Exploration of a Multi-dimensional Evaluation of Books Based on Online Reviews: A Text Mining Approach

2012 ◽  
pp. 119-129
Author(s):  
Tianxi Dong ◽  
Matti Hamalainen ◽  
Zhangxi Lin ◽  
Binjie Luo
Keyword(s):  
Text Mining ◽  
Online Reviews

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.

Negative Customer Experience in Lifestyle Hotels

2021 ◽  
pp. 126-157
Author(s):  
Manoel Vitor Santos ◽  
Amélia M. P. C. Brandão
Keyword(s):  
Social Networks ◽  
Text Mining ◽  
Consumer Behaviour ◽  
Online Reviews ◽  
Customer Experience ◽  
Customer Complaints ◽  
Online Platforms ◽  
Hotel Service ◽  
Hotel Managers

The primary purpose of the present research is to develop a methodology which can accurately analyse online public reviews on Google using Netnography studies combined with text mining analyses. By analysing the current techniques applied to a lifestyle hotel brand in nine properties in different countries and carefully studying how negative reviews are expressed online by costumers, this study aims to create a pattern of lifestyle customer complaints. This research seeks to demonstrate patterns of consumer behaviour that are not fully satisfied with the hotel service and how it can negatively affect the brand. This study identifies the areas that five stars lifestyle hoteliers and hotel managers need to pay attention to improve services, considering online reviews on online platforms, such as social networks and other tourism sites. Today, online reviews and customer experiences have a significant impact on the choice of a hotel.

Analysis of Online Hotel Reviews During the COVID-19 Pandemic Using Topic Modeling

2021 ◽  
pp. 478-494
Author(s):  
Özlem Ergüt
Keyword(s):  
Text Mining ◽  
Service Quality ◽  
Topic Modeling ◽  
Online Reviews ◽  
Current Situation ◽  
Tourism Industry ◽  
Daily Lives ◽  
New Normal ◽  
Tourism Sector ◽  
The World

The world is facing the COVID-19 pandemic that has impacted economies and millions of people worldwide. The fact that COVID-19 is highly contagious from person to person has greatly affected the daily lives of people, and it has also had a devastating effect on many sectors, particularly the tourism industry. In order to mitigate losses for the tourism sector and for it to gain a new dynamism under the current pandemic conditions, monitoring and analyzing online reviews is an important factor for better understanding the needs and desires of customers. The purpose of this study was to determine the main topics in online reviews by foreign guests staying in İstanbul during the pandemic period using text mining techniques. The information obtained as a result of the analysis is important in terms of understanding how to manage the current situation, developing suggestions for solutions, improving service quality, making future decisions, and adapting to the new normal.

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.

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.

Digital Narratives of Place: Learning About Neighborhood Sense of Place and Travel Through Online Responses

2017 ◽  
Vol 2666 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Ashok Sekar ◽  
Roger B. Chen ◽  
Adrian Cruzat ◽  
Meiyappan Nagappan
Keyword(s):  
Text Mining ◽  
Information And Communication Technologies ◽  
Sense Of Place ◽  
Travel Demand ◽  
Contextual Information ◽  
Online Reviews ◽  
Practical Implementation ◽  
Information And Communication ◽  
Digital Narratives ◽  
Travel Analysis

As the market penetration of mobile information and communication technologies continues to grow, visitor feedback, such as online reviews of locations or sites visited, will continue to grow in parallel at finer temporal and geographic scales. This growth in data opens the opportunity for travel demand analysts to assess location attractiveness on the basis of online reviews and subsequently inform destination choice models. In geography and urban planning, the construct of sense of place (SOP) has emerged as an indicator for visitor association or connection with a place or site. An opportunity exists for examining SOP through the lens of text mining (i.e., extracting information from online text reviews and forming digital narratives of place). Several websites devoted to sharing feedback on experiences and overall perceptions exist, including Yelp and TripAdvisor. With text-mining methods, previously unidentified SOP-related topics and issues may emerge from online reviews and serve as a basis for subsequent analysis. The results from this study indicate that these emerging topics or terms require more contextual information and interpretation. As a stand-alone method, text mining is insufficient for identifying SOP topics, given the complexity of dimensions that characterize SOP. In addition, the results suggest that timing and seasonality play an important role in visitors’ evaluation of a site; these factors have received less attention in the literature. With respect to text mining as a methodology to gain insights into SOP and supplement existing travel analysis, several barriers exist, including interpretation of topics from topic models. Nonetheless, these approaches are promising and require more research to guide practical implementation for inferring SOP from online text reviews and integration with existing travel analysis approaches.

