{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T01:11:52Z","timestamp":1754183512496,"version":"3.41.2"},"reference-count":36,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2025,8,1]]},"DOI":"10.1587\/transinf.2024edp7095","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T17:13:22Z","timestamp":1738257202000},"page":"977-990","source":"Crossref","is-referenced-by-count":0,"title":["Aspect-Level Cross-Linguistic Multi-Layer Sentiment Analysis: A Case Study on User Preferences for Mask Attributes During the COVID-19 Pandemic"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Haoran","family":"LUO","sequence":"first","affiliation":[{"name":"Graduate School of Creative Science and Engineering, Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengfei","family":"SHAO","sequence":"additional","affiliation":[{"name":"Graduate School of Creative Science and Engineering, Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomoji","family":"KISHI","sequence":"additional","affiliation":[{"name":"Graduate School of Creative Science and Engineering, Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenglei","family":"LI","sequence":"additional","affiliation":[{"name":"Graduate School of Creative Science and Engineering, Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] W. Cao, F. Shi, Y. Yuan, and J. Jia, \u201cSpatial and temporal evolution of population mobility networks in china and its impact on the spread of new crown epidemics,\u201d Journal of Systems Management, vol.31, no.06, pp.1123-1136, 2022."},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] H. Li, Q. Chen, Z. Zhong, R. Gong, and G. Han, \u201cE-word of mouth sentiment analysis for user behavior studies,\u201d Information Processing & Management, vol.59, no.1, p.102784, 2022. 10.1016\/j.ipm.2021.102784","DOI":"10.1016\/j.ipm.2021.102784"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] L. Li, J. Johnson, W. Aarhus, and D. Shah, \u201cKey factors in mooc pedagogy based on nlp sentiment analysis of learner reviews: What makes a hit,\u201d Computers & Education, vol.176, p.104354, 2022. 10.1016\/j.compedu.2021.104354","DOI":"10.1016\/j.compedu.2021.104354"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] D.C. Edara, L.P. Vanukuri, V. Sistla, and V.K.K. Kolli, \u201cSentiment analysis and text categorization of cancer medical records with lstm,\u201d Journal of Ambient Intelligence and Humanized Computing, vol.14, no.5, pp.5309-5325, 2023. 10.1007\/s12652-019-01399-8","DOI":"10.1007\/s12652-019-01399-8"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] A.C. Sanders, R.C. White, L.S. Severson, R. Ma, R. McQueen, H.C.A. Paulo, Y. Zhang, J.S. Erickson, and K.P. Bennett, \u201cUnmasking the conversation on masks: Natural language processing for topical sentiment analysis of covid-19 twitter discourse,\u201d AMIA Summits on Translational Science Proceedings, 2021:555, 2021. 10.1101\/2020.08.28.20183863","DOI":"10.1101\/2020.08.28.20183863"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] L. He, C. He, T.L. Reynolds, Q. Bai, Y. Huang, C. Li, K. Zheng, and Y. Chen, \u201cWhy do people oppose mask wearing? a comprehensive analysis of us tweets during the covid-19 pandemic,\u201d Journal of the American Medical Informatics Association, vol.28, no.7, pp.1564-1573, 2021. 10.1093\/jamia\/ocab047","DOI":"10.1093\/jamia\/ocab047"},{"key":"7","unstructured":"[7] Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R.R. Salakhutdinov, and Q.V. Le, \u201cXlnet: Generalized autoregressive pretraining for language understanding,\u201d Advances in neural information processing systems, 32, 2019."},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] A.H. Sweidan, N. El-Bendary, and H. Al-Feel, \u201cSentence-level aspect-based sentiment analysis for classifying adverse drug reactions (adrs) using hybrid ontology-xlnet transfer learning,\u201d IEEE Access, vol.9, pp.90828-90846, 2021. 