What’s Vs. How’s in Online Hotel Reviews: Comparing Information Value of Content and Writing Style with Machine Learning

Shin, Seung-Hun; Du, Qianzhou; Xiang, Zheng

doi:10.1007/978-3-030-05940-8_25

Cited by 10 publications

(6 citation statements)

References 30 publications

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“…A study used content and style features extracted from online hotel reviews to analyze their impact on helpfulness. Textual features were reported as the key features for predicting the helpfulness of online hotel reviews [42]. The impact of review numerical and textual features on the helpfulness of three types of reviews were analyzed.…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

et al. 2020

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The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract people's attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation, linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R 2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers. INDEX TERMS Business reputation, rating behavior, review helpfulness, user profiling, crowd-sourced reviews.

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Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

et al. 2020

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“…The textual features of the review were examined using ML models, i.e., RandF, Naïve Bayes (NB), etc., to identify the quality of hotel reviews available on TripAdvisor. The stylistic features were reported as a more important determinant of review helpfulness, however, by combining stylistic features with content features, produced better prediction results [52].…”

Section: Literature Reviewmentioning

confidence: 99%

Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews

et al. 2019

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With easy access to the Internet and the popularity of online review platforms, the volume of crowd-sourced reviews is continuously rising. Many studies have acknowledged the importance of reviews in making purchase decisions. The consumer’s feedback plays a vital role in the success or failure of a business. The number of studies on predicting helpfulness and ranking reviews is increasing due to the increasing importance of reviews. However, previous studies have mainly focused on predicting helpfulness of “reviews” and “reviewer”. This study aimed to profile cumulative helpfulness received by a business and then use it for business ranking. The reliability of proposed cumulative helpfulness for ranking was illustrated using a dataset of 1,92,606 businesses from Yelp.com. Seven business and four reviewer features were identified to predict cumulative helpfulness using Linear Regression (LNR), Gradient Boosting (GB), and Neural Network (NNet). The dataset was subdivided into 12 datasets based on business categories to predict the cumulative helpfulness. The results reported that business features, including star rating, review count and days since the last review are the most important features among all business categories. Moreover, using reviewer features along with business features improves the prediction performance for seven datasets. Lastly, the implications of this study are discussed for researchers, review platforms and businesses.

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“…However, none of the above approaches have applied topic modeling, and sentiment analysis approaches to examine the concept of user experience and niche tourism, as well as the tourists' perception of those concepts. More user-oriented research has focused on predicting reviews' usefulness [15,16], identifying suitable attractions' recommendations for users [17,18], and extracting certain user profiles [19]. While these works study individual user behavior, they do not examine the correlation between user profiles and tourist experiences to better understand different market segments.…”

Section: Natural Language Processing and The Tourism Domainmentioning

confidence: 99%

Experience Economy Perspective on Recreational Fishing Tourism Travelers’ Reviews: A Data Science Approach

Yfantidou,

Karanatsiou

et al. 2024

Preprint

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This study explores the Experience Economy dimensions within fishing tourism, adopting a data science perspective by employing web crawling for extensive data gathering on reviewer profiles from TripAdvisor. Utilizing Natural Language Processing (NLP) techniques, it scrutinizes the relationship between Experience Economy dimensions and user profiling aspects. Findings reveal "Entertainment" as the predominant dimension in tourist reviews, followed by "Aesthetic," "Educational," and "Escapist." Particularly notable are the frequent co-occurrences of "Entertainment"-"Aesthetic" and "Educational"-"Entertainment" pairs. The practical implications underscore the potential socioeconomic benefits of fishing tourism for local communities and fishermen, emphasizing the necessity for governmental support in terms of infrastructure, leadership, legislation, and financial backing to foster sustainable development in this sector. Notably, this research stands as a pioneering effort in its focus on Experience Economy dimensions and user profiling within the context of fishing tourism, drawing data from business pages and user profiles.

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What’s Vs. How’s in Online Hotel Reviews: Comparing Information Value of Content and Writing Style with Machine Learning

Cited by 10 publications

References 30 publications

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews

Experience Economy Perspective on Recreational Fishing Tourism Travelers’ Reviews: A Data Science Approach

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