Leisure Motivation and Satisfaction: A Text Mining of Yoga Centres, Yoga Consumers, and Their Interactions

Jia, Susan

doi:10.3390/su10124458

Cited by 39 publications

(79 citation statements)

References 71 publications

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“…. , , refer to a set of rows or vectors, which are composed of the words that contain the different topics, and ∅ … ∅ refer to the set of rows that define each topic [4,7,8].…”

Section: Latent Dirichlet Allocation (Lda)mentioning

confidence: 99%

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A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

2019

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The global development of the Internet, which has enabled the analysis of large amounts of data and the services linked to their use, has led companies to modify their business strategies in search of new ways to increase marketing productivity and profitability. Many strategies are based on business intelligence (BI) and marketing intelligence (MI) that make it possible to extract profitable knowledge and insights from large amounts of data generated by company customers in digital environments. In this context, the present study proposes a three-step research methodology based on data text mining (DTM). In further research, this methodology can be used for business intelligence analysis (BIA) strategies to analyze user generated content (UGC) in social networks and on digital platforms. The proposed methodology unfolds in the following three stages. First, a Latent Dirichlet Allocation (LDA) model that determines the database topic is used. Second, a sentiment analysis (SA) is proposed. This SA is applied to the LDA results to divide the topics identified in the sample into three sentiments. Thirdly, textual analysis (TA) with data text mining techniques is applied on the topics in each sentiment. The proposed methodology offers important advances in data text mining in terms of accuracy, reliability and insight generation for both researchers and practitioners seeking to improve the BIA processes in business and other sectors.

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“…. , , refer to a set of rows or vectors, which are composed of the words that contain the different topics, and ∅ … ∅ refer to the set of rows that define each topic [4,7,8].…”

Section: Latent Dirichlet Allocation (Lda)mentioning

confidence: 99%

“…Subsequently, the main topics discovered in the dataset are classified. [8]. Equation 1describes how this is done and shows the importance of the hidden and observed variables in the joint distribution [8].…”

Section: Topic Identification Using Ldamentioning

confidence: 99%

A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

2019

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“…The first step is the development of a mathematical model to detect topics from a series of inputs on which this algorithm is applied. Pritchard and Stephens [44] created the model, and Blei et al improved it [45]. The improved model is known as LDA.…”

Section: Knowledge-based Methods To Extract Insights From Ugcmentioning

confidence: 99%

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

2019

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New analysis and visualization techniques are required to glean useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of big data may also be the age of knowledge, visualization, and understanding. Education is the keystone area used in this study because it is deeply affected by digital platforms as an educational medium and also because it deals mostly with digital natives who use information and communication technology (ICT) for all manner of purposes. Students and teachers are therefore a rich source of user generated content (UGC) on social networks and digital platforms. This article shows how useful knowledge can be extracted and visualized from samples of readily available UGC, in this case the text published in tweets from the social network Twitter. The first stage employs topic-modeling using LDA (latent dirichlet allocation) to identify topics, which are then subjected to sentiment analysis (SA) using machine-learning (developed in Python). The results take on meaning through an application of data mining techniques and a data visualization algorithm for complex networks. The results obtained show insights related to innovative educational trends that practitioners can use to improve strategies and interventions in the education sector in a short-term future.

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“…Additionally, understanding online reviews is also beneficial for business owners and marketers to gain clearer insight into customers' attitudes and behavior, which can be used by practitioners to improve their service, and create a sustainable competitive advantage. Prior studies have analyzed online reviews to understand customer experience and satisfaction across different service contexts including hotel industry, short-term rental industry, airline industry, and wellness industry [27][28][29]. In the restaurant industry, Pantelids [30] empirically examined meal experience using 2471 online restaurant comments, revealing six salient factors in a diner's evaluation of a restaurant: food, service, ambience, price, menu, and décor.…”

Section: The Role Of Ewom In Promoting Business Sustainabilitymentioning

confidence: 99%

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Luo

2019

Sustainability

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Helpful online reviews could be utilized to create sustainable marketing strategies in the restaurant industry, which contributes to national sustainable economic development. This study, the main aspects (including food/taste, experience, location, and value) from 294,034 reviews on Yelp.com were extracted empirically using the Latent Dirichlet Allocation (LDA) and positive and negative sentiment were assigned to each extracted aspect. Positive sentiments were associated with food/taste, while negative sentiments were associated with value. This study further proves a robust classification algorithm based on Support Vector Machine (SVM) with a Fuzzy Domain Ontology (FDO) algorithm outperforms other traditional classification algorithms such as Naïve Bayes (MB) and SVM ontology in predicting the helpfulness of online reviews. This study enriches the literature on managerial aspects of sustainability by analyzing a large amount of plain text data that customers generated. The results of this study could be used as sustainable marketing strategy for review website developers to design sophisticated, intelligence review systems by enabling customers to sort and filter helpful reviews based on their preferences. The extracted aspects and their assigned sentiment could also help restaurateurs better understand how to meet diverse customers’ needs and maintain sustainable competitive advantages.

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Leisure Motivation and Satisfaction: A Text Mining of Yoga Centres, Yoga Consumers, and Their Interactions

Cited by 39 publications

References 71 publications

A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

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