Emotional Text Mining: Customer profiling in brand management

Self-care in children and young people with complex chronic conditions: a qualitative study using Emotional Text Mining

Objectives

To explore: (1) self-care behaviors in children and young people (range: 6 months-24 years) with complex chronic conditions, characterized by the diagnosis of a severe chronic condition, substantial family-identified needs, functional limitations associated with technology dependence, and intensive use of healthcare services; (2) the contribution to self-care of family members and other persons involved in the child's health and daily life context (e.g., health professionals and teachers), and (3) the principal factors that might have influenced the self-care process associated with developmental age.

Methods

A qualitative descriptive study was conducted in an Italian academic tertiary pediatric hospital between September 2020 and May 2021. Overall, 25 focus groups and 7 online interviews were conducted via videoconferencing. Textual data were analyzed using Emotional Text Mining to identify three levels of communication: the factors, the main themes (clusters), and the sub-themes.

Results

A total of 104 participants were enrolled, including 27 patients with complex chronic conditions (12 males, mean age = 11.1 ± 4.40), 33 parents, 6 siblings, 33 health professionals, and 5 teachers. Participants described the process of self-care through four main factors: "self-care", "external settings", "family", and "management". Five clusters (themes) were identified: (1) Self-care management (device; consulting); (2) Shift of agency (influencing factors; parents; school); (3) Self-care support (normal life and personal development; multidisciplinary support); (4) Daily self-care maintenance/monitoring; (5) Treatment adherence. Self-care management was mostly relevant for parents of children aged between 6 months and 3 years.

Conclusion

The self-care process varies according to the needs related to the specific developmental age and the evolution of the clinical condition over time. The contribution of the family, health professionals, and social networks is fundamental for adequate self-care. To help families manage the unstable condition of their children at home, it is necessary to strengthen support networks implement home care, and ensure continuity of care.

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.

Pediatric Diabetes Technology Management: An Italian Exploratory Study on Its Representations by Psychologists and Diabetologists

The incidence of type 1 diabetes (T1D) has increased by about 3% per year over the last two decades. Continuous Insulin Subcutaneous Therapy (CSII) is widely used in the pediatric population with diabetes; however, it requires more preparation by the treating team and a careful selection of its potential users. Prescriptive provisions vary from region to region, and the perspective of health workers in this regard remains an unexplored area. The aim of this research project is to explore the representations of a group of diabetologists and psychologists working in pediatric diabetology throughout the country, regarding their roles, functions, and activities as part of a multidisciplinary team; it also aims to investigate their views on the potential benefits of CSII and the types of individuals who apply for the use of this technology. A socio-anagraphic data sheet was administered, and two homogeneous focus groups were conducted, one for each profession, which were then audio recorded. The transcripts produced were analyzed using the Emotional Text Mining (ETM) methodology. Each of the two corpora generated three clusters and two factors. For diabetologists, a focus on patient care emerged at different levels, involving collaboration with other health professionals and engagement with the community, often incorporating technology in medical interventions. Similarly, psychologists' representations highlighted interdisciplinary networking with a stronger emphasis on the psychological processes involved in managing the disease, from acceptance to the elaboration and integration of diabetes into the family narrative. Understanding the representations of health professionals working in pediatric diabetes with new technologies can contribute to the consolidation of a network of professionals through targeted work on possible critical issues that may arise.

From #iononriapro to #ioapro: analysing hashtag activism in the Italian restaurant Twittersphere during the 2020 lockdown

Purpose

In this article, the authors analyse the impact of the 2020 lockdown and the subsequent measures to contain the spread of COVID-19 in Italy in the hospitality industry by looking at the social demands brought forward by the restaurant sector.

Design/methodology/approach

To analyse social demands, the authors choose Twitter as an observation point using two hashtags as keywords to scratch the data: #iononriapro and #ioapro, which correspond to two different instances conveyed by the same subject: the restaurant sector. The instances linked to the hashtags produced different levels of engagement and penetration within the social structure and digital platform. To analyse the first block of data linked to the first hashtag-flag #iononriapro, the authors used content analysis. To analyse the second and third block of data linked to the hashtag-flag #ioapro, the authors used an automatic procedure, emotional text mining.

