Open Access
RAIRO-Oper. Res.
Volume 57, Number 3, May-June 2023
Page(s) 1125 - 1147
Published online 18 May 2023
  • X. Chen, L. Xu, Z. Liu, M. Sun and H. Luan, Joint learning of character and word embeddings, in Twenty-Fourth International Joint Conference on Artificial Intelligence (2015). [Google Scholar]
  • S. Garcia-Bordils, A. Mafla, A.F. Biten, O. Nuriel, A. Aberdam, S. Mazor, R. Litman and D. Karatzas, Out-of-vocabulary challenge report, in Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part IV. Springer Nature Switzerland, Cham (2023) 359–375. [Google Scholar]
  • H. Yang, B. Zeng, J. Yang, Y. Song and R. Xu, A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction. Neurocomputing 419 (2021) 344–356. [CrossRef] [Google Scholar]
  • X. Gu, Y. Gu and H. Wu, Cascaded convolutional neural networks for aspect-based opinion summary. Neural Process Lett. 46 (2017) 581–594. [Google Scholar]
  • A. Tamchyna and K. Veselovská, Ufal at semeval-2016 task 5: recurrent neural networks for sentence classification, in Proceedings of the 10th International Workshop on Semantic Evaluation (2016) 367–371. [Google Scholar]
  • K. Schouten and F. Frasincar, Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28 (2016) 813–830. [CrossRef] [Google Scholar]
  • S. Poria, E. Cambria and A. Gelbukh, Aspect extraction for opinion mining with a deep convolutional neural network. Knowl.-Based Syst. 108 (2016) 42–49. [Google Scholar]
  • Y. Kim, Convolutional neural networks for sentence classification, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014) 1746–1751. [Google Scholar]
  • J. Feng, S. Cai and X. Ma, Enhanced sentiment labeling and implicit aspect identification by integration of deep convolution neural network and sequential algorithm. Cluster Comput. 22 (2019) 5839–5857. [CrossRef] [Google Scholar]
  • P. Liu, S. Joty and H. Meng, Fine-grained opinion mining with recurrent neural networks and word embeddings, in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (2015) 1433–1443. [Google Scholar]
  • X. Li, L. Bing, P. Li, W. Lam and Z. Yang, Aspect term extraction with history attention and selective transformation, in Proceedings of the 27th International Joint Conference on Artificial Intelligence (2018) 4194–4200. [Google Scholar]
  • J. Zhou, Q. Chen, J.X. Huang, Q.V. Hu and L. He, Position-aware hierarchical transfer model for aspect-level sentiment classification. Inf. Sci. 513 (2020) 1–16. [Google Scholar]
  • Y. Qian, Y. Du, X. Deng, B. Ma, Q. Ye and H. Yuan, Detecting new Chinese words from massive domain texts with word embedding. J. Inf. Sci. 45 (2019) 196–211. [Google Scholar]
  • J. Pei, C. Zhang, D. Huang and J. Ma, Combining word embedding and semantic lexicon for Chinese word similarity computation, in Natural Language Understanding and Intelligent Applications. Springer International Publishing (2016) 766–777. [CrossRef] [Google Scholar]
  • Y. Li, W. Li, F. Sun and S. Li, Component-enhanced Chinese character embeddings, in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (2015) 829–834. [Google Scholar]
  • D.Q. Nguyen and K. Verspoor, Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings, in Proceedings of the BioNLP 2018 Workshop (2018) 129–136. [CrossRef] [Google Scholar]
  • H. Peng, Y. Ma, Y. Li and E. Cambria, Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowl.-Based Syst. 148 (2018) 167–176. [Google Scholar]
  • M. Zhang, B. Fan, N. Zhang, W. Wang and W. Fan, Mining product innovation ideas from online reviews. Inf. Process. Manag. 58 (2021) 102389. [Google Scholar]
  • X. Wu and H. Liao, Customer-oriented product and service design by a novel quality function deployment framework with complex linguistic evaluations. Inf. Process. Manag. 58 (2021) 102469. [Google Scholar]
  • C. Fikar, A. Mild and M. Waitz, Facilitating consumer preferences and product shelf life data in the design of e-grocery deliveries. Eur. J. Oper. Res. 294 (2021) 976–986. [CrossRef] [Google Scholar]
