{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T19:20:33Z","timestamp":1754162433904,"version":"3.41.2"},"reference-count":40,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Civil aviation passengers\u2019 comments about airlines or airports on social media are the key to improving service quality. In order to make effective use of these data, in-depth analysis is needed to provide solid support for service improvement of airlines and airports. Due to its uniqueness, accurate modeling and analysis are required. First, the data are accurately collected from various network platforms and reprocessed. In this process, transfer learning, artificial data annotation, and term frequency\u2013inverse document frequency (TF-IDF) analysis technology are innovatively integrated to ensure data quality and analysis depth. Then, according to the characteristics of the review data, the civil aviation domain-specific word vector based on Word2Vec was customized and developed, and the backtranslation \u2013 convolutional neural networks \u2013 bi-directional long short-term memory (Backtranslation-CNN-BiLSTM) model was constructed for sentiment analysis. The model is verified by multi-dimensional evaluation indicators, which shows excellent performance indicators and ensures reasonable efficiency. Finally, the cutting-edge BERTopic modeling technology was used to deeply mine the passenger comment topics to reveal the focus and potential needs of passengers. This study successfully constructed the technical system of civil aviation passenger comment sentiment analysis, which provided technical support for industry service optimization.<\/jats:p>","DOI":"10.1515\/pjbr-2024-0005","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T06:00:51Z","timestamp":1753855251000},"source":"Crossref","is-referenced-by-count":0,"title":["Air fare sentiment via Backtranslation-CNN-BiLSTM and BERTopic"],"prefix":"10.1515","volume":"16","author":[{"given":"Xijun","family":"Ke","sequence":"first","affiliation":[{"name":"College of Science, Civil Aviation Flight University of China , Luocheng Town, Guanghan City, Deyang City, No. 46, Section 4, Nanchang Road , GuangHan , Sichuan Province , China"}]},{"given":"Jiajun","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China , Luocheng Town, Guanghan City, Deyang City, No. 46, Section 4, Nanchang Road , GuangHan , Sichuan Province , China"}]},{"given":"Haiwen","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Science, Civil Aviation Flight University of China , Luocheng Town, Guanghan City, Deyang City, No. 46, Section 4, Nanchang Road , GuangHan , Sichuan Province , China"}]},{"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China , Luocheng Town, Guanghan City, Deyang City, No. 46, Section 4, Nanchang Road , GuangHan , Sichuan Province , China"}]}],"member":"374","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"2025073006004612321_j_pjbr-2024-0005_ref_001","doi-asserted-by":"crossref","unstructured":"M. Al-Ayyoub, A. Nuseir, K. Alsmearat, Y. Jararweh, and B. Gupta, \u201cDeep learning for arabic NLP: A survey,\u201d J. Comput. Sci., vol. 26, pp. 522\u2013531, 2018.","DOI":"10.1016\/j.jocs.2017.11.011"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_002","doi-asserted-by":"crossref","unstructured":"M. V. Naik, M. D. Anasari, V. K. Gunjan, and S. Kumar, A Comprehensive Study of Sentiment Analysis in Big Data Applications, Singapore: Springer; 2020, pp. 333\u2013351, https:\/\/doi.org\/10.1007\/978-981-15-3125-5_35.","DOI":"10.1007\/978-981-15-3125-5_35"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_003","doi-asserted-by":"crossref","unstructured":"M. Ahmed, M. D. Ansari, N. Singh, V. Gunjan, S. K. BV, and M. Khan, \u201cRating-based recommender system based on textual reviews using IoT smart devices,\u201d Mobile Inform. Syst., vol. 2022, pp. 1\u201318, July 2022.","DOI":"10.1155\/2022\/2854741"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_004","doi-asserted-by":"crossref","unstructured":"L. Ting, M. Khan, A. Sharma, and M. D. Ansari, \u201cA secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing,\u201d J. Intel. Syst., vol. 31, no. 1, pp. 221\u2013236, 