HOTEL SELECTION UTILIZING ONLINE REVIEWS: A NOVEL DECISION SUPPORT MODEL BASED ON SENTIMENT ANALYSIS AND DL-VIKOR METHOD

With the considerable development of tourism market, as well as the expansion of the e-commerce platform scale, increasing tourists often prefer to select tourism products such as services or hotels online. Thus, it needs to provide an efficient decision support model for tourists to select tourism products. Online reviews based on the user experience would help tourists improve decision efficiency on tourism products. Therefore, in this study, a quantitative method for hotel selection with online reviews is proposed. First, with respect this problem with online reviews, by analyzing sentiment words in online reviews, tourists’ sentiment preferences are transformed into the format of distribution linguistic with respect to sentiment levels. Second, from a theoretical perspective, we proposed a method to determine the ideal solution and nadir solution for distribution linguistic evaluations. Next, based on the frequency of words for evaluating hotel and the distribution linguistic evaluations, the weight vector of the evaluation features is determined. Further, a novel DL-VIKOR method is developed to rank and then to select hotels. Finally, a realistic case from TripAdvisor.com for selecting hotel is used to demonstrate practically and feasibility of the proposed model.

Download Full-text

Decision Support Model for Ecological Operation of Reservoirs Based on Dynamic Bayesian Network

Water ◽

10.3390/w13121658 ◽

2021 ◽

Vol 13 (12) ◽

pp. 1658

Author(s):

Tao Zhou ◽

Zengchuan Dong ◽

Xiuxiu Chen ◽

Qihua Ran

Keyword(s):

Decision Support ◽

Bayesian Network ◽

Reservoir Operation ◽

Dynamic Bayesian Network ◽

Operation Model ◽

Decision Support Model ◽

Model Based ◽

Proposed Model ◽

Ecological Targets ◽

Support Model

In this study, a model was proposed based on the sustainable boundary approach, to provide decision support for reservoir ecological operation with the dynamic Bayesian network. The proposed model was developed in four steps: (1) calculating and verifying the sustainable boundaries in combination with the ecological objectives of the study area, (2) generating the learning samples by establishing an optimal operation model and a Monte Carlo simulation model, (3) establishing and training a dynamic Bayesian network by learning the examples and (4) calculating the probability of the economic and ecological targets exceeding the set threshold from time to time with the trained dynamic Bayesian network model. Using the proposed model, the water drawing of the reservoir can be adjusted dynamically according to the probability of the economic and ecological targets exceeding the set threshold during reservoir operation. In this study, the proposed model was applied to the middle reaches of Heihe River, the effect of water supply proportion on the probability of the economic target exceeding the set threshold was analyzed, and the response of the reservoir water storage in each period to the probability of the target exceeding the set threshold was calculated. The results show that the risks can be analyzed with the proposed model. Compared with the existing studies, the proposed model provides guidance for the ecological operation of the reservoir from time to time and technical support for the formulation of reservoir operation chart. Compared with the operation model based on the designed guaranteed rate, the reservoir operation model based on uncertainty reduces the variation range of ecological flow shortage or the overflow rate and the economic loss rate by 5% and 6%, respectively. Thus, it can be seen that the decision support model based on the dynamic Bayesian network can effectively reduce the influence of water inflow and rainfall uncertainties on reservoir operation.

Download Full-text

An Integrated Decision Support Model For Enhancing Continuous Improvement Of Academic Programs

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.3079 ◽

2019 ◽

Vol 9 (5) ◽

pp. 4835-4841

Author(s):

M. A. Al-Shargabi

Keyword(s):

Decision Support ◽

Teaching And Learning ◽

Vital Role ◽

Educational Process ◽

Academic Program ◽

Academic Programs ◽

Educational Goals ◽

Decision Support Model ◽

Proposed Model ◽

Support Model

Continuous Quality Improvement (CQI) in higher educational institutions is considered an aspect that plays a vital role in maintaining excellence in teaching and learning. It focuses on several critical phases such as designing Program Learning Outcomes (PLOs) assessment plans, collecting data, evaluating the of PLO achievement results, and designing and implementing improvement actions to achieve educational goals. CQI is very critical to assess and improve the educational process. As it appears, academic program decision-makers find difficult to formulate and implement an authentic CQI for their programs without the help of decision support systems (DSSs). Therefore, this paper proposes an integrated decision support model that provides CQI decision support to enhance academic programs. Moreover, it provides authentication of the CQI process by auto-checking the accuracy of the CQI phases (assessment, evaluation, designing, and implementing improvement). After the development and implementation of the proposed model in a bachelor-level academic institution, the results show that the proposed model provides decision support for enhancing academic program’s CQI, and authentication of the CQI accuracy, which will lead to the improvement of the educational process and the achievement of educational goals.

Download Full-text