scholarly journals Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model

2022 ◽  
Vol 12 (2) ◽  
pp. 692
Author(s):  
Yanyan Chen ◽  
Yumei Zhong ◽  
Sumin Yu ◽  
Yan Xiao ◽  
Sining Chen
Keyword(s):  
Sentiment Analysis ◽  
Poor Performance ◽  
Online Reviews ◽  
Kano Model ◽  
Analysis Techniques ◽  
Sentiment Lexicon ◽  
Data Source ◽  
Hotel Managers ◽  
Hotel Booking ◽  
Practical Implications

As people increasingly make hotel booking decisions relying on online reviews, how to effectively improve customer ratings has become a major point for hotel managers. Online reviews serve as a promising data source to enhance service attributes in order to improve online bookings. This paper employs online customer ratings and textual reviews to explore the bidirectional performance (good performance in positive reviews and poor performance in negative reviews) of hotel attributes in terms of four hotel star ratings. Sentiment analysis and a combination of the Kano model and importance-performance analysis (IPA) are applied. Feature extraction and sentiment analysis techniques are used to analyze the bidirectional performance of hotel attributes in terms of four hotel star ratings from 1,090,341 online reviews of hotels in London collected from TripAdvisor.com (accessed on 4 January 2022). In particular, a new sentiment lexicon for hospitality domain is built from numerous online reviews using the PolarityRank algorithm to convert textual reviews into sentiment scores. The Kano-IPA model is applied to explain customers’ rating behaviors and prioritize attributes for improvement. The results provide determinants of high/low customer ratings to different star hotels and suggest that hotel attributes contributing to high/low customer ratings vary across hotel star ratings. In addition, this paper analyzed the Kano categories and priority rankings of six hotel attributes for each star rating of hotels to formulate improvement strategies. Theoretical and practical implications of these results are discussed in the end.

The effect of management response similarity on online hotel booking

2019 ◽  
Vol 31 (7) ◽  
pp. 2739-2758 ◽  
Author(s):  
Zili Zhang ◽  
Hengyun Li ◽  
Fang Meng ◽  
Yuanshuo Li
Keyword(s):  
New York ◽  
Secondary Data ◽  
Online Reviews ◽  
Hotel Management ◽  
Initial Attempt ◽  
Content Type ◽  
Hotel Managers ◽  
Hotel Booking ◽  
First Time ◽  
Practical Implications

Purpose This paper aims to examine the influences of the number of hotel management responses and especially the textual similarity in hotel management responses to online reviews on hotel online booking. Design/methodology/approach This study used the data from 437 hotels in New York City on Expedia. The data specifically include online reviews, management responses and real-time number of online hotel bookings, which were merged to create one dataset for this study. To calculate the management response similarity, three widely recognized text mining functions of calculating textual similarity were adopted in this model. Fixed-effect panel data model was then used to examine the influence of management response to consumer online reviews on online hotel booking volume. Findings The empirical results demonstrate that the number of management responses to consumer online reviews does not significantly affect hotel booking; compared to none or only one management response, or management responses with low similarity, management responses with high similarity can significantly reduce the hotel booking on Expedia. Practical implications This study suggests that the similarity of management responses influences customers’ hotel booking, and hotel managers should avoid providing too similar management responses. Originality/value First, this study, for the first time, proposes the concept of management response similarity and its measurement methods. Second, this study takes an initial attempt to empirically test the influence of response similarity on hotel booking by using secondary data online.

The role of key online reviews in affecting online hotel booking: an empirical investigation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dong Zhang ◽  
Pengkun Wu ◽  
Chong Wu
Keyword(s):  
Binary Logistic Regression ◽  
Online Reviews ◽  
Limited Attention ◽  
Content Type ◽  
Factors Affecting ◽  
Mediating Role ◽  
Antecedents And Consequences ◽  
Hotel Managers ◽  
Hotel Booking

Purpose The importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking.Design/methodology/approach To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking.Findings This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking.Originality/value This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

Sentiment Classification

2015 ◽  
pp. 1-26
Author(s):  
Jalel Akaichi
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Training Model ◽  
Sentiment Classification ◽  
The Other ◽  
Support Vector ◽  
Analysis Techniques ◽  
Sentiment Lexicon ◽  
And Behavior

In this work, we focus on the application of text mining and sentiment analysis techniques for analyzing Tunisian users' statuses updates on Facebook. We aim to extract useful information, about their sentiment and behavior, especially during the “Arabic spring” era. To achieve this task, we describe a method for sentiment analysis using Support Vector Machine and Naïve Bayes algorithms, and applying a combination of more than two features. The output of this work consists, on one hand, on the construction of a sentiment lexicon based on the Emoticons and Acronyms' lexicons that we developed based on the extracted statuses updates; and on the other hand, it consists on the realization of detailed comparative experiments between the above algorithms by creating a training model for sentiment classification.

