[Objective] This paper identifies user preferences based on their reviews of the catering providers, aiming to find and improve the un-satisfactory products or services. [Methods] Firstly, we retrieved user reviews on catering industry from the DianPing website to pre-train unsupervised corpus. Then, we fine-tuned the pre-training language model with a small amount of label data. Finally, we quantified the sentiment scores of attributes from user reviews and combined the KANO model to analyze their preferences for products or services. [Results] We successfully identified user preferences with their reviews. [Limitations] The KANO model might yield some inaccurate overall preference analysis. [Conclusions] The proposed method could effectively reveal user preferences with the help of reviews and some label data.
Terjesen S, Patel P C . In Search of Process Innovations: The Role of Search Depth, Search Breadth, and the Industry Environment[J]. Journal of Management, 2015,43(5):1421-1446.
doi: 10.1177/0149206315575710
( Yu Xianyun, Zhou Qing . Impact of External Search Tactics and Knowledge Absorptive Capacity on Technological Innovation Performance[J]. Science Research Management, 2018,39(8):11-18.)
[3]
Liang R, Guo W, Yang D . Mining Product Problems from Online Feedback of Chinese Users[J]. Kybernetes, 2017,46(3):572-586.
doi: 10.1108/K-03-2016-0048
[4]
Netzer O, Feldman R, Goldenberg J , et al. Mine Your Own Business: Market-Structure Surveillance Through Text Mining[J]. Marketing Science, 2012,31(3):521-543.
doi: 10.1287/mksc.1120.0713
( Tang Xiaobo, Liu Guangchao . Research Review on Fine-grained Sentiment Analysis[J]. Library and Information Service, 2017,61(5):132-140.)
[6]
Chen Z, Mukherjee A, Liu B. Aspect Extraction with Automated Prior Knowledge Learning[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2014: 347-358.
[7]
Moghaddam S, Ester M. Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews[C]// Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, 2010: 1825-1828.
( He Youshi, He Shufang . Sentiment Mining of Online Product Reviews Based on Domain Ontology[J]. Data Analysis and Knowledge Discovery, 2018,2(8):60-68.)
[9]
Fan F, Feng Y, Zhao D. Multi-grained Attention Network for Aspect-Level Sentiment Classification[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2018: 3433-3442.
[10]
Schmitt M, Steinheber S, Schreiber K, et al. Joint Aspect and Polarity Classification for Aspect-Based Sentiment Analysis with End-to-End Neural Networks[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2018: 1109-1114.
( Yu Bengong, Zhang Peixing, Xu Qingtang . Selecting Products Based on F-BiGRU Sentiment Analysis[J]. Data Analysis and Knowledge Discovery, 2018,2(9):22-30.)
[12]
Quan C, Ren F . Unsupervised Product Feature Extraction for Feature-Oriented Opinion Determination[J]. Information Sciences, 2014,272:16-28.
doi: 10.1016/j.ins.2014.02.063
[13]
Suleman K, Vechtomova O . Discovering Aspects of Online Consumer Reviews[J]. Journal of Information Science, 2015,42(4):492-506.
doi: 10.1177/0165551515595742
[14]
Law D, Gruss R, Abrahams A S . Automated Defect Discovery for Dishwasher Appliances from Online Consumer Reviews[J]. Expert Systems with Applications, 2017,67:84-94.
doi: 10.1016/j.eswa.2016.08.069
[15]
Guo Y, Barnes S J, Jia Q . Mining Meaning from Online Ratings and Reviews: Tourist Satisfaction Analysis Using Latent Dirichlet Allocation[J]. Tourism Management, 2017,59:467-483.
doi: 10.1016/j.tourman.2016.09.009
[16]
Jeong B, Yoon J, Lee J , et al. Social Media Mining for Product Planning: A Product Opportunity Mining Approach Based on Topic Modeling and Sentiment Analysis[J]. International Journal of Information Management, 2019,48:280-290.
doi: 10.1016/j.ijinfomgt.2017.09.009
[17]
Fiore A M . The Digital Consumer: Valuable Partner for Product Development and Production[J]. Clothing and Textiles Research Journal, 2008,26(2):177-190.
[18]
Bengio Y, Ducharme R, Vincent P , et al. A Neural Probabilistic Language Model[J]. Journal of Machine Learning Research, 2003,3:1137-1155.
[19]
Merity S, Keskar N S, Socher R. Regularizing and Optimizing LSTM Language Models[C]// Proceedings of the 6th International Conference on Learning Representations. 2018.
[20]
Melis G, Dyer C, Blunsom P. On the State of the Art of Evaluation in Neural Language Models[C]// Proceedings of the 6th International Conference on Learning Representations. 2018.
[21]
Min S, Seo M J, Hajishirzi H. Question Answering Through Transfer Learning from Large Fine-grained Supervision Data[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2017: 510-517.
[22]
Dai A M, Le Q V. Semi-supervised Sequence Learning[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015: 3079-3087.
[23]
Howard J, Ruder S. Universal Language Model Fine-tuning for Text Classification[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2018: 328-339.
[24]
Peters M E, Neumann M, Iyyer M, et al. Deep Contextualized Word Representations[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2018: 2227-2237.
[25]
Devlin J, Chang M, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2019: 4171-4186.
[26]
Li S, Zhao Z, Hu R, et al. Analogical Reasoning on Chinese Morphological and Semantic Relations[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2018: 138-143.
[27]
Joulin A, Grave E, Bojanowski P, et al. Bag of Tricks for Efficient Text Classification[C]// Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2017: 427-431.
[28]
Kim Y. Convolutional Neural Networks for Sentence Classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2014: 1746-1751.
[29]
Wang Y, Huang M, Zhu X, et al. Attention-based LSTM for Aspect-level Sentiment Classification[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2016: 606-615.
[30]
Xue W, Li T. Aspect Based Sentiment Analysis with Gated Convolutional Networks[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia. Association for Computational Linguistics, 2018: 2514-2523.
[31]
Meng Q, Jiang X. A Method for Rating Customer Requirements' Final Importance in QFD Based on Quantitative Kano Model[C]// Proceedings of the 8th International Conference on Service Systems and Service Management. IEEE, 2011: 1-6.