FLORA: a hierarchical fuzzy system for online accommodation review analysis

2019 ◽  
Vol 21 (3) ◽  
pp. 347-367
Author(s):  
Thara Angskun ◽  
Jitimon Angskun
Keyword(s):  
Sentiment Analysis ◽  
Semantic Analysis ◽  
Online Reviews ◽  
Online Review ◽  
Probability Weighting ◽  
Content Type ◽  
Analysis Process ◽  
Review Analysis ◽  
Accuracy Comparison ◽  
Hierarchical Fuzzy System

Purpose This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap. Design/methodology/approach FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine. Findings The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating. Research limitations/implications This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base. Practical implications This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system. Originality/value This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

Reviews’ length and sentiment as correlates of online reviews’ ratings

2018 ◽  
Vol 28 (3) ◽  
pp. 544-563 ◽  
Author(s):  
Maryam Ghasemaghaei ◽  
Seyed Pouyan Eslami ◽  
Ken Deal ◽  
Khaled Hassanein
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
Marketing Strategies ◽  
Insurance Companies ◽  
Online Review ◽  
Data Set ◽  
Content Type ◽  
Customer Reviews ◽  
Service Companies ◽  
Consumer Review

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.

Does traveler satisfaction differ in various travel group compositions?

2018 ◽  
Vol 30 (3) ◽  
pp. 1663-1685 ◽  
Author(s):  
Xun Xu
Keyword(s):  
Semantic Analysis ◽  
Online Reviews ◽  
Influential Factors ◽  
Online Review ◽  
Content Type ◽  
Performance Factors ◽  
Segmentation Strategy ◽  
The Usa ◽  
And Performance ◽  
Group Compositions

Purpose This study aims to investigate the online customer review behavior and determinants of overall satisfaction with hotels of travelers in various travel group compositions. Design/methodology/approach The author collected data from online reviews of travelers in various travel group compositions from 600 hotels in 100 of the largest cities in the USA from Booking.com and used latent semantic analysis (LSA) to identify the positive and negative factors from online reviews of travelers in various travel group compositions. Then, text regression was used to determine the influential factors of overall satisfaction of travelers in various travel group compositions. Findings It was found in this study that not all the positive and negative textual factors mined from travelers’ online reviews significantly influenced their overall satisfaction. In addition, the determinants of traveler satisfaction were different when travelers were in different travel group compositions. Research limitations/implications The author found similar online review behavior, but different basic, excitement and performance factors of travelers in different travel group compositions. Practical implications This study helps hoteliers understand customers’ perception of the specific attributes of their products and services, which provides a guideline for businesses to design the priority rule to improve these corresponding attributes and use market segmentation strategy when dealing with customers in different travel group compositions. Originality/value The author examined and compared the online review behavior and determinants of satisfaction using the factors mined from online reviews between travelers in various travel group compositions. This study combined customer ratings with textual reviews and predicted customer ratings from the factors extracted from textual reviews using LSA and text regression.

Determining banking service attributes from online reviews: text mining and sentiment analysis

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Divya Mittal ◽  
Shiv Ratan Agrawal
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Banking Sector ◽  
Online Reviews ◽  
Socioeconomic Background ◽  
Content Type ◽  
Customer Reviews ◽  
Banking Service ◽  
The Interest Rate

PurposeThe current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.Design/methodology/approachA total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.FindingsThe study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.Research limitations/implicationsThe study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.Practical implicationsThe study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.Originality/valueThis paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

scholarly journals Which cues influence the perceived usefulness and credibility of an online review? A conjoint analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ana Isabel Lopes ◽  
Nathalie Dens ◽  
Patrick De Pelsmacker ◽  
Freya De Keyzer
Keyword(s):  
Conjoint Analysis ◽  
Product Category ◽  
Online Reviews ◽  
Perceived Usefulness ◽  
Online Review ◽  
Large Set ◽  
Relative Importance ◽  
Writing Quality ◽  
Content Type ◽  
Argument Strength

PurposeThis study aims to assess the relative importance of the argument strength, argument sidedness, writing quality, number of arguments, rated review usefulness, summary review rating and number of reviews in determining the perceived usefulness and credibility of an online review. Additionally, the authors use insights from the elaboration likelihood model (ELM) to explore the effect of consumers' product category involvement on the cues' relative importance.Design/methodology/approachA conjoint analysis (N = 287) is used to study the relative importance of the seven previously mentioned attributes. A balanced orthogonal design generated eight cards that correspond to individual reviews. Respondents scored all eight cards in a random order for perceived usefulness and credibility.FindingsOverall, argument strength is the most important cue, while summary review rating and the number of reviews are the least important for perceived review usefulness and credibility. The number of arguments is more important for people who are more highly involved with the product, while writing quality and rated review usefulness are relatively more important for the low-involvement group.Originality/valueThis study provides a comprehensive test of how consumers perceive online reviews, as it the first to the authors’ knowledge to simultaneously investigate a large set of cues using conjoint analysis. This method allows for the implicit valuation (utility) of the individual cues, revealing the cues' relative importance, in a setting that comes close to a real-life context. Besides, insights of the ELM are used to understand how the relative importance of cues differs depending on the level of review readers' product category involvement.