Dimensions of wine hotel experiences shared online

2019 ◽  
Vol 32 (1) ◽  
pp. 59-77 ◽  
Author(s):  
Ana Brochado ◽  
Mike Troilo ◽  
Helena Rodrigues ◽  
Fernando Oliveira-Brochado
Keyword(s):  
Market Segmentation ◽  
Food Service ◽  
Online Reviews ◽  
Tourism Industry ◽  
Content Type ◽  
Wine Tourism ◽  
Tourism Experience ◽  
Hotel Managers ◽  
Practical Implications ◽  
Present Understanding

Purpose The purpose of this study sought to identify the main themes linked with wine hotel experiences, based on tourists’ narratives shared online, and to investigate whether these narratives vary according to traveler type. Design/methodology/approach Content analysis was carried out on 4,114 online reviews of 52 wine hotels located in 27 wine regions across 11 nations in both the Old and New World. Findings The analysis of these web reviews revealed that narratives can be grouped under 11 themes organized into 7 main dimensions as follows: wine, lodging (i.e. hotel, area and room), food service (i.e. restaurant and breakfast), scenery (i.e. views and vineyards), staff, transportation and recommendation. The main narratives vary according to traveler type. Practical implications Improving the present understanding of wine tourists’ experiences should help wine hotel managers find new approaches to enhancing visitors’ satisfaction. As the dimensions of wine tourism experiences shared online vary according to traveler type, wine managers can design their offer to target families, couples, friends, solo and corporate clients. Originality/value Prior research has identified the need for market segmentation in the wine tourism industry. This research addresses this need by specifying the wine tourism experience according to traveler type. The breadth of the data, and the method of using travelers’ own testimony as opposed to more common surveying are additional contributions for both academics and managers.

The embedded feelings in local gastronomy: a sentiment analysis of online reviews

2020 ◽  
Vol 11 (3) ◽  
pp. 461-478
Author(s):  
Chung-En Yu ◽  
Xinyu Zhang
Keyword(s):  
Sentiment Analysis ◽  
Design Methodology ◽  
Online Reviews ◽  
The Novel ◽  
Content Type ◽  
Different Seasons ◽  
Methodological Procedures ◽  
Positive Feelings ◽  
Practical Implications

Purpose This study aims to quantify the underlying feelings of online reviews and discover the role of seasonality in customer dining experiences. Design/methodology/approach This study applied sentiment analysis to determine the polarity of a given comment. Furthermore, content analysis was conducted based on the core attributes of the customer dining experiences. Findings Positive feelings towards the food and the service do not show a linear relationship, while the overall dining experiences increase in line with the positive feelings on food quality. Moreover, feelings towards the atmosphere of the restaurants are the most positive in peak season. Practical implications This study provides guidelines for restaurateurs regarding the aspects that need more attention in different seasons. Originality/value The paper contributes to the knowledge of customer feelings in local restaurants/gastronomy and the role seasonality plays in fostering such feelings. In addition, the novel methodological procedures provide insights for tourism research in discovering new dimensions in theories based on big data.

scholarly journals MODELLING CONSUMER SATISFACTION BASED ON ONLINE REVIEWS USING THE IMPROVED KANO MODEL FROM THE PERSPECTIVE OF RISK ATTITUDE AND ASPIRATION

2021 ◽  
Vol 0 (0) ◽  
pp. 1-33
Author(s):  
Meng Zhao ◽  
Chen-xi Zhang ◽  
Yi-qi Hu ◽  
Ze-shu Xu ◽  
Hao Liu
Keyword(s):  
Consumer Satisfaction ◽  
Risk Attitude ◽  
Online Reviews ◽  
Risk Attitudes ◽  
Service Improvement ◽  
Product Attributes ◽  
Kano Model ◽  
Total Utility ◽  
Hotel Service ◽  
Data Source

With the development of e-commerce, an increasing number of online reviews can serve as a promising data source for enterprises to improve online products. This paper proposes a method for modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration. Firstly, the attributes concerned by consumers are extracted from online reviews, and sentiment analysis of the extracted attributes is carried out using Standford CoreNLP. Secondly, to identify the types of product attributes, an improved Kano model is proposed based on the effects of product attributes on consumer total utility. On this basis, different attribute types are illustrated from the perspective of risk attitude. Then, the consumer aspirations are mined based on the risk attitudes of different attributes and the attribute impact on consumer satisfaction. According to the risk attitudes and aspirations of different attributes, the quantified satisfaction functions are constructed to provide more objective and accurate improvement suggestions. Finally, the proposed method is applied to the hotel service improvement to illustrate the effectiveness.

Aspect-based sentiment analysis for online reviews with hybrid attention networks

2021 ◽  
Author(s):  
Yuming Lin ◽  
Yu Fu ◽  
You Li ◽  
Guoyong Cai ◽  
Aoying Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Attention Networks
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