Cloud decision support model for selecting hotels on TripAdvisor.com with probabilistic linguistic information

Peng, Hong-gang; Zhang, Hongyu; Wang, Jianqiang

doi:10.1016/j.ijhm.2017.10.001

Cited by 176 publications

(67 citation statements)

References 51 publications

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“…Therefore, we compared our approach with other recent approaches in Table 9. All of the studies [61][62][63][64][65][66][67][68] are very recent and are targeting different problem domains by using online reviews. All the studies either detect sentiments or the credibility of a review.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Shafqat

Byun

2019

Applied Sciences

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The accelerated growth rate of internet users and its applications, primarily e-business, has accustomed people to write their comments and reviews about the product they received. These reviews are remarkably competent to shape customers' decisions. However, in crowdfunding, where investors finance innovative ideas in exchange for some rewards or products, the comments of investors are often ignored. These comments can play a markedly significant role in helping crowdfunding platforms to battle against the bitter challenge of fraudulent activities. We take advantage of the language modeling techniques and aim to merge them with neural networks to identify some hidden discussion patterns in the comments. Our objective is to design a language modeling based neural network architecture, where Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) is used to predict discussion trends, i.e., either towards scam or non-scam. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. In order to optimize the recommendations, we used Particle Swarm Optimization (PSO) as a baseline algorithm. This module helps investors find secure projects to invest in (with the highest chances of delivery) within their preferred categories. We used prediction accuracy, an optimal number of identified topics, and the number of epochs, as metrics of performance evaluation for the proposed approach. We compared our results with simple Neural Networks (NNs) and NN-LDA based on these performance metrics. The strengths of both integrated models suggest that the proposed model can play a substantial role in a better understanding of crowdfunding comments.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Shafqat

Byun

2019

Applied Sciences

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“…It is also recognized to be the most popular independent consumer review site on the web [38]. The site provides access to a staggering 300 million reviews and opinions which have been submitted by travelers who have trekked the globe [39]. It covers 4 million businesses and properties related to accommodations, with hotels, B and Bs, and specialty lodgings accounting for 810,000 units [33].…”

Section: The Significances Of Tripadvisormentioning

confidence: 99%

Does Culture of Origin Have an Impact on Online Complaining Behaviors? The Perceptions of Asians and Non-Asians

2020

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The main purpose of this study was to analyze and compare the online complaining behavior of Asian and non-Asian hotels guests who have posted negative hotel reviews on TripAdvisor to voice their dissatisfaction towards a select set of hotel service attributes. A qualitative content analysis of texts which relied on manual coding was used while examining 2020 online complaining reviews directed at 353 UK hotels and posted by visitors originating from 63 countries. The results from the word frequency analysis reveal that both Asian and non-Asian travelers tend to put more emphasis on Booking and Reviews when posting complaints online. Based on a manual qualitative content analysis, 11 different major online complaint categories and 65 sub-categories were identified. Among its important findings, results of this study show that non-Asian guests frequently make complaints which are longer and more detailed than Asian customers. Managerial implications and opportunities for future studies are also discussed.Sustainability 2020, 12, 1838 2 of 37 booking intentions [7,8], consumer decision-making [9], consumer attitudes [10,11], and perception of trust [12]. Research on online consumer behavior has demonstrated that negative reviews hold more sway than positive reviews; therefore, consumers will give more weight to negative information when making judgments or performing decision-making tasks [13]. An example of this can be seen in a study by Casaló, Flavián, Guinalíu, and Ekinci (2015) on travelers' perceived usefulness of online reviews. The results show that the travelers find negative online reviews more useful than positive ones. Nevertheless, although the significance and the influence of online reviews is well recognized, the important question of how to understand online complaining behavior within the context of different cultural backgrounds still remains, with only a few researchers focusing on the relevant factors thus far.Customer dissatisfaction and consumer complaining behavior were quick to draw attention from the researchers of and service providers in the hospitality industry. Although consumer complaints do give hoteliers the opportunity to improve their marketing programs in ways that can enhance customer satisfaction and hotel profitability, they can also impact the hotel's reputations and potentially do great damage to the company [14]. Thus, we duly recognize the importance that must be attached to proper and applicable understandings of consumer complaint behavior. Literature has argued that complaint behavior may manifest itself differently on account of the distinct norms that are inherent to varying cultures [15]. For example, Ngai, Heung, Wong, and Chan (2007) examined the different attitudes Asian and non-Asian hotel guests held towards complaining behavior. The study found out that Asian guests were less likely to complain directly to the hotels for fear of "losing face", preferring rather to take non-direct action, such as through negative word-of-mouth. Yuksel, Kilinc, and Yuksel (2006) ...

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“…As a result, the information involved in many decision making processes includes a large amount of fuzzy information, for example, the fuzzy linguistic approach intuitionistic fuzzy sets (Atanassov [1], interval-valued intuitionistic fuzzy sets (Atanassov and Gargov [2]), type-2 fuzzy sets (Dubois and Prade [3], Mizumoto and Tanaka [4]), type-n fuzzy sets (Dubois and Prade [3]), fuzzy multisets (Yager [5]), hesitant fuzzy sets (Torra and Narukawa [6]), Pythagorean fuzzy sets (Yager [7,8]), and uncertain variable sets. Especially, since the introduction of intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets, intuitionistic fuzzy set theory has been extensively researched and applied in a wide range of fields, including similarity measures (Li and Cheng [9], Xu and Yager [10]), distance measures (Merigó et al [11][12][13], Szmidt et al [14], Tian et al [15]), linguistic decision making (Yu et al [16]), aggregation operators (Li [17], Hayat [18], Malik [19], Wang et al [20], Wang and Liu [21]), and models for multiple attribute group decision making (MAGDM) (Li [22], Nie et al [23], Peng et al [24], Yu et al [25], Zeng et al [26,27], Morente-Molinera et al [28], Idrus et al [29], Bagga et al [30], Carrasco et al [31]). On the other hand, decision information with uncertain linguistic information has also attracted a great deal of attention over the last decades.…”

Section: Introductionmentioning

confidence: 99%

Generalized Hamacher Aggregation Operators for Intuitionistic Uncertain Linguistic Sets: Multiple Attribute Group Decision Making Methods

Jin

Merigó

et al. 2019

Information

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In this paper, we consider multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of intuitionistic uncertain linguistic variables. Based on Hamacher operations, we developed several Hamacher aggregation operators, which generalize the arithmetic aggregation operators and geometric aggregation operators, and extend the algebraic aggregation operators and Einstein aggregation operators. A number of special cases for the two operators with respect to the parameters are discussed in detail. Also, we developed an intuitionistic uncertain linguistic generalized Hamacher hybrid weighted average operator to reflect the importance degrees of both the given intuitionistic uncertain linguistic variables and their ordered positions. Based on the generalized Hamacher aggregation operator, we propose a method for MAGDM for intuitionistic uncertain linguistic sets. Finally, a numerical example and comparative analysis with related decision making methods are provided to illustrate the practicality and feasibility of the proposed method.

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Cloud decision support model for selecting hotels on TripAdvisor.com with probabilistic linguistic information

Cited by 176 publications

References 51 publications

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Does Culture of Origin Have an Impact on Online Complaining Behaviors? The Perceptions of Asians and Non-Asians

Generalized Hamacher Aggregation Operators for Intuitionistic Uncertain Linguistic Sets: Multiple Attribute Group Decision Making Methods

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