| Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory Y Liu, JW Bi, ZP Fan Information Fusion 36, 149-161, 2017 | 328 | 2017 |
| Wisdom of crowds: Conducting importance-performance analysis (IPA) through online reviews JW Bi, Y Liu, ZP Fan, J Zhang Tourism Management 70, 460-478, 2019 | 298 | 2019 |
| Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms Y Liu, JW Bi, ZP Fan Expert Systems with Applications 80, 323-339, 2017 | 231 | 2017 |
| A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm Y Liu, JW Bi, ZP Fan Information Sciences 394, 38-52, 2017 | 202 | 2017 |
| Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model JW Bi, Y Liu, ZP Fan, E Cambria International Journal of Production Research 57 (22), 7068-7088, 2019 | 191 | 2019 |
| Exploring asymmetric effects of attribute performance on customer satisfaction in the hotel industry JW Bi, Y Liu, ZP Fan, J Zhang Tourism Management 77, 104006, 2020 | 151 | 2020 |
| Daily tourism volume forecasting for tourist attractions JW Bi, Y Liu, H Li Annals of Tourism Research 83, 102923, 2020 | 148 | 2020 |
| Tourism demand forecasting with time series imaging: A deep learning model JW Bi, H Li, ZP Fan Annals of tourism Research 90, 103255, 2021 | 91 | 2021 |
| A method for ranking products through online reviews based on sentiment classification and interval-valued intuitionistic fuzzy TOPSIS Y Liu, JW Bi, ZP Fan International Journal of Information Technology & Decision Making 16 (06 …, 2017 | 86 | 2017 |
| Representing sentiment analysis results of online reviews using interval type-2 fuzzy numbers and its application to product ranking JW Bi, Y Liu, ZP Fan Information Sciences 504, 293-307, 2019 | 81 | 2019 |
| Forecasting daily tourism demand for tourist attractions with big data: an ensemble deep learning method JW Bi, C Li, H Xu, H Li Journal of Travel Research 61 (8), 1719-1737, 2022 | 36 | 2022 |
| The construction of the affinity-seeking strategies of Airbnb homestay hosts H Qiu, D Chen, JW Bi, J Lyu, Q Li International Journal of Contemporary Hospitality Management 34 (3), 861-884, 2022 | 34 | 2022 |
| 基于 SPSS 多元回归分析的回采工作面瓦斯涌出量预测 毕建武， 贾进章， 刘丹 安全与环境学报 13 (5), 183-186, 2013 | 31 | 2013 |
| International tourism demand forecasting with machine learning models: The power of the number of lagged inputs JW Bi, TY Han, H Li Tourism Economics 28 (3), 621-645, 2022 | 28 | 2022 |
| Hotel booking through online travel agency: Optimal Stackelberg strategies under customer-centric payment service GX Gao, JW Bi Annals of Tourism Research 86, 103074, 2021 | 28 | 2021 |
| A deep neural networks based recommendation algorithm using user and item basic data JW Bi, Y Liu, ZP Fan International journal of machine learning and cybernetics 11 (4), 763-777, 2020 | 28 | 2020 |
| Personalized travel recommendation: a hybrid method with collaborative filtering and social network analysis JL Chang, H Li, JW Bi Current Issues in Tourism 25 (14), 2338-2356, 2022 | 25 | 2022 |
| Perceived crowding and festival experience: The moderating effect of visitor-to-visitor interaction H Cheng, Q Liu, JW Bi Tourism Management Perspectives 40, 100888, 2021 | 24 | 2021 |
| The impact of public health emergencies on hotel demand-Estimation from a new foresight perspective on the COVID-19 LY He, H Li, JW Bi, JJ Yang, Q Zhou Annals of Tourism Research 94, 103402, 2022 | 21 | 2022 |
| Ranking hotels through multi-dimensional hotel information: A method considering travelers’ preferences and expectations JW Bi, TY Han, Y Yao, H Li Information Technology & Tourism 24 (1), 127-155, 2022 | 20 | 2022 |