2022, https:\/\/doi.org\/10.1515\/jisys-2022-0012.","DOI":"10.1515\/jisys-2022-0012"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_005","doi-asserted-by":"crossref","unstructured":"Z. A. Jaaz, M. D. Ansari, P. S. JosephNg, and H. M. Gheni, \u201cOptimization technique based on cluster head selection algorithm for 5G-enabled IoMT smart healthcare framework for industry,\u201d Paladyn J. Behav. Robotics, vol. 13, no. 1, pp. 99\u2013109, 2022, https:\/\/doi.org\/10.1515\/pjbr-2022-0101.","DOI":"10.1515\/pjbr-2022-0101"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_006","doi-asserted-by":"crossref","unstructured":"K. Du, F. Xing, R. Mao, and E. Cambria, \u201cFinancial sentiment analysis: Techniques and applications,\u201d ACM Comput. Surveys, vol. 56, no. 9, pp. 1\u201342, 2024.","DOI":"10.1145\/3649451"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_007","doi-asserted-by":"crossref","unstructured":"A. A. Naeem, Zaib Syed, \u201cA near optimal scheduling algorithm for efficient radio resource management in multi-user mimo systems,\u201d Wireless Personal Commun. Int. J., vol. 106, no. 3, 2019.","DOI":"10.1007\/s11277-019-06222-3"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_008","unstructured":"C. Zhou, C. Sun, Z. Liu, and F. Lau, A c-LSTM neural network for text classification, 2015, arXiv: http:\/\/arXiv.org\/abs\/arXiv:1511.08630."},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_009","doi-asserted-by":"crossref","unstructured":"M. Jamal, Z. Ullah, M. Naeem, M. Abbas, and A. Coronato, \u201cA hybrid multi-agent reinforcement learning approach for spectrum sharing in vehicular networks,\u201d Future Internet, vol. 16, no. 5, p. 152, 2024, https:\/\/doi.org\/10.3390\/fi16050152.","DOI":"10.3390\/fi16050152"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_010","unstructured":"C. Quan and F. Ren, \u201cTarget based review classification for fine-grained sentiment analysis,\u201d Int. J. Innovat. Comput. Inform. Control, vol. 10, no. 1, pp. 257\u2013268, 2014."},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_011","doi-asserted-by":"crossref","unstructured":"O. Irsoy and C. Cardie, \u201cOpinion mining with deep recurrent neural networks,\u201d in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Conference Proceedings, 2014, pp. 720\u2013728.","DOI":"10.3115\/v1\/D14-1080"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_012","doi-asserted-by":"crossref","unstructured":"P. Liu, S. Joty, and H. Meng, \u201cFine-grained opinion mining with recurrent neural networks and word embeddings,\u201d in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Conference Proceedings, 2015, pp. 1433\u20131443.","DOI":"10.18653\/v1\/D15-1168"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_013","doi-asserted-by":"crossref","unstructured":"A. K. Goel, R. Chakraborty, M. Agarwal, M. D. Ansari, S. K. Gupta, and D. 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Shang, \u201cFMSA-SC: A fine-grained multimodal sentiment analysis dataset based on stock comment videos,\u201d IEEE Trans. Multimedia, vol. 26, pp. 7294\u20137306, 2024, https:\/\/doi.org\/10.1109\/TMM.2024.3363641.","DOI":"10.1109\/TMM.2024.3363641"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_019","doi-asserted-by":"crossref","unstructured":"M. M. Danyal, S. S. Khan, M. Khan, S. Ullah, F. Mehmood, and I. Ali, \u201cProposing sentiment analysis model based on BERT and XLNet for movie reviews,\u201d Multimedia Tools Appl., vol. 83, pp. 64315\u201364339, 2024, https:\/\/doi.org\/10.1007\/s11042-024-18156-5.","DOI":"10.1007\/s11042-024-18156-5"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_020","doi-asserted-by":"crossref","unstructured":"R. Song, W. Shi, W. Qin, X. Xue, and H. 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Nie, \u201cA robot electronic device for multimodal emotional recognition of expressions,\u201d Paladyn, vol. 15, no. 1, p. 20220127, 2024."