Using Big Data and Text Analytics to Understand How Customer Experiences Posted on Yelp.com Impact the Hospitality Industry

Ting, Pei-Ju Lucy; Chen, Szu-Ling; Chen, Hsiang; Fang, Wenchang

doi:10.7903/cmr.17730

Cited by 17 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And that may be tied to poor use of big data. Important research in this area relates to: big data analytics challenges (Lee, 2017; Pouyanfar et al , 2018), big data reduction methods (Rehman et al (2016), customer experiences on social media (Ting et al , 2017) and debunking myths (Gandomi and Haider, 2015; Kimble and Milolidakis, 2015).…”

Section: A Look Backmentioning

confidence: 99%

The value of online surveys: a look back and a look ahead

Evans

Mathur

2018

INTR

318

186

View full text Add to dashboard Cite

Purpose -The purpose of this paper is to present a detailed and critical look at the evolution of online survey research since Evans and Mathur's (2005) article on the value of online surveys. At that time, online survey research was in its early stages. Also covered are the present and future states of online research. Many conclusions and recommendations are presented. Design/methodology/approach -The look back focuses on online surveys, strengths and weaknesses of online surveys, the literature on several aspects of online surveys and online survey best practices. The look ahead focuses on emerging survey technologies and methodologies, and new non-survey technologies and methodologies. Conclusions and recommendations are provided. Findings -Online survey research is used more frequently and better accepted by researchers than in 2005. Yet, survey techniques are still regularly transformed by new technologies. Non-survey digital research is also more prominent than in 2005 and can better track actual behavior than surveys can. Hybrid surveys will be widespread in the future. Practical implications -The paper aims to provide insights for researchers with different levels of online survey experience. And both academics and practitioners should gain insights. Social implications -Adhering to a strong ethics code is vital to gain respondents' trust and to produce valid results. Originality/value -Conclusions and recommendations are offered in these specific areas: defining concepts, understanding the future role of surveys, developing and implementing surveys and a survey code of ethics. The literature review cites more than 200 sources.

show abstract

Section: A Look Backmentioning

confidence: 99%

The value of online surveys: a look back and a look ahead

Evans

Mathur

2018

INTR

318

186

View full text Add to dashboard Cite

show abstract

“…hotel ambience, food and beverage products, staff performance, reservations services and overall financial value) positively affect customer satisfaction. In analyzing data from Yelp.com through text mining, Ting et al (2017) compiled a list of 36 words and grouped them into 5 dimensions: the core product, amenities, hotel attributes, staff related descriptors and evaluation of experience. And their findings were similar to the literature discussed above.…”

Section: Hotel Guest Service Experience Evaluation and Satisfactionmentioning

confidence: 99%

Exploring influential factors affecting guest satisfaction

Lee

Cai²,

DeFranco

et al. 2020

JHTT

View full text Add to dashboard Cite

Purpose Electronic word of mouth in the form of user-generated content (UGC) in social media plays an important role in influencing customer decision-making and enhancing service providers’ brand images, sales and service innovations. While few research studies have explored real content generated by hotel guests in social media, business analytics techniques are still not widely seen in the literature and how such techniques can be deployed to benefit hoteliers has not been fully explored. Thus, this study aims to explore the significant factors that affect hotel guest satisfaction via UGC and business analytics and also to showcase the use of business analytics tools for both the hospitality industry and the academic world. Design/methodology/approach This study uses big data and business analytics techniques. Big data and business analytics enable hoteliers to develop effective and efficient strategies improving products and services for guest satisfaction. Therefore, this study analyzes 200,431 hotel reviews on Tripadvisor.com through business analytics to explore and assess the significant factors affecting guest satisfaction. Findings The findings show that service, room and value evaluations are the top-three factors affecting overall guests’ satisfaction. While brand type and negative emotions are negatively associated with guests’ satisfaction, all other factors considered were positively associated with guests’ satisfaction. Originality/value The current study serves as a great starting point to further explore the relationship between specific evaluation factors and guests’ overall satisfaction by analyzing user-generated online reviews through business analytics so as to assist hoteliers to resolve performance-related problems by analyzing service gaps that exist in these influential factors.

show abstract

“…There are similar applications related to text analytics method. For the data used by these similar applications are from social media sources such as Facebook [25], Twitter [26,27] and there are some resources taken from a business-theme website such as online review data from hotel booking websites [24] and online product sales. From the comparative studies made all of the data taken from online sources and requires the process of identifying the required data and data forms.…”

Section: Related Workmentioning

confidence: 99%

“…Many analysts can do analytics products to determine the details of data intent but lack of integration between data translation with the web interface. In analyzing the unstructured textual data, Batrinca et al [12] categorized the key techniques into six that are natural language processing (NLP) [13], news analytics, opinion mining [14][15][16], data scraping, sentiment analysis [9,[17][18][19][20][21][22][23] and text analytics [10,24]. In this study, initially, natural language processing is the procedure of strategy to separating significant data from regular language input and creating tendency data meaning.…”

Section: Introductionmentioning

confidence: 99%

Text analysis on health product reviews using r approach

Kadir¹,

Aliman²

2020

IJEECS

View full text Add to dashboard Cite

In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. It combines all the NLP features in the menu part of the text analytics process in steps and it is labeled to make it easier for users to view all the text summarization. This study uses health product review from Shaklee as the data set. The proposed approach shows the acceptable performance in terms of system features execution compared with the baseline model system.

show abstract

Using Big Data and Text Analytics to Understand How Customer Experiences Posted on Yelp.com Impact the Hospitality Industry

Cited by 17 publications

References 24 publications

The value of online surveys: a look back and a look ahead

The value of online surveys: a look back and a look ahead

Exploring influential factors affecting guest satisfaction

Text analysis on health product reviews using r approach

Contact Info

Product

Resources

About