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.

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.

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 Mining Express Service Innovation Opportunity from Online Reviews

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Service Innovation ◽  
Online Reviews ◽  
User Generated Content ◽  
Customer Demand ◽  
Market Competitiveness ◽  
Service Attribute ◽  
Express Delivery

In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, we propose a framework for mining service innovation opportunities. This framework uses text mining to analyze user generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, we crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, we carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.

The impact of COVID-19 on hotel customer satisfaction: evidence from Beijing and Shanghai in China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shaolong Sun ◽  
Fuxin Jiang ◽  
Gengzhong Feng ◽  
Shouyang Wang ◽  
Chengyuan Zhang
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Consumer Satisfaction ◽  
Topic Model ◽  
Hotel Industry ◽  
Online Reviews ◽  
Control Measures ◽  
Content Type ◽  
Before And After ◽  
The Impact

Purpose The purpose of this study is to provide better service to hotel customers during the COVID-19 era. Specifically, this study focuses on understanding the changes in hotel customer satisfaction during the epidemic and formulating effective marketing strategies to satisfy and attract guests. Design/methodology/approach As the first victim of the COVID-19 virus, China’s hotel industry has been profoundly affected and customer satisfaction and needs have also changed. Taking 105,635 hotel reviews obtained from Tripadvisor.com in Beijing and Shanghai as samples, this study explores the changes in consumer satisfaction by using text-mining methods. Findings The results suggest that there are significant differences in overall ratings, spatial distribution and ratings of different traveller types before and after the epidemic. Generally, customers have higher “tolerance” and are more inclined to give higher ratings and pay more attention to hotel prevention and control measures to reduce health risks after the COVID-19. Research limitations/implications This paper proves the changes in customer satisfaction before and after the COVID-19 at the theoretical level and reveals the changes in customer attention through the topic model and provides a basis for guiding hotel managers to reduce the impact of the COVID-19 crisis. Practical implications Empirical findings would provide useful insights into tourism management and improve hotel service quality during the COVID-19 epidemic era. Originality/value This research explores the hotel customer satisfaction in the field of hotel management before COVID-19 and after COVID-19, by using text mining to analyse mandarin online reviews. The results of this study will suggest that the hotel industry should continuously adjust its products and services based on the effective information obtained from customer reviews, so as to realize the activation and revitalization of the hotel industry in the epidemic era.

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 Mining Express Service Innovation Opportunity From Online Reviews

2021 ◽  
Vol 33 (6) ◽  
pp. 1-15
Author(s):  
Ning Zhang ◽  
Rui Zhang ◽  
Zhiliang Pang ◽  
Xue Liu ◽  
Wenfei Zhao
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Service Innovation ◽  
Online Reviews ◽  
User Generated Content ◽  
Customer Demand ◽  
Market Competitiveness ◽  
Service Attribute ◽  
Express Delivery

In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, we propose a framework for mining service innovation opportunities. This framework uses text mining to analyze user generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, we crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, we carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.

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.

Internet banking service quality, e-customer satisfaction and loyalty: the modified e-SERVQUAL model

2020 ◽  
Vol 32 (6) ◽  
pp. 1443-1466 ◽  
Author(s):  
Syed Ali Raza ◽  
Amna Umer ◽  
Muhammad Asif Qureshi ◽  
Abdul Samad Dahri
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Customer Loyalty ◽  
Banking Sector ◽  
Internet Banking ◽  
Partial Least Square ◽  
Equation Modeling ◽  
Content Type ◽  
Banking Service ◽  
Customer's Loyalty

PurposeThis study explores the service quality dimensions in Internet banking and their impact on e-customer’s satisfaction and e-customer’s loyalty. This study tries to inspect the structural association between Internet banking service quality, electronic customer satisfaction and electronic customer loyalty based on separate constructs.Design/methodology/approachIn this present research, quantitative approach is applied. The data is gathered from 500 bank clients in Pakistan by using structured questionnaires, and the theoretical model is tested by partial least square structured equation modeling (PLS-SEM). Moreover, convergent validity and discriminant validity were assessed.FindingsResults show that all the dimensions are found to have a positive and significant influence on customer satisfaction while customer’s satisfaction has a significant and positive impact on customer’s loyalty. Findings indicate that service quality plays a very important role in every society, as it has become the basis for how customers interpret online banking and, in the end, how it interacts and operates with online services.Practical implicationsThis research adds up considerably to the literature of bank marketing, and it is also fruitful for the academicians since it demonstrates the way Internet banking service quality determinants predict e-satisfaction of clients which ultimately raises the e-loyalty of clients. This study is useful for those E-retailers and managers who want to grab e-retailing market.Originality/valueThis research suggests a model which ultimately enhances customer loyalty towards Internet banking service quality through customer satisfaction in Pakistan. It involves modified model of E-SERVQUAL (user friendliness, efficiency of websites, personal need, and site organization) which connects it to electronic customer satisfaction and electronic customer loyalty. Therefore, it will assist the Internet banking sector in building effective marketing tactics, establishing long lasting relationships with clients and acquiring the competitive edge in the market.

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.

scholarly journals MODIFIED SERVQUAL MODEL AND EFFECTS OF CUSTOMER ATTITUDE AND TECHNOLOGY ON CUSTOMER SATISFACTION IN BANKING INDUSTRY: MEDIATION, MODERATION AND CONDITIONAL PROCESS ANALYSIS

2017 ◽  
Vol 18 (5) ◽  
pp. 974-1004 ◽  
Author(s):  
Rizwan Raheem AHMED ◽  
Jolita VVEINHARDT ◽  
Dalia ŠTREIMIKIENĖ ◽  
Muhammad ASHRAF ◽  
Zahid Ali CHANNAR
Keyword(s):  
Factor Analysis ◽  
Customer Satisfaction ◽  
Financial Services ◽  
Banking Sector ◽  
Banking Industry ◽  
Process Analysis ◽  
Perceived Value ◽  
Online Banking ◽  
Foreign Banks ◽  
Banking Service

Banks are very important financial services sector, and in banking sector there is an intense competition amongst the local and foreign banks throughout the world. The objective of this research is to analyse the effects of perceived value and customer trust, and role of technology in banking service qualities and customers’ satisfaction in Pakistani context. For this purpose we employed modified SERVQUAL model with four dimensions such as empathy, competence, reliability, and online service. An adapted questionnaire was used to carry out this survey research, and collected 830 responses from the customers of Pakistani banking industry. We used factor analysis, confirmatory factor analysis, and bootstrapping methods to carry out this research. The results of the study demonstrated that our four-dimensional model of modified SERVQUAL has a significant impact on overall customer satisfaction. It is further concluded from the bootstrapping method that modified SERVQUAL dimensions and customer satisfaction are positively mediated by the perceived value and trust. Finally, it is also concluded that the implementation of technology serves as moderating variable in the banking sector. The outcomes of this research are beneficial to the senior management of banking sector in order to implement the effective and customised online banking structure to gain competitive advantages, and provide vibrant online banking services that enhance the standard and ease of services to the customers and earn their confidence. The originality and novelty of this research provide a significant contribution in the application of SERVQUAL model specifically for the banking service quality dimensions and customer satisfaction in marketing research.

Sign in / Sign up

Export Citation Format

Share Document