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1 – 10 of over 19000
Article
Publication date: 31 May 2021

Xiaofan Lai, Fan Wang and Xinrui Wang

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the…

1398

Abstract

Purpose

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the big data technique, this paper aims to investigate the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM, and to further identify the moderating effects of review characteristics.

Design/methodology/approach

The authors first retrieve 273,457 customer-generated reviews from a well-known online travel agency in China using automated data crawlers. Next, they exploit two different sentiment analysis methods to obtain sentiment scores. Finally, empirical studies based on threshold regressions are conducted to establish the asymmetric relationship between customer sentiment and online hotel ratings.

Findings

The results suggest that the relationship between customer sentiment and online hotel ratings is asymmetric, and a negative sentiment score will exert a larger decline in online hotel ratings, compared to a positive sentiment score. Meanwhile, the reviewer level and reviews with pictures have moderating effects on the relationship between customer sentiment and online hotel ratings. Moreover, two different types of sentiment scores output by different sentiment analysis methods verify the results of this study.

Practical implications

The moderating effects of reviewer level and reviews with pictures offer new insights for hotel managers to make different customer service policies and for customers to select a hotel based on reviews from the online travel agency.

Originality/value

This paper contributes to the literature by applying big data analysis to the issues in hotel management. Based on the eWOM communication theories, this study extends previous study by providing an analysis framework for the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM.

Details

International Journal of Contemporary Hospitality Management, vol. 33 no. 6
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 4 June 2018

Maryam Ghasemaghaei, Seyed Pouyan Eslami, Ken Deal and Khaled Hassanein

The purpose of this paper is twofold: first, to identify and validate reviews’ length and sentiment as correlates of online reviews’ ratings; and second, to understand the…

2243

Abstract

Purpose

The purpose of this paper is twofold: first, to identify and validate reviews’ length and sentiment as correlates of online reviews’ ratings; and second, to understand the emotions embedded in online reviews and how they associate with specific words used in such reviews.

Design/methodology/approach

A panel data set of customer reviews was collected for auto, life, and home insurance from January 2012 to December 2015 using a web scraping technique. Using a sentiment analysis approach, 1,584 reviews for the auto, home, and life insurance services of 156 insurance companies were analyzed.

Findings

The results indicate that, since 2013, consumers have generally had more negative emotions than positive ones toward insurance services. The results also show that consumer review sentiment correlates positively and review length correlates negatively with consumer online review ratings. Furthermore, a two-way ANOVA analysis shows that, in general, short reviews with positive sentiment are associated with high review ratings.

Practical implications

The findings of this study provide service companies, in general, and insurance companies, in particular, with important guidelines that should be considered to increase consumers’ positive attitude toward their services.

Originality/value

This paper highlights the importance of sentiment analysis in identifying consumer reviews’ emotions and understanding the associations and interactions of reviews’ length and sentiment on online review rating, which can lead to improved marketing strategies.

Details

Internet Research, vol. 28 no. 3
Type: Research Article
ISSN: 1066-2243

Keywords

Article
Publication date: 11 October 2021

Fuad Mehraliyev, Irene Cheng Chu Chan and Andrei Petrovich Kirilenko

This study aims to conduct a systematic review and critically analyze the sentiment analysis literature in hospitality and tourism from methodological (data sets and analyzes) and…

3144

Abstract

Purpose

This study aims to conduct a systematic review and critically analyze the sentiment analysis literature in hospitality and tourism from methodological (data sets and analyzes) and thematic (topics, theories, key constructs and their relationships) perspectives.

Design/methodology/approach

Qualitative thematic review and quantitative systematic review were performed on 70 papers obtained from hospitality and tourism categories of two databases, namely, Web of Science and Scopus.

Findings

A total of 5 topics and 27 sub-topics were identified and the major theme is market intelligence. Sentiment variables were investigated not only as independent but also as dependent variables. The customer rating is the most investigated dependent variable, whereas moderators and mediators were rarely tested. Most reviewed studies did not use theory. The findings from the methodological review show that analysis of big data was rare. Moreover, testing the performance of sentiment analyzes was uncommon, and only one paper tested the performance of aspect/feature extraction.

Research limitations/implications

This study extends prior review studies by providing a comprehensive view of how knowledge and methodologies of sentiment analysis have developed. The identified themes and key constructs serve as a solid base for future knowledge advancement. Future research directions on sentiment analysis are also provided.

