentiment Analysis: Tasks Methods and Challenges. (2022).","DOI":"10.1109\/TKDE.2022.3230975"},{"key":"e_1_3_1_181_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2885362"},{"key":"e_1_3_1_182_2","doi-asserted-by":"crossref","unstructured":"Z. Zhang X. Han Z. Liu X. Jiang M. Sun and Q. Liu. 2019. ERNIE: Enhanced language representation with informative entities. (2019).","DOI":"10.18653\/v1\/P19-1139"},{"key":"e_1_3_1_183_2","article-title":"Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification","volume":"193","author":"Zhao P.","year":"2019","unstructured":"P. Zhao, L. Houb, and O. Wua. 2019. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification. Knowledge-Based Systems 193 (2019).","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_1_184_2","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics","author":"Zhu P.","year":"2018","unstructured":"P. Zhu and T. Qian. 2018. Enhanced aspect level sentiment classification with auxiliary memory. In Proceedings of the 27th International Conference on Computational Linguistics."},{"key":"e_1_3_1_185_2","volume-title":"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing","author":"Zou W. Y.","year":"2013","unstructured":"W. Y. Zou, R. Socher, D. Cer, and C. D. Manning. 2013. Bilingual word embeddings for phrase-based machine translation. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548772","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3548772","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T13:50:53Z","timestamp":1750168253000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548772"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,23]]},"references-count":184,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8,31]]}},"alternative-id":["10.1145\/3548772"],"URL":"https:\/\/doi.org\/10.1145\/3548772","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,23]]},"assertion":[{"value":"2021-04-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-27","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}