Assessing technology adoption practices in Chinese theme parks: text mining and sentiment analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tingting Zhang ◽  
Bin Li ◽  
Ady Milman ◽  
Nan Hua
Keyword(s):  
Text Mining ◽  
Technology Adoption ◽  
Sentiment Analysis ◽  
Statistical Tests ◽  
Online Reviews ◽  
Theme Park ◽  
Theme Parks ◽  
Content Type ◽  
Customer Reviews ◽  
Technology Applications

Purpose This study aims to examine technology adoption practices in Chinese theme parks by leveraging text mining and sentiment analysis approaches on actual theme park customers’ online reviews. Design/methodology/approach The study text mined a total of 65,518 reviews of 490 Chinese theme parks with the aid of the Python program. Further, it computed sentiment scores of the customer reviews associated with the ratings of each categorized technology practice applied in the theme parks. Findings The study identified two major categories of technology applications in theme parks: supporting and experiential technologies. Multiple statistical tests confirmed that supporting technologies consisted of three types: intelligent services, ticketing and in-park transportation. Experiential technologies further included five aspects of technologies according to Schmitt’s strategic experiential modules (SEMs): sense, feel, act, think and relate. Originality/value The study findings contribute to the current understanding of theme park visitors’ perceptions of technology adoption practices and provide insightful implications for theme park practitioners who intend to invest in high technology solutions to deliver a better customer experience.

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.

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.

scholarly journals Improving international attractiveness of higher education institutions based on text mining and sentiment analysis

2018 ◽  
Vol 32 (3) ◽  
pp. 431-447 ◽  
Author(s):  
Carolina Leana Santos ◽  
Paulo Rita ◽  
João Guerreiro
Keyword(s):  
Higher Education ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Design Methodology ◽  
Higher Education Institutions ◽  
Online Reviews ◽  
Content Type ◽  
International Environment ◽  
Depth Analysis ◽  
Support Students

Purpose The increasing competition among higher education institutions (HEI) has led students to conduct a more in-depth analysis to choose where to study abroad. Since students are usually unable to visit each HEIs before making their decision, they are strongly influenced by what is written by former international students (IS) on the internet. HEIs also benefit from such information online. The purpose of this paper is to provide an understanding of the drivers of HEIs success online. Design/methodology/approach Due to the increasing amount of information published online, HEIs have to use automatic techniques to search for patterns instead of analysing such information manually. The present paper uses text mining (TM) and sentiment analysis (SA) to study online reviews of IS about their HEIs. The paper studied 1938 reviews from 65 different business schools with Association to Advance Collegiate Schools of Business accreditation. Findings Results show that HEIs may become more attractive online if they financially support students cost of living, provide courses in English, and promote an international environment. Research limitations/implications Despite the use of a major platform with a broad number of reviews from students around the world, other sources focussed on other types of HEIs may have been used to reinforce the findings in the current paper. Originality/value The study pioneers the use of TM and SA to highlight topics and sentiments mentioned in online reviews by students attending HEIs, clarifying how such opinions are correlated with satisfaction. Using such information, HEIs’ managers may focus their efforts on promoting international attractiveness of their institutions.

scholarly journals Insights from sentiment analysis to leverage local tourism business in restaurants

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ting Yu ◽  
Paulo Rita ◽  
Sérgio Moro ◽  
Cristina Oliveira
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Bench Mark ◽  
Quality Factors ◽  
Vast Number ◽  
Content Type ◽  
Food And Beverage

Purpose Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry. Design/methodology/approach The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value. Findings The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management. Originality/value The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.

The asymmetric effect of review valence on numerical rating

2019 ◽  
Vol 43 (2) ◽  
pp. 283-300 ◽  
Author(s):  
Hsiu-Yuan Tsao ◽  
Ming-Yi Chen ◽  
Hao-Chiang Koong Lin ◽  
Yu-Chun Ma
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Negative Impact ◽  
Online Reviews ◽  
Data Set ◽  
Brand Strength ◽  
Content Type ◽  
Numerical Rating ◽  
The Impact ◽  
Moderating Role

PurposeThe basic assumption is that there is a symmetric relationship between review valence and rating, but what if review valence and rating were linked asymmetrically? There are few studies which have investigated the situations in which positive and negative online reviews exert different influences on ratings. This study considers brand strength as having an important moderating role because the average rating of existing reviews for a particular product is a heuristic cue for decision makers. Thus, the purpose of this paper is to argue that an asymmetric relationship between review content valence and numerical rating will depend on brand strength.Design/methodology/approachThe authors have conducted a sentiment analysis via text mining, using self-developed computer programs to retrieve a data set from the TripAdvisor website.FindingsThis study finds there is an asymmetric relationship between review valence (verbal) and numerical rating. The authors further find brand strength to have an important moderating role. For a stronger brand, negative review content will have a greater impact on numerical ratings than positive review content, while for a weaker brand, positive review content will have a greater impact on numerical ratings than negative review content.Practical implicationsMarketers could adopt sentiment analysis via text mining of online reviews as a valid measure or predictor of consumer satisfaction or numerical ratings. Strong brands should direct more attention to negative reviews, because in such reviews the negative impact transcends the positive. In contrast, weak brands should aim to exploit as many positive reviews as possible to minimize the impact of any negative reviews.Originality/valueThis study finds there is an asymmetric relationship between review valence (verbal) and numerical rating and considers brand strength to play an important moderating role. The authors have used real data from the TripAdvisor website, which allow people to express themselves in an unsolicited manner, and linked these with the results from the sentiment analysis.