Originality/value

To the best of the authors’ knowledge, this study is the first comprehensive methodological and thematic review of sentiment analysis in hospitality and tourism. Based on the identified findings, the authors propose several directions for future research.

Details

International Journal of Contemporary Hospitality Management, vol. 34 no. 1
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 1 June 2022

Md Shamim Hossain, Mst Farjana Rahman, Md Kutub Uddin and Md Kamal Hossain

There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and…

Abstract

Purpose

There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

Design/methodology/approach

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

Findings

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

Practical implications

The results facilitate halal restaurateurs in identifying customer review behavior.

Social implications

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

Originality/value

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

Details

Journal of Islamic Marketing, vol. 14 no. 7
Type: Research Article
ISSN: 1759-0833

Keywords

Article
Publication date: 23 January 2024

Chong Wu, Zijiao Zhang, Chang Liu and Yiwen Zhang

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most…

Abstract

Purpose

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

Design/methodology/approach

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

Findings

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Originality/value

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 29 August 2023

Srinivasan Sekar, Sriji Edakkat Subhakaran and Deeptam Chattopadhyay

The purpose of this paper is to investigate employees’ perception of their organization and to examine the impact of such perception on overall employee satisfaction using…

Abstract

Purpose

The purpose of this paper is to investigate employees’ perception of their organization and to examine the impact of such perception on overall employee satisfaction using qualitative and quantitative employee reviews in an online platform.

Design/methodology/approach

The employee online reviews were classified into positive and negative sentiments using sentiment analysis. Further, multiple regression was employed to examine the impact of sentiments (positive and negative) and employee ratings on pre-defined job attributes in explaining overall employee satisfaction with the organization.

Findings

The findings of the sentiment analysis shows that employee online reviews exhibit more positive sentiments than negative sentiments. Positive sentiments show a significant positive effect, and negative sentiments show a significant inverse relationship with overall employee satisfaction. Further, pre-defined job attributes are differently related to overall employee satisfaction with the organization, skill development being the strongest predictor.

Practical implications

Practitioners may customize the organization's policies and practices based on the sentiments associated with employee reviews. Organizations emphasizing employee engagement and satisfaction are suggested to include skill development as their priority.

Originality/value

Most existing work on overall employee satisfaction is primarily based on the survey questionnaire method; only recently, online employee reviews (OERs) are gaining attention. The current research uses employee online reviews to study overall employee satisfaction.

Details

Management Decision, vol. 61 no. 11
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 17 May 2021

Sayeh Bagherzadeh, Sajjad Shokouhyar, Hamed Jahani and Marianna Sigala

Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget…

1151

Abstract

Purpose

Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget. This study aims to contribute to the field by developing and testing a new methodology for sentiment analysis that surpasses the standard dictionary-based method by creating two hotel-specific word lexicons.

Design/methodology/approach

Big data of hotel customer reviews posted on the TripAdvisor platform were collected and appropriately prepared for conducting a binary sentiment analysis by developing a novel bag-of-words weighted approach. The latter provides a transparent and replicable procedure to prepare, create and assess lexicons for sentiment analysis. This approach resulted in two lexicons (a weighted lexicon, L1 and a manually selected lexicon, L2), which were tested and validated by applying classification accuracy metrics to the TripAdvisor big data. Two popular methodologies (a public dictionary-based method and a complex machine-learning algorithm) were used for comparing the accuracy metrics of the study’s approach for creating the two lexicons.

Findings

The results of the accuracy metrics confirmed that the study’s methodology significantly outperforms the dictionary-based method in comparison to the machine-learning algorithm method. The findings also provide evidence that the study’s methodology is generalizable for predicting users’ sentiment.

Practical implications

The study developed and validated a methodology for generating reliable lexicons that can be used for big data analysis aiming to understand and predict customers’ sentiment. The L2 hotel dictionary generated by the study provides a reliable method and a useful tool for analyzing guests’ feedback and enabling managers to understand, anticipate and re-actively respond to customers’ attitudes and changes. The study also proposed a simplified methodology for understanding the sentiment of each user, which, in turn, can be used for conducting comparisons aiming to detect and understand guests’ sentiment changes across time, as well as across users based on their profiles and experiences.

Originality/value

This study contributes to the field by proposing and testing a new methodology for conducting sentiment analysis that addresses previous methodological limitations, as well as the contextual specificities of the tourism industry. Based on the paper’s literature review, this is the first research study using a bag-of-words approach for conducting a sentiment analysis and creating a field-specific lexicon.

