Hotel online reviews: creating a multi-source aggregated index

António, Nuno; Almeida, Ana de; Nunes, Luís; Batista, Fernando; Ribeiro, Ricardo

doi:10.1108/ijchm-05-2017-0302

Cited by 31 publications

(17 citation statements)

References 41 publications

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“…The only downside is that it is slightly away from the center”), which is a sentence that is not fully positive. Therefore, as expected, the results presented in Table 8 show that there is a difference between the results of the quantitative rating ( RevRating ) and the sentiment of the textual component ( RevSentimentStrength ) (Antonio et al 2018b). Nevertheless, in the 48 combinations of categories, results only differ in 7 of them.…”

Section: Data Analysis and Resultssupporting

confidence: 77%

The Influence of Geographic and Psychic Distance on Online Hotel Ratings

Phillips

António

Almeida

et al. 2019

Journal of Travel Research

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This study examines the relationship between distance measures and a Portuguese data set consisting of 34,622 online hotel reviews extracted from Booking.com and TripAdvisor written in Portuguese, Spanish, and English. Based on the country of origin of each review author, a geographic and a psychic distance measure is calculated for Portugal. Data and text mining analysis provides additional insights into online hotel ratings. The authors confirm that online travelers’ evaluations are multifaceted constructs displaying varying patterns of rating behavior among the traveler base. By investigating the contemporary relevance of geographic and psychic distance, a key finding of this study is that travelers with less distance both in terms of psychic and geographic distance give a lower rating score than travelers with greater distance. The inclusion of psychic and geographic distance is advocated as a salient aspect for future researchers and for those practitioners who wish to enhance hotel product and service features.

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Section: Data Analysis and Resultssupporting

confidence: 77%

The Influence of Geographic and Psychic Distance on Online Hotel Ratings

Phillips

António

Almeida

et al. 2019

Journal of Travel Research

Self Cite

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“…However, there are hardly any studies that have analyzed the credibility of the online hotel reviews. Further, prior studies have considered the source aspect in online hotel review context (Antonio et al , 2018; Korfiatis and Poulos, 2013). However, there are hardly any studies that have considered all the three factors, namely, source, receiver and message to analyze the credibility of the information.…”

Section: Introductionmentioning

confidence: 99%

Perceived credibility of online hotel reviews and its impact on hotel booking intentions

Chakraborty

2019

IJCHM

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Purpose This study aims to evaluate the vitality of source, receiver and message factors on credibility assessment of online hotel reviews. Further, the current study determines the effects of perceived credibility of online reviews (PCOR) on hotel booking intentions (HBI). Design/methodology/approach The current study performs reliability analysis to determine the internal consistency of the measurement scales and to ensure the uni-dimensionality of the measurement scales the present study performs exploratory factor analysis. Further, the present study performs structural equation modeling to identify the relationships between the variables. Findings Two-sided online reviews have a more significant positive impact on PCOR as compared to the effects of receiver, review quality, review consistency and negative-sided online reviews. Originality/value This is one of the first study that analyses the impact of source, receiver and message on PCOR. Moreover, the present study offered theoretical justification behind the factors that affect the authenticity of online hotel reviews and its effects on HBI.

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“…ML (also known as AI) has rapidly grown with big data by providing solutions to obtain useful insights, predictions and decisions from vast amounts of data (Jordan and Mitchell, 2015). ML allows a computer to discern meaningful patterns without researchers' Hospitality big data analytics involvement in each process (Balducci and Marinova, 2018) and provides a better predictive power (Antonio et al, 2018;Chatterjee, 2020). The development of new and improved ML algorithms allows structured and unstructured data exploration and superior predictions based on big data (i.e.…”

Section: Prediction Model For Review Helpfulness: Big Data Analytics Through Machine Learningmentioning

confidence: 99%

Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making

Lee

Kwon

Back

2021

IJCHM

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Purpose Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making. Findings By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity. Practical implications The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results. Originality/value To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

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Hotel online reviews: creating a multi-source aggregated index

Cited by 31 publications

References 41 publications

The Influence of Geographic and Psychic Distance on Online Hotel Ratings

The Influence of Geographic and Psychic Distance on Online Hotel Ratings

Perceived credibility of online hotel reviews and its impact on hotel booking intentions

Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making

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