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

Analysis of COVID-19-Related User Content on the Baseball Bulletin Board in 2020 through Text Mining

The world engaged in online sport watching during COVID-19. Fortunately, in Taiwan, the pandemic was stably controlled in 2020, allowing for the continuation of the Chinese Professional Baseball League (CPBL); this attracted international attention and encouraged relevant discussions on social media in Taiwan. In the present study, through text mining, we analyzed user content (e.g., the concepts of sports service quality and social identity) on the Professional Technology Temple (PTT) baseball board-the largest online bulletin board system in Taiwan. A predictive model was constructed to assess PTT users' COVID-19-related comments in 2020. A total of 422 articles and 21,167 comments were retrieved. PTT users interacted more frequently during the closed-door period, particularly during the beginning of the CPBL in April. Effective pandemic prevention, which garnered global attention to the league, generated a sense of national identity among the users, which was strengthened with the development of peripheral products, such as English broadcasting and live broadcasting on Twitch. We used machine learning to develop a chatbot for predicting the attributes of users' comments; this chatbot may improve CPBL teams' understanding of public opinion trends. Our findings may help stakeholders develop tailored programs for online spectators of sports during pandemic situations.

Associations between Leisure Preferences, Mindfulness, Psychological Capital, and Life Satisfaction

This study intended to explore which leisure preferences contribute to mindfulness, psychological capital, and life satisfaction and assess whether mindfulness, psychological capital, and life satisfaction are associated with different leisure preferences. This study applied the Satisfaction with Life Scale (SWLS), the Psychological Capital Questionnaire (PCQ-12), the Mindful Attention Awareness Scale (MAAS), and the instrument to evaluate the prevalence of leisure preferences. A sample consisted of 586 participants, 104 males and 478 females. The mean age of participants was 42.06, SD = 13.29. The results show that respondents who did not spend free time watching television scored higher on life satisfaction, mindfulness, and psychological capital. Participants who preferred attending events scored higher on life satisfaction and psychological capital. Participants who preferred spending time with family as a leisure preference scored significantly higher on life satisfaction, mindfulness, and psychological capital, including PsyCap overall, PsyCap work, PsyCap relationship, and PsyCap health. The findings also reveal that time spent with family is significantly associated with life satisfaction. Besides, males’ life satisfaction was significantly associated with time spent in nature, while females’ satisfaction was associated with spending time with family and participating in events. Males’ mindfulness was significantly associated with book reading, and females’ mindfulness was associated with not watching television. Males’ psychological capital was significantly associated with spending time with family and book reading, and females’ psychological capital was associated with not watching television but spending time with family, participating in events, and spending time in nature. The findings also showed that mindfulness mediated the link between watching television and life satisfaction, and psychological capital mediated links between spending time with family, participating in events, and life satisfaction. The findings demonstrate that life satisfaction is also significantly associated with spending time with family as a leisure preference. This study also revealed a significant negative association between age and spending time with friends or family, evidencing the possible loneliness of elderly respondents. Due to limitations of this study, including sample size and characteristics, cultural context, and research design, the research findings would preferably be regarded thoughtfully.

Exploration of Topic Classification in the Tourism Field with Text Mining Technology—A Case Study of the Academic Journal Papers

This study collects abstracts of SSCI tourism journal papers between 2010 and 2019 from the WoS (Web of Science) database and uses a novel method of topic classification to explore the vocabulary characteristics of the classified articles. The corpora of abstracts are given quantitative Term Frequency–Inverse Document Frequency (TF–IDF) weights. A hierarchical K-means cluster analysis is then performed to automatically classify the articles; co-word analysis techniques are used to show the characteristics of feature words for distinct clusters, titles, and the consistency of the classified articles. Based on the results for 5783 abstracts, cluster analysis classifies the number of K-means clusters into six categories: travel, culture, sustainability, model, behavior, and hotel. A cross-check method is applied to assess the consistency of the topic classifications, list titles and keywords of the documents with the three smallest distances in each category and apply a strategic diagram to present the features of the distinct categories.

Topic modelling and opinion mining of user generated content on the internet using machine learning: An analysis of postpartum care centres in Shanghai

In order to reach a compromise between adhering to the traditional culture and embracing the modern lifestyle, more and more Asian moms are heading towards postpartum care centres for postpartum recovery. However, research regarding the quality of care of these postpartum care centres is nearly missing from the literature. This paper investigated the status quo of the postpartum care centres in Shanghai, China from mothers’ perspectives by means of analysing the 34280 pairs of ratings and reviews posted by postpartum care centre customers on the internet with machine learning and text mining. Results show that the mothers are generally satisfied with the studied care centres. Meanwhile, the 13 major topics in the customer online reviews were identified, which provide an overview of the interaction between a mother and a care centre. In addition, weight of topic analysis suggests that the studied care centres can further improve in the areas of support team, environment, and facility.

