Deep Learning-Based Truthful and Deceptive Hotel Reviews

Devbrat Gupta, Anuja Bhargava, Diwakar Agarwal, Mohammed H. Alsharif (), Peerapong Uthansakul (), Monthippa Uthansakul and Ayman A. Aly
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Devbrat Gupta: Department of Electronics & Communication, GLA University, Mathura 281406, India
Anuja Bhargava: Department of Electronics & Communication, GLA University, Mathura 281406, India
Diwakar Agarwal: Department of Electronics & Communication, GLA University, Mathura 281406, India
Mohammed H. Alsharif: Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of Korea
Peerapong Uthansakul: School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Monthippa Uthansakul: School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Ayman A. Aly: Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia

Sustainability, 2024, vol. 16, issue 11, 1-17

Abstract: For sustainable hospitality and tourism, the validity of online evaluations is crucial at a time when they influence travelers’ choices. Understanding the facts and conducting a thorough investigation to distinguish between truthful and deceptive hotel reviews are crucial. The urgent need to discern between truthful and deceptive hotel reviews is addressed by the current study. This misleading “opinion spam” is common in the hospitality sector, misleading potential customers and harming the standing of hotel review websites. This data science project aims to create a reliable detection system that correctly recognizes and classifies hotel reviews as either true or misleading. When it comes to natural language processing, sentiment analysis is essential for determining the text’s emotional tone. With an 800-instance dataset comprising true and false reviews, this study investigates the sentiment analysis performance of three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Among the training, testing, and validation sets, the CNN model yielded the highest accuracy rates, measuring 98%, 77%, and 80%, respectively. Despite showing balanced precision and recall, the LSTM model was not as accurate as the CNN model, with an accuracy of 60%. There were difficulties in capturing sequential relationships, for which the RNN model further trailed, with accuracy rates of 57%, 57%, and 58%. A thorough assessment of every model’s performance was conducted using ROC curves and classification reports.

Keywords: hotel reviews; sustainable hospitality; sustainable tourism; CNN; RNN; LSTM; data science (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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