10.1109\/access.2021.3091394","DOI":"10.1109\/ACCESS.2021.3091394"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] N. Habbat, H. Anoun, and L. Hassouni, \u201cCombination of gru and cnn deep learning models for sentiment analysis on french customer reviews using xlnet model,\u201d IEEE Engineering Management Review, vol.51, no.1, pp.41-51, 2022. 10.1109\/emr.2022.3208818","DOI":"10.1109\/EMR.2022.3208818"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] J. Wang, L.-C. Yu, K.R. Lai, and X. Zhang, \u201cDimensional sentiment analysis using a regional cnn-lstm model,\u201d Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers), pp.225-230, 2016. 10.18653\/v1\/p16-2037","DOI":"10.18653\/v1\/P16-2037"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] R. He, L. Liu, H. Ye, Q. Tan, B. Ding, L. Cheng, J. Low, L. Bing, and L. Si, \u201cOn the effectiveness of adapter-based tuning for pretrained language model adaptation,\u201d arXiv preprint arXiv:2106.03164, 2021.","DOI":"10.18653\/v1\/2021.acl-long.172"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] W.-L. Wei, C.-H. Wu, and J.-C. Lin, \u201cA regression approach to affective rating of chinese words from anew,\u201d Affective Computing and Intelligent Interaction: Fourth International Conference, ACII 2011, Memphis, TN, USA, Oct. 9-12, 2011, Proceedings, Part II, pp.121-131. Springer, 2011. 10.1007\/978-3-642-24571-8_13","DOI":"10.1007\/978-3-642-24571-8_13"},{"key":"13","unstructured":"[13] R. TQ Chen, Y. Rubanova, J. Bettencourt, and D.K. Duvenaud, \u201cNeural ordinary differential equations,\u201d Advances in neural information processing systems, 31, 2018."},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] T. Ma, H. Zhou, Y. Tian, and N. Al-Nabhan, \u201cA novel rumor detection algorithm based on entity recognition, sentence reconfiguration, and ordinary differential equation network,\u201d Neurocomputing, vol.447, pp.224-234, 2021. 10.1016\/j.neucom.2021.03.055","DOI":"10.1016\/j.neucom.2021.03.055"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] M. Okawa and T. Iwata, \u201cPredicting opinion dynamics via sociologically-informed neural networks,\u201d Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.1306-1316, 2022. 10.1145\/3534678.3539228","DOI":"10.1145\/3534678.3539228"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] M. Tamire, S. Anumasa, and P.K. Srijith, \u201cBi-directional recurrent neural ordinary differential equations for social media text classification,\u201d arXiv preprint arXiv:2112.12809, 2021.","DOI":"10.18653\/v1\/2022.wit-1.3"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] I. Rida, N. Al-Maadeed, S. Al-Maadeed, and S. Bakshi, \u201cA comprehensive overview of feature representation for biometric recognition,\u201d Multimedia Tools and Applications, vol.79, pp.4867-4890, 2020. 10.1007\/s11042-018-6808-5","DOI":"10.1007\/s11042-018-6808-5"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] M. Montefinese, E. Ambrosini, B. Fairfield, and N. Mammarella, \u201cThe adaptation of the affective norms for english words (anew) for italian,\u201d Behavior research methods, vol.46, pp.887-903, 2014. 10.3758\/s13428-013-0405-3","DOI":"10.3758\/s13428-013-0405-3"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] A. Mollahosseini, B. Hasani, and M.H. Mahoor, \u201cAffectnet: A database for facial expression, valence, and arousal computing in the wild,\u201d IEEE Transactions on Affective Computing, vol.10, no.1, pp.18-31, 2017. 10.1109\/taffc.2017.2740923","DOI":"10.1109\/TAFFC.2017.2740923"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] J. Wang, L.-C. Yu, K.R. Lai, and X. Zhang, \u201cLocally weighted linear regression for cross-lingual valence-arousal prediction of affective words,\u201d Neurocomputing, vol.194, pp.271-278, 2016. 10.1016\/j.neucom.2016.02.057","DOI":"10.1016\/j.neucom.2016.02.057"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] T. Cover and P. Hart, \u201cNearest neighbor pattern classification,\u201d IEEE transactions on information theory, vol.13, no.1, pp.21-27, 1967. 10.1109\/tit.1967.1053964","DOI":"10.1109\/TIT.1967.1053964"},{"key":"22","unstructured":"[22] R.-C. Su, S.-S. Chong, T.-E. Su, and M.