Determination of Motivating Factors of Urban Forest Visitors through Latent Dirichlet Allocation Topic Modeling

Despite the unique characteristics of urban forests, the motivating factors of urban forest visitors have not been clearly differentiated from other types of the forest resource. This study aims to identify the motivating factors of urban forest visitors, using latent Dirichlet allocation (LDA) topic modeling based on social big data. A total of 57,449 cases of social text data from social blogs containing the keyword "urban forest" were collected from Naver and Daum, the major search engines in South Korea. Then, 17,229 cases were excluded using morpheme analysis and stop word elimination; 40,110 cases were analyzed to identify the motivating factors of urban forest visitors through LDA topic modeling. Seven motivating factors-"Cafe-related Walk", "Healing Trip", "Daily Leisure", "Family Trip", "Wonderful View", "Clean Space", and "Exhibition and Photography"-were extracted; each contained five keywords. This study elucidates the role of forests as a place for healing, leisure, and daily exercise. The results suggest that efforts should be made toward developing various programs regarding the basic functionality of urban forests as a natural resource and a unique place to support a diversity of leisure and cultural activities.

Validation of the Physical Activity and Leisure Motivation Scale in Adolescent School Children in Spain (PALMS-e)

The aim of this study was to translate and adapt the physical activity and leisure motivation scale (PALMS) into Spanish, and to analyse its validity and reliability. The sample comprised 867 adolescents, with a mean age of 14.04 ± 1.19 years, 53.9% of whom were male. During the translation process, some of the items in the instrument were modified slightly, improving its comprehensibility. On the other hand, the exploratory factor analysis did not present an adequate factor structure, so a more in-depth analysis was carried out, using item response theory and confirmatory factor analysis; the conclusion was that it would be appropriate to eliminate several items from the scale. From this, a final shortened version, consisting of 25 items, was produced, with adequate fit indices—CFI = 0.933, TLI = 0.918, SRMR = 0.042, RMSEA = 0.052 (90% CI 0.048; 0.056)—and good reliability for each of the dimensions, ranging from 0.625 to 0.835. It can be concluded that the abbreviated version of the PALMS instrument, adapted for Spanish adolescents (PALMS-e), is a valid and reliable instrument for assessing their motives for doing physical activity.

Analyzing Restaurant Customers’ Evolution of Dining Patterns and Satisfaction during COVID-19 for Sustainable Business Insights

Observing and interpreting restaurant customers’ evolution of dining patterns and satisfaction during COVID-19 is of critical importance in terms of developing sustainable business insights. This study describes and analyzes customers’ dining behavior before and after the pandemic outbreak by means of statistically aggregating and empirically correlating 651,703 restaurant-user-generated contents posted by diners during 2019–2020. Twenty review topics, mostly food, were identified by latent Dirichlet allocation, whereas analysis of variation and rating-review regression were performed to explore whether and why customers became less satisfied. Results suggest that customers have been paying fewer visits to restaurants since the outbreak, assigning lower ratings, and showing limited evidence of spending more. Interestingly, queuing, the most annoying factor for restaurant customers during normal periods, turns out to receive much less complaint during COVID-19. This study contributes by discovering business knowledge in the context of COVID-19 based on big data that features accessibility, relevance, volume, and information richness, which is transferable to future studies and can benefit additional population and business. Meanwhile, this study also provides practical suggestions to managers regarding the framework of self-evaluation, business mode, and operational optimization.

Important Factors Affecting User Experience Design and Satisfaction of a Mobile Health App-A Case Study of Daily Yoga App

In recent years, mobile health (mHealth) has gained popularity. Yoga apps help users to exercise at home and improve their health. It is worth discussing how to give yogis a better experience and higher satisfaction to improve their willingness to keep using yoga apps. In this study, the Daily Yoga app was selected as the research object to explore important factors related to its user experience design and user satisfaction. Through a literature review and Delphi method composed of eight experts, this study put forward the important criteria framework of user experience design for the Daily Yoga app and then, used the DEMATEL (Decision Making and Trial Evaluation Laboratory)-based ANP (Analytic Network Process) method to determine the factors’ importance order and the causal relationships among them. Finally, combined with the results of an importance–performance analysis of 16 real users, we discuss the improvement measures. The research results show that the yoga class is the most critical factor in the user experience design of the Daily Yoga app, the target plan is a factor that is in great need of improvement, and having an attractive interface can improve user experience. The evaluation model of the study can act as a reference for improving user experience with the Daily Yoga app, and can also be widely used in the process of user experience design, questionnaire production, and evaluation optimization of mHealth app and related applications.

