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 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.

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.

Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews

2019 ◽  
Vol 62 (2) ◽  
pp. 195-215
Author(s):  
Frederik Situmeang ◽  
Nelleke de Boer ◽  
Austin Zhang
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Research Methodology ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Product Reviews ◽  
Marketing Literature ◽  
Mining Technique ◽  
Online Product Reviews ◽  
The Relationship

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

eWOW of Guests Regarding Their Hotel Experience

2020 ◽  
pp. 295-308
Author(s):  
Zelia Breda ◽  
Rui Costa ◽  
Gorete Dinis ◽  
Amandine Angie Martins
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
User Generated Content ◽  
Sources Of Information ◽  
Value For Money ◽  
Quality Of Sleep ◽  
Online Comments ◽  
Analytical Response ◽  
Source Of Information

Online comments are increasingly mentioned as an important source of information, simplifying consumers' buying decisions. Online user-generated content has become one of the main sources of information for tourists, who themselves become creators of their own online content. This chapter focuses on sentiment analysis of comments made on TripAdvisor regarding one resort located in the Algarve region, in Portugal. The resort has good reviews, which means that the eWOM is positive. The highest scores relate to the resort's cleanliness, location and quality of sleep, and those that were less relevant were the value for money, the rooms and the service. The most dominant emotion is joy, followed by an analytical response. Negative emotions, such as sadness and anger, were not found very often in the online reviews. These results could be explained by the quality of the service, the kindness of the staff, the facilities for children, the entertainment, and the location, attributes that were often highlighted in the comments.

scholarly journals Customer Sentiments towards Fin-Tech Apps in India: A Text Mining Application

2020 ◽  
Vol 9 (1) ◽  
pp. 384-387
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Service Providers ◽  
Online Reviews ◽  
Customer Experience ◽  
Internet Connectivity ◽  
The Government ◽  
Text Mining Application

Fintech sector has witnessed incredible growth in India with the government promoting digital and cashless transactions along with the penetration of smartphones and internet connectivity in the country. Online reviews and customer opinions play a key role in the choice of Fintech apps among users. The customers compare the services of these service providers based on online reviews and ratings to finalize their choice. Fintech companies use this data to improve their customer experience. In this paper we attempt to provide useful insights into the customer sentiments towards Fintech apps in India by using a text mining approach. By analyzing customer opinions and reviews, we attempt to understand the acceptance of the services provided by the Fintech companies in India. We have used sentiment analysis to classify positive, negative and neutral reviews to understand the user sentiment towards Fintech apps. We also put forward suggestions to address the gaps in the services provided by these Fintech companies based on our analysis and findings.

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.

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 Dynamic Measurement and Evaluation of Hotel Customer Satisfaction Through Sentiment Analysis on Online Reviews

2021 ◽  
Vol 33 (6) ◽  
pp. 1-27
Author(s):  
Zhou Gang ◽  
Liao Chenglin
Keyword(s):  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Dynamic Measurement ◽  
Evaluation Framework ◽  
Online Review ◽  
Emotional Intensity ◽  
Competitor Analysis ◽  
Measurement And Evaluation

Both researchers and practitioners have attached great importance to the measurement and evaluation of hotel customer satisfaction. However, there are several problems in the dimensions, methods and conclusions. Thus, it is urgent to standardize the theories, methods, and techniques. The purpose of this study is to propose a dynamic measurement and evaluation framework for hotel customer satisfaction through sentiment analysis on online reviews. The framework consists of five steps: (1)The corpus is obtained from online review sites; (2)From the perspective of managers, the useful texts are recognized; (3)Based on the useful texts, a three-layer index system is created; (4)The center term-based short sentence sentimental orientation (CTSSSO) algorithm is developed to compute emotional intensity, then dynamically measure the customer satisfaction; (5)The dynamic important performance competitor analysis (DIPCA) is adopted for dynamic evaluation of customer satisfaction.The feasibility of our framework was demonstrated through a case study on the online reviews of two five-star hotels.

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