scholarly journals An Affordance-Based Online Review Analysis Framework

2019 ◽  
Vol 1 (1) ◽  
pp. 2457-2466
Author(s):  
Tianjun Hou ◽  
Bernard Yannou ◽  
Yann Leroy ◽  
Emilie Poirson
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
User Preferences ◽  
Data Driven ◽  
Online Review ◽  
Product Reviews ◽  
Analysis Framework ◽  
Review Analysis ◽  
Classical Models ◽  
Feedback Data

AbstractOne of the main tasks of today's data-driven design is to learn customers' concerns from the feedback data posted on the internet, to drive smarter and more profitable decisions during product development. Feature-based opinion mining was first performed by the computer and design scientists to analyse online product reviews. In order to provide more sophisticated customer feedback analyses and to understand in a deeper way customer concerns about products, the authors propose an affordance-based online review analysis framework. This framework allows understanding how and in what condition customers use their products, how user preferences change over years and how customers use the product innovatively. An empirical case study using the proposed approach is conducted with the online reviews of Kindle e-readers downloaded from amazon.com. A set of innovation leads and redesign paths are provided for the design of next-generation e-reader. This study suggests that bridging data analytics with classical models and methods in design engineering can bring success for data-driven design.

An exploratory study of online review management in hospitality services

2018 ◽  
Vol 28 (1) ◽  
pp. 79-98 ◽  
Author(s):  
Run Hong Niu ◽  
Ying Fan
Keyword(s):  
Management System ◽  
Management Practices ◽  
Measurement Model ◽  
Online Reviews ◽  
Online Review ◽  
Theory Approach ◽  
Content Type ◽  
Integrative Framework ◽  
Review Management ◽  
Key Dimensions

Purpose More and more customers refer to online reviews before making any purchasing decisions thanks to the increasing popularity of social media and online shopping. This phenomenon has caught the attention of business managers who are increasingly aware that online reviews provide great opportunities to connect with current and potential customers. However, both practices and research on online review management from the businesses’ perspective are fragmented. The purpose of this paper is to develop an integrative framework that includes the key dimensions of an online review management system. Design/methodology/approach Based on the Grounded Theory approach, the authors conducted a multiple case study by analyzing the interviews with 11 hospitality services. Findings The authors found that an online review management system should go beyond the current norm of response management to incorporate key dimensions of formality, centralization, specialization, response customization, integration and review analytics. Practical implications The study provides a systematic guideline for online review management practices. The framework can be used as a tool for a business to evaluate existing online review management practices and develop/refine its online review management system. Originality/value The study contributes to online review management literature by developing a comprehensive framework to understand the structure and processes of online review management. The key dimensions of an online review management system identified in this study provide an initial measurement model for the online review management construct. Furthermore, the study provides a springboard for future empirical validation and refinement of the key factors for effective online review management.

Product weakness finder: an opinion-aware system through sentiment analysis

2014 ◽  
Vol 114 (8) ◽  
pp. 1301-1320 ◽  
Author(s):  
Hongwei Wang ◽  
Wei Wang
Keyword(s):  
Sentiment Analysis ◽  
Comparative Evaluation ◽  
Analytical Framework ◽  
Online Reviews ◽  
Product Reviews ◽  
Network Algorithms ◽  
Content Type ◽  
Sentiment Mining ◽  
Highly Correlated ◽  
Sentiment Score

Purpose – Extant methods of product weakness detection usually depend on time-consuming questionnaire with high artificial involvement, so the efficiency and accuracy are not satisfied. The purpose of this paper is to propose an opinion-aware analytical framework – PRODWeakFinder – to expect to detect product weaknesses through sentiment analysis in an effective way. Design/methodology/approach – PRODWeakFinder detects product weakness by considering both comparative and non-comparative evaluations in online reviews. For comparative evaluation, an aspect-oriented comparison network is built, and the authority is assessed for each node by network analysis. For non-comparative evaluation, sentiment score is calculated through sentiment analysis. The composite score of aspects is calculated by combing the two types of evaluations. Findings – The experiments show that the comparative authority score and the non-comparative sentiment score are not highly correlated. It also shows that PRODWeakFinder outperforms the baseline methods in terms of accuracy. Research limitations/implications – Semantic-based method such as ontology are expected to be applied to identify the implicit features. Furthermore, besides PageRank, other sophisticated network algorithms such as HITS will be further employed to improve the framework. Practical implications – The link-based network is more suitable for weakness detection than the weight-based network. PRODWeakFinder shows the potential on reducing overall costs of detecting product weaknesses for companies. Social implications – A quicker and more effective way would be possible for weakness detection, enabling to reduce product defects and improve product quality, and thus raising the overall social welfare. Originality/value – An opinion-aware analytical framework is proposed to sentiment mining of online product reviews, which offer important implications regarding how to detect product weaknesses.