论可推广性的情感分析法以创建酒店字典:以TripAdvisor酒店评论为样本的大数据分析

摘要

研究目的

对于在线游客评论的研究在过去的几年中与日俱增, 但是仍缺乏有效方法能在有限的时间喝预算内提供终端用户价值。本论文开发并测试了一套情感分析的新方法, 创建两套酒店相关的词库, 此方法超越了标准词典式分析法。

研究设计/方法/途径

研究样本为TripAdvisor酒店客户评论的大数据, 通过开发崭新的有配重的词库法, 来开展两极式情感分析。这个崭新的具有配重的词库法能够呈现透明化和可复制的程序, 准备、创建、并检验情感分析的词条。这个方法用到了两种词典(有配重的词典L1和手动选择的词典L2), 本论文通过对TripAdvisor大数据进行使用词类划分精准度, 来检测和验证这两种词典。本论文采用两种热门方法(公共词典法和复杂机器学习算法)来对比词典的准确度。

研究结果

精确度对比结果证实了本论文的方法, 相较于机器学习算法, 显著地超越了以字典为基础的方法。研究结果还表明, 本论文的方法可以就预测用户情感趋势进行推广。

研究实际启示

本论文开发并验证了一项方法, 这种方法通过创建可信的词典进行大数据分析, 以判定用户情感。本论文创建的L2酒店词库对分析客人反馈是可靠有用的工具, 这个词库还能帮助酒店经理了解、预测、以及积极相应客人的态度和改变。本论文还提出了一项可以了解每个用户情感的简易方法, 这项方法可以通过对比的方式来检测和了解客人不同时间的情感变化, 以及根据其不同背景和经历的不同用户之间的变化。

研究原创性/价值

本论文提出并检测了一项新方法, 这项情感分析方法可以解决之前方法的局限并立脚于旅游行业。基于文献综述, 本论文是首篇研究, 使用词库法来进行情感分析和创建特别领域词典的方式。

Details

Journal of Hospitality and Tourism Technology, vol. 12 no. 2
Type: Research Article
ISSN: 1757-9880

Keywords

Article
Publication date: 12 February 2018

Mike Thelwall

The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females.

1981

Abstract

Purpose

The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females.

Design/methodology/approach

This paper uses data sets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females.

Findings

Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis.

Research limitations/implications

Only one lexical sentiment analysis algorithm was used.

Practical implications

Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results.

Originality/value

This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.

Details

Online Information Review, vol. 42 no. 1
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 4 April 2016

Alain Yee Loong Chong, Boying Li, Eric W.T. Ngai, Eugene Ch'ng and Filbert Lee

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user…

9951

Abstract

Purpose

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales.

Design/methodology/approach

The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.

Findings

This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.

Originality/value

This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.

Details

International Journal of Operations & Production Management, vol. 36 no. 4
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 27 June 2019

Yunseon Choi

This study is part of a larger research project which aims to investigate whether sentiments in online reviews on children’s books would represent significant factors which are…

Abstract

Purpose

This study is part of a larger research project which aims to investigate whether sentiments in online reviews on children’s books would represent significant factors which are useful for selecting the right books for children. This paper aims to examine whether positive, negative or neutral attitude would be directly associated with the overall ratings of books.

Design/methodology/approach

The study investigates subjectivity and polarity of online reviews on children’s books such as neutral, positive or negative sentiment. For the investigation of a statistical association between the sentiment values and the rating scores, this study performs correlation analysis. For a clear explanation of the factors affecting the relationships between the sentiment value and the rating score, this study uses the concept-level sentiment analysis of online reviews.

Findings

The findings of this study demonstrate that there is a weak or low correlation between the sentiment value and the rating score of a book and they are hardly related for most books. The results of this study also uncover key contributing factors that affected the correlations between two variables and made the relationship weak.

Research limitations/implications

This study increases awareness of the implications of online reviews as user-generated contents for complementing the existing controlled vocabulary.

Practical implications

This study contributes to improving library catalogs by using latent topics extracted from online reviews which provide additional access points for assisting in the selection of books.

Originality/value

Although several studies have conducted on online reviews in the domain of business, no research appears to exist on the sentiment analysis of online reviews about children’s books. This study attempts to address the potential and challenges associated with using online reviews to help find the right books for children.

Details

The Electronic Library , vol. 37 no. 3
Type: Research Article
ISSN: 0264-0473

Keywords

1 – 10 of over 19000