  • M. Halme and M. Kallio, Estimation methods for choice-based conjoint analysis of consumer preferences. Eur. J. Oper. Res. 214 (2011) 160–167. [CrossRef] [Google Scholar]
  • S.-B. Yang, S.-H. Shin, Y. Joun and C. Koo, Exploring the comparative importance of online hotel reviews’ heuristic attributes in review helpfulness: a conjoint analysis approach. J. Travel Tour Mark. 34 (2017) 963–985. [Google Scholar]
  • A. Shahin and M. Zairi, Kano model: a dynamic approach for classifying and prioritising requirements of airline travellers with three case studies on international airlines. Total Qual. Manag. Bus. 20 (2009) 1003–1028. [Google Scholar]
  • S. Lee, S. Park and M. Kwak, Revealing the dual importance and Kano type of attributes through customer review analytics. Adv. Eng. Inf. 51 (2022) 101533. [CrossRef] [Google Scholar]
  • J. Qi, Z. Zhang, S. Jeon and Y. Zhou, Mining customer requirements from online reviews: a product improvement perspective. Inf. Manage. 53 (2016) 951–963. [CrossRef] [Google Scholar]
  • N. Kalchbrenner, E. Grefenstette and P. Blunsom, A convolutional neural network for modelling sentences, in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (2014) 655–665. [Google Scholar]
  • T. Mikolov, K. Chen, G. Corrado and J. Dean, Efficient estimation of word representations in vector space. Preprint arXiv:1301.3781 (2013). [Google Scholar]
  • W. Liu, T. Xu, Q. Xu, J. Song and Y. Zu, An encoding strategy based word-character LSTM for Chinese NER, in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2019) 2379–2389. [Google Scholar]
  • Y. Zhang, Y. Wang and J. Yang, Lattice LSTM for Chinese sentence representation. IEEE-ACM Trans. Audio Speech Lang. 28 (2020) 1506–1519. [CrossRef] [Google Scholar]
  • Y. Guo, S.J. Barnes and Q. Jia, Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent dirichlet allocation. Tourism Manage. 59 (2017) 467–483. [CrossRef] [Google Scholar]
  • J.-W. Bi, Y. Liu, Z.-P. Fan and J. Zhang, Wisdom of crowds: conducting importance-performance analysis (IPA) through online reviews. Tourism Manage. 70 (2019) 460–478. [CrossRef] [Google Scholar]
  • B. Settles, Active learning literature survey (2009). [Google Scholar]
  • N. Liu, B. Shen, Aspect-based sentiment analysis with gated alternate neural network. Knowl.-Based Syst. 188 (2020) 105010. [Google Scholar]
  • R. He, W.S. Lee, H.T. Ng and D. Dahlmeier, Exploiting document knowledge for aspect-level sentiment classification, in 56th Annual Meeting of the Association-for-Computational-Linguistics (ACL) (2018) 579–585. [Google Scholar]
  • T. Liu, S. Yu, B. Xu and H. Yin, Recurrent networks with attention and convolutional networks for sentence representation and classification. Appl. Intell. 48 (2018) 3797–3806. [CrossRef] [Google Scholar]
  • J. Nowak, A. Taspinar and R. Scherer, LSTM recurrent neural networks for short text and sentiment classification, in International Conference on Artificial Intelligence and Soft Computing, Springer International Publishing (2017) 553–562. [Google Scholar]
  • L.N. Smith, A disciplined approach to neural network hyper-parameters: Part 1 – learning rate, batch size, momentum, and weight decay. Preprint arXiv:1803.09820 (2018). [Google Scholar]
  • C. Dos Santos and M. Gatti, Deep convolutional neural networks for sentiment analysis of short texts, in Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (2014) 69–78. [Google Scholar]
  • J. Li 中文褒贬义词典v1.0 (2011). [Google Scholar]
  • S. Xiao, C.-P. Wei and M. Dong, Crowd intelligence: analyzing online product reviews for preference measurement. Inf. Manage. 53 (2016) 169–182. [CrossRef] [Google Scholar]
  • K.O. Cowart and R.E. Goldsmith, The influence of consumer decision-making styles on online apparel consumption by college students. Int. J. Consum. Stud. 31 (2007) 639–647. [CrossRef] [Google Scholar]
  • J.L. Joines, C.W. Scherer and D.A. Scheufele, Exploring motivations for consumer web use and their implications for e-commerce. J. Consum. Mark. 20 (2003) 90–108. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.