The influence of online reviews to online hotel booking intentions

2015 ◽  
Vol 27 (6) ◽  
pp. 1343-1364 ◽  
Author(s):  
Xinyuan (Roy) Zhao ◽  
Liang Wang ◽  
Xiao Guo ◽  
Rob Law
Keyword(s):  
Regression Analysis ◽  
Mainland China ◽  
Online Reviews ◽  
Online Review ◽  
Content Type ◽  
Theoretical Contribution ◽  
Review Management ◽  
Marketing Tool ◽  
Booking Intentions ◽  
Hotel Booking

Purpose – This study aims to investigate the impacts of online review and source features upon travelers’ online hotel booking intentions. Design/methodology/approach – This study developed a research model and empirically examined the model by collecting data from business travelers in the Mainland China. Factor analysis was adopted to identify features of online reviews content and source attribute. Regression analysis was used to examine impacts of these attributes upon travelers’ online booking intention. Findings – Six features of online reviews content and one source attribute were identified, namely, usefulness, reviewer expertise, timeliness, volume, valence (negative and positive) and comprehensiveness. Regression analysis results testified positive causal relationships between usefulness, reviewer expertise, timeliness, volume and comprehensiveness and respondents’ online booking intentions. A significantly negative relation between negative online reviews and online booking intentions was identified, whereas impacts from positive online reviews upon booking intentions were not statistically significant. Research limitations/implications – The major limitation of this study is that interrelationships among features of online reviews, which were discussed in other similar studies, were not considered. Still, this study benefited researchers from scrutinizing features of online reviews, rather than several of them. As such, it offered more comprehensive suggestions for practitioners in how to better utilize online reviews as a marketing tool. Practical implications – Hospitality practitioners could enhance consumer review management by applying the six underlying factors of online review in the present study to find out the ways of increasing consumers’ booking intentions in the specific hotel contexts. Originality/value – A major theoretical contribution of this paper is its comprehensiveness in examining features of review content as well as its source simultaneously. This study also offered areas worthy of more research efforts from perspectives of practitioners and researchers.

Brand equity, online reviews, and message trust: the moderating role of persuasion knowledge

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fayez Ahmad ◽  
Francisco Guzmán
Keyword(s):  
Brand Equity ◽  
Experimental Studies ◽  
Online Reviews ◽  
Review Literature ◽  
Online Review ◽  
Persuasion Knowledge ◽  
Content Type ◽  
Mediating Role ◽  
The Relationship

Purpose This paper aims to investigate whether a message from a brand with stronger brand equity generates more trust than a message from a brand with lower brand equity, and thus is more likely to encourage consumers to write online reviews. This paper also explores what happens when consumers become aware that brands are trying to persuade them to write a review. Design/methodology/approach Through three experimental studies, where participants were randomly assigned to a brand that has either a stronger or weaker brand equity, participants’ intention to write reviews was measured. Trust in the message was measured to study its mediating role, and persuasion knowledge of the participants was manipulated to investigate its moderating effect. Findings The findings confirm that consumers are more likely to write online reviews when a message comes from a brand that has stronger brand equity, trust in the message mediates the relationship between brand equity and consumer intention to write an online review, and persuasion knowledge has a differential effect on consumer intention to write reviews. Originality/value The study adds to the brand equity and online review literature by providing evidence that a higher level of consumer trust on brands that have stronger brand equity leads to an increased intention to write a review for the brand. It also shows that consumers’ awareness of the motive of the brand is more beneficial for brands with strong brand equity, contributing to persuasion knowledge literature.

Title redacted: the impact of negative online review censorship

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jennifer L. Stevens ◽  
Carol L. Esmark Jones ◽  
Mike Breazeale
Keyword(s):  
Product Information ◽  
Experimental Studies ◽  
Online Reviews ◽  
Online Review ◽  
Content Type ◽  
Brand Relationship ◽  
Exchange Product ◽  
Negative Impacts ◽  
The Impact ◽  
Harmful Effects

Purpose Consumers are increasingly using review sites to exchange product information, whereas companies attempt to maintain control of brand-related communications. One method marketers may take to retain control is to remove negative opinions about the brand. This paper aims to examine the impact on consumer’s brand perceptions when negative reviews are censored. Design/methodology/approach Two experimental studies were conducted to assess whether censorship of a negative online review, in the form of removal by the company, weakens brand relationship quality (BRQ) dimensions. Findings The results show that censoring negative online reviews has a damaging effect on BRQ. Additionally, the findings indicate that a brand may not be able to increase BRQ when a negative review has been posted, however strategic measures can be taken to diminish the potentially harmful impact. Originality/value As many brands still do not adequately understand how to handle negative online reviews, this research offers valuable implications in furthering the examination of negative electronic word-of-mouth and ways to diminish its harmful effects. Additionally, while substantial research focuses on the positive consequences of brand relationships, this research answers calls to examine the negative impacts to BRQ.

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