{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T04:44:14Z","timestamp":1729658654184,"version":"3.28.0"},"reference-count":52,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,29]]},"DOI":"10.1109\/besc53957.2021.9635309","type":"proceedings-article","created":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T20:38:45Z","timestamp":1639773525000},"page":"1-8","source":"Crossref","is-referenced-by-count":1,"title":["Beyond the Polarities: Sentiment Analysis of French Restaurant Reviews Using BERT-based Models"],"prefix":"10.1109","author":[{"given":"Hyun Jung","family":"Kang","sequence":"first","affiliation":[]},{"given":"Iris","family":"Eshkol-Taravella","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"311","article-title":"Opinion mining and sentiment analysis","volume":"35","author":"pang","year":"2009","journal-title":"Comput Linguist"},{"key":"ref38","first-page":"380","article-title":"Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence","volume":"1","author":"sun","year":"2019","journal-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies"},{"key":"ref33","first-page":"1234","article-title":"Biobert: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"lee","year":"2020","journal-title":"Bioin-[ormatics"},{"journal-title":"Improving language understanding by generative pre-training","year":"2018","author":"radford","key":"ref32"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref30","first-page":"6297","article-title":"Learned in translation: Contextualized word vectors","author":"mccann","year":"2017","journal-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems"},{"key":"ref37","article-title":"Finbert: Financial sentiment analysis with pre-trained language models","author":"araci","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref36","article-title":"Clinicalbert: Modeling clinical notes and predicting hospital readmission","author":"huang","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-1909"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1371"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.3115\/v1\/D14-1162","article-title":"Glove: Global vectors for word representation","author":"pennington","year":"2014","journal-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)"},{"key":"ref27","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"2013","journal-title":"Proceedines of WorkshoD at ICLR"},{"journal-title":"Getting Started with Google BERT Build and train state-of-the-art natural language processing models using BERT","year":"2021","author":"ravichandiran","key":"ref29"},{"journal-title":"Flaubert Unsupervised language model pretraining for french","year":"2020","author":"le","key":"ref2"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.645"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2396761.2398637"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1258"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/2479832.2479857"},{"key":"ref24","first-page":"33","article-title":"Intention analysis for sales, marketing and customer service","author":"carlos","year":"0","journal-title":"Proceedings of COLING 2012 Demonstration Papers"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_a_00278"},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v29i1.9529","article-title":"Mining user consumption intention from social media using domain adaptive convolutional neural network","volume":"29","author":"ding","year":"2015","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref25","first-page":"1041","article-title":"Identifying intention posts in discussion forums","author":"chen","year":"2013","journal-title":"Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies"},{"journal-title":"Deep Learning with Python","year":"2017","author":"chollet","key":"ref50"},{"journal-title":"RoBERTa A Robustly optimized BERT Pretraining Approach","year":"2019","author":"liu","key":"ref51"},{"key":"ref52","article-title":"Understanding the behaviors of bert in ranking","author":"qiao","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014073"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.2200\/S00416ED1V01Y201204HLT016"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139084789"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/1183614.1183625"},{"journal-title":"Building a Sentiment Summarizer for Local Service Reviews","year":"2008","author":"blair-goldensohn","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3115\/1609067.1609124"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2016.12.002"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-804412-4.00008-5"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3115\/1620754.1620793"},{"key":"ref18","first-page":"54","article-title":"Wishful thinking-finding suggestions and‘buy'wishes from product reviews","author":"ramanand","year":"2010","journal-title":"Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation ot emotion in text"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.13053\/rcs-70-1-15"},{"key":"ref4","first-page":"4942","article-title":"An empirical examination of online restaurant reviews","author":"kang","year":"2020","journal-title":"Proceedings of the 12th Language Resources and Evaluation Conference"},{"key":"ref3","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2019","journal-title":"NAACL"},{"key":"ref6","article-title":"Thumbs up? sentiment classification using machine learning techniques","volume":"10","author":"pang","year":"2002","journal-title":"EMNLP"},{"journal-title":"Regards croises sur les avis en ligne","year":"2021","author":"kang","key":"ref5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3115\/1034678.1034721"},{"key":"ref7","first-page":"417","article-title":"Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews","author":"turney","year":"2002","journal-title":"Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0026683"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3115\/990820.990864"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s10936-004-2667-5"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.21236\/ADA221854"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.2307\/2529310"},{"key":"ref47","first-page":"22","article-title":"The measurement of interrater agreement","volume":"2","author":"fleiss","year":"1981","journal-title":"Statistical Methods for Rates and Proportions"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55394-8_1"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3115\/1220575.1220619"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3758\/BF03214418"},{"key":"ref43","first-page":"1","article-title":"Beyond the stars: improving rating predictions using review text content","volume":"9","author":"ganu","year":"2009","journal-title":"WehDB"}],"event":{"name":"2021 8th International Conference on Behavioral and Social Computing (BESC)","start":{"date-parts":[[2021,10,29]]},"location":"Doha, Qatar","end":{"date-parts":[[2021,10,31]]}},"container-title":["2021 8th International Conference on Behavioral and Social Computing (BESC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9635008\/9635096\/09635309.pdf?arnumber=9635309","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T02:27:21Z","timestamp":1674095241000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9635309\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":52,"URL":"https:\/\/doi.org\/10.1109\/besc53957.2021.9635309","relation":{},"subject":[],"published":{"date-parts":[[2021,10,29]]}}}