},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_026","doi-asserted-by":"crossref","unstructured":"T. Hayashi, S. Watanabe, Y. Zhang, T. Toda, T. Hori, R. Astudillo, and K. Takeda, \u201cBack-translation-style data augmentation for end-to-end ASR,\u201d in: 2018 IEEE Spoken Language Technology Workshop (SLT), IEEE, Conference Proceedings, 2018, pp. 426\u2013433.","DOI":"10.1109\/SLT.2018.8639619"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_027","doi-asserted-by":"crossref","unstructured":"L. Gomes, R. da Silva Torres, and M. L. C\u00c3?rtes, \u201cBERT-and TF-IDF-based feature extraction for long-lived bug prediction in floss: A comparative study,\u201d Inform. Software Tech., vol. 160, p. 107217, 2023.","DOI":"10.1016\/j.infsof.2023.107217"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_028","unstructured":"J. Teng, W. Kong, Q. Tian, and Z. Wang, \u201cText classification method based on LSTM-attention and cnn hybrid model,\u201d Comput. Eng. Appl., vol. 57, no. 14, pp. 126\u2013133, 2021."},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_029","doi-asserted-by":"crossref","unstructured":"D. Tang, B. Qin, and T. Liu, \u201cLearning semantic representations of users and products for document level sentiment classification,\u201d in: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (volume 1: long papers), Conference Proceedings, 2015, pp. 1014\u20131023.","DOI":"10.3115\/v1\/P15-1098"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_030","unstructured":"T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, 2013, arXiv: http:\/\/arXiv.org\/abs\/arXiv:1301.3781."},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_031","doi-asserted-by":"crossref","unstructured":"M. Schuster and K. K. Paliwal, \u201cBidirectional recurrent neural networks,\u201d IEEE Trans. Signal Proces., vol. 45, no. 11, pp. 2673\u20132681, 1997.","DOI":"10.1109\/78.650093"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_032","doi-asserted-by":"crossref","unstructured":"S. M. Al-Selwi, M. F. Hassan, S. J. Abdulkadir, A. Muneer, E. H. Sumiea, A. Alqushaibi, et al., \u201cRNN-LSTM: From applications to modeling techniques and beyond \u2013 systematic review,\u201d J. King Saud Univ.-Comput. Inform. Sci., vol. 36, no. 5, p. 102068, 2024. https:\/\/doi.org\/10.1016\/j.jksuci.2024.102068.","DOI":"10.1016\/j.jksuci.2024.102068"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_033","doi-asserted-by":"crossref","unstructured":"S. Liu and Q. Liu, \u201cA sentiment analysis model based on dynamic pre-training and stacked involutions,\u201d J. Supercomput., vol. 80, pp. 1\u201323, 2024.","DOI":"10.1007\/s11227-024-06052-6"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_034","doi-asserted-by":"crossref","unstructured":"P. Rasappan, M. Premkumar, G. Sinha, and K. Chandrasekaran, \u201cTransforming sentiment analysis for e-commerce product reviews: Hybrid deep learning model with an innovative term weighting and feature selection,\u201d Inform. Proces. Manag., vol. 61, no. 3, p. 103654, 2024.","DOI":"10.1016\/j.ipm.2024.103654"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_035","doi-asserted-by":"crossref","unstructured":"A. Murtadha, B. Wen, S. Pan, J. Su, L. Ao, and Y. Liu, \u201cBERT-ASC: Auxiliary-sentence construction for implicit aspect learning in sentiment analysis,\u201d Expert Syst. Appl., vol. 258, p. 125195, 2024.","DOI":"10.1016\/j.eswa.2024.125195"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_036","doi-asserted-by":"crossref","unstructured":"M. Wankhade, A. C. S. Rao, and C. Kulkarni, \u201cA survey on sentiment analysis methods, applications, and challenges,\u201d Artif. Intel. Rev., vol. 55, no. 7, pp. 5731\u20135780, 2022.","DOI":"10.1007\/s10462-022-10144-1"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_037","doi-asserted-by":"crossref","unstructured":"F. Long, K. Zhou, and W. Ou, \u201cSentiment analysis of text based on bidirectional LSTM with multi-head attention,\u201d IEEE Access, vol. 7, pp. 141960\u2013141969, 2019.","DOI":"10.1109\/ACCESS.2019.2942614"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_038","doi-asserted-by":"crossref","unstructured":"M. J. Warrens, \u201cKappa coefficients for dichotomous-nominal classifications,\u201d Adv. Data Anal. Classification, vol. 15, no. 1, pp. 193\u2013208, 2021.","DOI":"10.1007\/s11634-020-00394-8"},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_039","unstructured":"L. Han, X. Zeng, and L. Song, \u201cA novel transfer learning based on ALBERT for malicious network traffic classification,\u201d Int. J. Innovat. Comput. Inform. Control, vol. 16, no. 6, pp. 2103\u20132119, 2020."},{"key":"2025073006004612321_j_pjbr-2024-0005_ref_040","unstructured":"M. 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