-H. Su, \u201cSoochowds at rocling-2021 shared task: Text sentiment analysis using bert and lstm,\u201d Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pp.375-379, 2021."},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] G. Xu, H. Wei, J. Wang, X. Chen, and B. Zhu, \u201cA local weighted linear regression (lwlr) ensemble of surrogate models based on stacking strategy: Application to hydrodynamic response prediction for submerged floating tunnel (sft),\u201d Applied Ocean Research, vol.125, p.103228, 2022. 10.1016\/j.apor.2022.103228","DOI":"10.1016\/j.apor.2022.103228"},{"key":"24","unstructured":"[24] B. Csan\u00e1dy, L. Muzsai, P. Vedres, Z. N\u00e1dasdy, and A. Luk\u00e1cs, \u201cLlambert: Large-scale low-cost data annotation in nlp,\u201d arXiv preprint arXiv:2403.15938, 2024."},{"key":"25","unstructured":"[25] Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R.R. Salakhutdinov, and Q.V. Le, \u201cXlnet: Generalized autoregressive pretraining for language understanding,\u201d Advances in neural information processing systems, 32, 2019."},{"key":"26","unstructured":"[26] F.A. Heinsen, \u201cAn algorithm for routing vectors in sequences,\u201d arXiv preprint arXiv:2211.11754, 2022."},{"key":"27","unstructured":"[27] S. Wang, H. Fang, M. Khabsa, H. Mao, and H. Ma, \u201cEntailment as few-shot learner,\u201d arXiv preprint arXiv:2104.14690, 2021."},{"key":"28","unstructured":"[28] L. Haonan, S. H Huang, T. Ye, and G. Xiuyan, \u201cGraph star net for generalized multi-task learning,\u201d arXiv preprint arXiv:1906.12330, 2019."},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] Z. Bingyu and N. Arefyev, \u201cThe document vectors using cosine similarity revisited,\u201d arXiv preprint arXiv:2205.13357, 2022.","DOI":"10.18653\/v1\/2022.insights-1.17"},{"key":"30","unstructured":"[30] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P.J. Liu, \u201cExploring the limits of transfer learning with a unified text-to-text transformer,\u201d Journal of machine learning research, vol.21, no.140, pp.1-67, 2020."},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] H. Jiang, P. He, W. Chen, X. Liu, J. Gao, and T. Zhao, \u201cSmart: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization,\u201d arXiv preprint arXiv:1911.03437, 2019.","DOI":"10.18653\/v1\/2020.acl-main.197"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] A. Aghajanyan, A. Gupta, A. Shrivastava, X. Chen, L. Zettlemoyer, and S. Gupta, \u201cMuppet: Massive multi-task representations with pre-finetuning,\u201d arXiv preprint arXiv:2101.11038, 2021.","DOI":"10.18653\/v1\/2021.emnlp-main.468"},{"key":"33","unstructured":"[33] Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, \u201cAlbert: A lite bert for self-supervised learning of language representations,\u201d arXiv preprint arXiv:1909.11942, 2019."},{"key":"34","unstructured":"[34] W. Wang, B. Bi, M. Yan, C. Wu, Z. Bao, J. Xia, L. Peng, and L. Si, \u201cStructbert: Incorporating language structures into pre-training for deep language understanding,\u201d arXiv preprint arXiv:1908.04577, 2019."},{"key":"35","unstructured":"[35] J. Huang C. Yang, H. Zhang, and J. Wan, \u201cText sentiment analysis of cigarette online reviews,\u201d China Tobacco Journal, vol.26, no.02, pp.92-100, 2020."},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] J.-M. Chen, P.-C. Chang, and K.-W. Liang, \u201cSpeech emotion recognition based on joint self-assessment manikins and emotion labels,\u201d 2019 IEEE International Symposium on Multimedia (ISM), pp.327-3273, IEEE, 2019. 10.1109\/ism46123.2019.00073","DOI":"10.1109\/ISM46123.2019.00073"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/8\/E108.D_2024EDP7095\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T03:29:10Z","timestamp":1754105350000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/8\/E108.D_2024EDP7095\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"references-count":36,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024edp7095","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2025,8,1]]},"article-number":"2024EDP7095"}}