Gaining a deeper understanding of nutrition using social networks and user-generated content

Using user-generated content (UGC) on Twitter, the present study identifies the main themes that revolve around the concept of healthy diet and determine user feelings about various foods. Using a dataset of tweets with the hashtag "#Diet" or "#FoodDiet" (n = 10.591), we first use a Latent Dirichlet Allocation (LDA) model to identify the food categories most discussed on Twitter. Then, based on the results of the LDA model, we apply sentiment analysis to divide the identified tweets into three groups (negative, positive and neutral) based on the feelings expressed in corresponding tweets. Finally, the text mining approach is performed to identify foods according to the feelings expressed about those in corresponding tweets, as well as to derive key indicators that collectively present the UGC-based knowledge of healthy eating. The results of the present study show that among the foods most negatively perceived in the UGC are bacon, sugar, processed foods, red meat, and snacks. By contrast, water, apples, salads, broccoli and spinach are evaluated more positively. Furthermore, our findings suggest that the collective UGC knowledge is lacking on such healthy foods as fish, poultry, dry beans, nuts, as well as yogurt and cheese. The results of the present study can help the World Health Organization (WHO), as well as other institutions concerned with the study of healthy eating, to improve their communication policies on healthy products and preparation of balanced diets.

Marketing challenges in the #MeToo era: gaining business insights using an exploratory sentiment analysis

The #MeToo movement is among the most impressive social movements of recent years that have attracted stakeholders' attention and changed social mindsets. The present study seeks to provide a deeper understanding of the challenges involved in the #MeToo movement by identifying the main issues regarding business and marketing activities. To this end, the analysis of user-generated content (UGC) on Twitter was performed to extract the tweets with the hashtag "#MeToo" (31,305 tweets). Then, a Latent Dirichlet Allocation (LDA) model was applied to this database to identify topics. In the next step, using a Supervised Vector Machine (SVM) type analysis, we classified the tweets according to the sentiment they express (positive, negative, and neutral). Finally, we performed data text mining using the NVivo software. Our findings underscore the importance of (i) gender equality in communication campaigns, (ii) gender equality at work and (iii) social mobilizations in social networks, as well as suggest that (iv) marketing advertisers should become more inclusive and respectful in their advertising and marketing campaigns. The identified topics may be a starting point for future research on social movements, sociology, sexuality, or machismo in work environment, business and marketing strategies.

A Text Analytics-Based Importance Performance Analysis and Its Application to Airline Service

We introduce a new importance-performance analysis (IPA) methodology while making use of direct service experience perceptions represented by online reviews with numerical ratings. The proposed IPA, which we call the text analytics-based IPA (TAIPA), allows the real-time calculation of importance using the probability distribution of word frequency via the latent Dirichlet allocation (LDA) application to online reviews, and of performance using numerical rating values. The importance is also adjusted with the help of a sentiment analysis of online reviews to provide more precise measurements for service experience perceptions. To ensure an evaluation of the entire service process, we employ service encounters, in which service experiences occur and thus most customer perceptions are created, as a set of attributes composed of LDA topics that contain direct perceptions of service experiences. We investigate statistical correlations between TAIPA calculations and typical benchmarks of firm performance in the air-transport industry to verify how effective the proposed TAIPA is with respect to the degree that customer satisfaction is represented. As a primary result, TAIPA is more effective than comparison targets in that it shows stronger correlations with firm performance. TAIPA is specialized in determining which service step (i.e., a one-to-one relationship with a service encounter) needs to be improved. Moreover, TAIPA is flexible in considering multiple competitors.

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

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.

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

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.

Exotic or Home? Tourists’ Perception of Guest Houses, Guest Houses Loyalty, and Destination Loyalty in Remote Tourist Destinations

Guest houses offer an environmentally sustainable way to travel. The guest house serves not only to accommodate but also attract tourists to experience local culture when they visit remote destinations. This study was designed to explore how tourists’ multiple perceptions of guest houses in remote destinations affect their behavioral intention toward guest houses and destinations. Results demonstrated that both tourists’ perception of exotic local culture and sense of home had a significant positive effect on tourists’ loyalty to guest houses in remote destinations. In addition, tourists with high cultural distance staying in guest houses perceived a higher level of exotic local culture but lower level of sense of home compared with those with lower cultural distance. Managerial implications, limitations, and recommendations for future studies are also provided

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

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.

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.