Detection of Fake Online Reviews Using Semi-Supervised and Supervised Learning

2020 ◽  
Vol 17 (8) ◽  
pp. 3577-3580
Author(s):  
M. S. Roobini ◽  
B. Nikhil Chowdary ◽  
J. Madhav Chowdary ◽  
J. Aruna ◽  
Anitha Ponraj
Keyword(s):  
Text Mining ◽  
Supervised Learning ◽  
Online Shopping ◽  
Online Reviews ◽  
Business Organizations ◽  
Main Aspect ◽  
Web Based ◽  
Control Item

Online reviews have an incredible effect on the present business and trade. The development of web-based business organizations has pulled in numerous buyers since they provide a scope of items on aggressive costs. The main aspect most buyers depends on while doing online shopping is the review of items for closing the choice of object. Basic leadership for the acquisition of online items generally relies upon reviews given by the clients. Henceforth, deft people or gatherings attempt to control item surveys for their advantages. In perspective on the impacts of these phony surveys, various systems to recognize these were proposed in the research. Because of reviews and its nature, this is hard to group these utilizing only one classifier. Henceforth, the present research discusses a classifier for dealing with identifying such phony reviews. The study also presents the text mining techniques both supervised and semi-supervised to identify counterfeit online reviews just as looks at the effectiveness of the two strategies on the datasets with hotel surveys.

scholarly journals Utilizing Text Mining and Feature-Sentiment-Pairs to Support Data-Driven Design Automation Massive Open Online Course

2021 ◽  
Vol 16 (01) ◽  
pp. 134
Author(s):  
Nasa Zata Dina ◽  
Riky Tri Yunardi ◽  
Aji Akbar Firdaus
Keyword(s):  
Text Mining ◽  
Design Automation ◽  
Online Reviews ◽  
User Preferences ◽  
User Preference ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Related Design

This study aimed to develop a case-based design framework to analyze online us-er reviews and understanding the user preferences in a Massive Open Online Course (MOOC) content-related design. Another purpose was to identify the fu-ture trends of MOOC content-related design. Thus, it was an effort to achieve da-ta-driven design automation. This research extracts pairs of keywords which are later called Feature-Sentiment-Pairs (FSPs) using text mining to identify user preferences. Then the user preferences were used as features of an MOOC content-related design. An MOOC case study is used to implement the proposed framework. The online reviews are collected from www.coursera.org as the MOOC case study. The framework aims to use these large scale online review data as qualitative data and converts them into quantitative meaningful infor-mation, especially on content-related design so that the MOOC designer can de-cide better content based on the data. The framework combines the online re-views, text mining, and data analytics to reveal new information about users’ preference of MOOC content-related design. This study has applied text mining and specifically utilizes FSPs to identify user preferences in the MOOC content-related design. This framework can avoid the unwanted features on the MOOC content-related design and also speed up the identification of user preference.

scholarly journals Automated Evaluation of Students’ Feedbacks using Text Mining Methods

2019 ◽  
Vol 8 (4) ◽  
pp. 337-342
Keyword(s):  
Text Mining ◽  
Online Reviews ◽  
Structured Data ◽  
Unstructured Data ◽  
Future Perspectives ◽  
Web Based ◽  
Literary Structure ◽  
Automated Evaluation ◽  
Teaching Learning ◽  
Textual Form

In this era of competition there is a culture of online reviews or feedbacks. These feedbacks may be about any product or service. However, major issues are their unstructured textual form and big number. It means every user gives feedback in own style. Study and analyzing of such unorganized big number of feedbacks that are growing every year becomes herculean task. This paper describes about mining of structured data (table) and unstructured data (text) both. An application from academic environment for structured and unstructured form of data is considered and discussed to enhance understanding and easiness of researcher. Stanford Parser plays a very useful role to understand the semantic of a sentence. It gives a base that how to separate data from the wellspring of information accessible in the literary structure like web based life, tweets, news, books and so on. It is also helpful to judge a teaching learning process in terms of teacher’s performance and subject’s weakness if any. This paper has five sections first about introduction, second about literature of text mining and its techniques, third about proposed work and result, fourth about future perspectives and finally fifth as a conclusion.

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