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.

Islamic and Muslim tourism: service quality and theme parks in the UAE

2019 ◽  
Vol 75 (2) ◽  
pp. 402-413 ◽  
Author(s):  
Lamya Abbas Darwish Abdulla Lari ◽  
Shilpa Iyanna ◽  
Fauzia Jabeen
Keyword(s):  
Service Quality ◽  
United Arab Emirates ◽  
Moderating Effect ◽  
Environment Interaction ◽  
Theme Park ◽  
Theme Parks ◽  
Content Type ◽  
Park Service ◽  
Quality Dimensions ◽  
Tourism Service

Purpose The purpose of this paper is to identify dimensions of theme park quality from an Islamic perspective and develop a framework to examine the interrelationships between theme park service quality dimensions, visitor delight and visitor loyalty in theme parks in the United Arab Emirates (UAE). Design/methodology/approach The study is based on a detailed literature review and analysis of visitors’ comments on TripAdvisor.com about their experience in theme parks in the UAE. Findings Theme park service quality can be assessed across 4 main dimensions (physical environment, interaction, consumables and access) and 13 sub-dimensions. A relationship is proposed between these dimensions and visitors’ delight, with visitor loyalty a consequence of visitor delight. Nationality is proposed to have a moderating effect on the delight–loyalty relationship. Originality/value This study attempts to propose a model for theme park service quality that reflects both traditional dimensions of service quality and dimensions related to Islamic attributes. It is one of very few attempts to investigate theme park visitors’ perception of service quality and its relation to delight and loyalty in the UAE. It is also, as far as the authors are aware, the first study to investigate the moderating effect of nationality on the delight–loyalty relationship.

Understanding retail quality of sporting goods stores: a text mining approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luke Lunhua Mao
Keyword(s):  
Text Mining ◽  
Customer Service ◽  
Online Reviews ◽  
The United States ◽  
Alternative Methods ◽  
Content Type ◽  
Sport Industry ◽  
Sporting Goods ◽  
Quality Dimensions

PurposeSporting goods retailing is a significant sector within the sport industry with the total revenue of this sector reaching $52.2 billion in 2018. Beset with formidable competition, sporting goods stores are compelled to augment their merchandise with service and improve retail quality. The purpose of this study is to investigate retail quality of sporting goods stores (RQSGS).Design/methodology/approachBased on 27,793 online reviews of 1481 stores in the United States, this study used Leximancer 4.0, a text mining software, to identify critical retail quality dimensions associated with sporting goods stores, and further explored the most salient dimensions among different levels of ratings.FindingsCustomer service and store aspects are the two higher-order dimensions of RQSGS; holistic experience, manager and staff are three themes under customer service, and product, B&M store and online–offline integration are three themes under store aspects. Furthermore, extreme reviews focus more on customer service, whereas lukewarm reviews focus more on store aspects.Practical implicationsKnowledgeable staff, managers and online–offline integration are instrumental in creating superior retail quality. Sporting goods stores should enhance hedonic and social values for consumers in order to ward off online competitions.Originality/valueThis study explored retail quality dimensions that are pertinent to sporting goods retailing utilizing text mining methods. This study to certain extent cross-validated the existing retailing literature that is developed on alternative methods.

Customized reputation generation of entities using sentiment analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arpita Gupta ◽  
Saloni Priyani ◽  
Ramadoss Balakrishnan
Keyword(s):  
Sentiment Analysis ◽  
Classification Models ◽  
Data Set ◽  
Content Type ◽  
Customer Reviews ◽  
Art Methods ◽  
Review Helpfulness ◽  
Sentiment Score ◽  
State Of Art

Purpose In this study, the authors have used the customer reviews of books and movies in natural language for the purpose of sentiment analysis and reputation generation on the reviews. Most of the existing work has performed sentiment analysis and reputation generation on the reviews by using single classification models and considered other attributes for reputation generation. Design/methodology/approach The authors have taken review, helpfulness and rating into consideration. In this paper, the authors have performed sentiment analysis for extracting the probability of the review belonging to a class, which is further used for generating the sentiment score and reputation of the review. The authors have used pre-trained BERT fine-tuned for sentiment analysis on movie and book reviews separately. Findings In this study, the authors have also combined the three models (BERT, Naïve Bayes and SVM) for more accurate sentiment classification and reputation generation, which has outperformed the best BERT model in this study. They have achieved the best accuracy of 91.2% for the movie review data set and 89.4% for the book review data set which is better than the existing state-of-art methods. They have used the transfer learning concept in deep learning where you take knowledge gained from one problem and apply it to a similar problem. Originality/value The authors have proposed a novel model based on combination of three classification models, which has outperformed the existing state-of-art methods. To the best of the authors’ knowledge, there is no existing model which combines three models for sentiment score calculation and reputation generation for the book review data set.

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