Findings

The analysis procedures allow us to reconstruct the positioning of the topics of closures and reopenings due to lockdown in this sector and to identify two explanatory dimensions: structural and affective, which explain the tension that has emerged between the State and the restaurant sector around COVID-related closures.

Originality/value

The study's findings not only contribute to the current understandings of the birth, transformation and penetration of social issues by the restaurant sector over the specific period linked to the COVID-19 pandemic and the measures imposed for its containment but are also valuable to analyse the dynamics through which Twitter hashtags and the social issues they represent find strength or lose interest in the public.

Only Words Count; the Rest Is Mere Chattering: A Cross-Disciplinary Approach to the Verbal Expression of Emotional Experience

The analysis of sequences of words and prosody, meter, and rhythm provided in an interview addressing the capacity to identify and describe emotions represents a powerful tool to reveal emotional processing. The ability to express and identify emotions was analyzed by means of the Toronto Structured Interview for Alexithymia (TSIA), and TSIA transcripts were analyzed by Natural Language Processing to shed light on verbal features. The brain correlates of the capacity to translate emotional experience into words were determined through cortical thickness measures. A machine learning methodology proved that individuals with deficits in identifying and describing emotions (n = 7) produced language distortions, frequently used the present tense of auxiliary verbs, and few possessive determiners, as well as scarcely connected the speech, in comparison to individuals without deficits (n = 7). Interestingly, they showed high cortical thickness at left temporal pole and low at isthmus of the right cingulate cortex. Overall, we identified the neuro-linguistic pattern of the expression of emotional experience.

Construction of Rural Tourism Brand Value Management Model from the Perspective of Big Data

Rural tourism has become an important branch of tourism management. Big data technology provides tools for rural tourism brand value management. This study aims to build a brand value management model for rural tourism from the perspective of big data. The rural tourism brand value management model under the big data perspective takes the rural tourism brand competitiveness as the starting point to analyze the relationship between brand value and brand competitiveness, so that the brand competitiveness under the perspective of rural tourism brand value has a more specific and quantifiable index system. From the two aspects of enterprise value advantage and customer value advantage, this article looks for the factors that comprehensively reflect the brand competitiveness of rural tourism. After the establishment of the index system, the BP neural network model is used to make the multiple factor evaluation more objective and feasible. Finally, it is proposed to enhance the competitiveness of rural tourism brands from the perspective of the reconstruction of competitive advantage based on enterprise value and the reconstruction of competitive advantage based on customers.

Text Mining with Network Analysis of Online Reviews and Consumers’ Satisfaction: A Case Study in Busan Wine Bars

With the growth of internet technology, customers are sharing up their experiences. Hence, these types of customer experiences are spreading rapidly as a source of online reviews. For this reason, online reviews have become a critical source of information that influences customers’ purchase intentions and behavior. Thus, businesses should monitor online reviews to understand the customer experience and increase customer satisfaction and loyalty. This study attempts to identify essential characteristics for positive online reviews of wine bars and examine the structural relationships of these attributes. To accomplish this purpose, a total of 1,337 online reviews were collected from Google Travel and analyzed. The frequency analysis was performed using text mining to determine the most frequently referred to attributes, and the semantic network analysis, factor analysis, and regression analysis were conducted to understand customer experience and satisfaction of wine bars located in Busan, South Korea. The results show that the top 50 keywords identified from the online reviews were categorized as four groups—‘Atmosphere’, ‘Service’, ‘Date and Location’, and ‘Menu’. The results of the factor analysis reduced the original dimension of 48 keywords to 16 keywords and classified them into six factors, namely, ‘Service’, ‘Staff’, ‘Menu’, ‘Environment’, ‘Recommendation’ and ‘Atmosphere’. Based on these results, implications for sustainable wine bar marketing strategies were suggested.

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective.

Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information

This study explored how the success of project crowdfunding can be predicted based on the texts of Internet social welfare crowdfunding projects. Through a calculation of the quantity of information and a mining of the sentimental value of the text, how the text information of the interconnected social welfare crowdfunding project affects the success of the project was studied. To this aim, a sentimental dictionary of Chinese Internet social welfare crowdfunding texts was constructed, and information entropy was used to calculate the quantity of information in the text. It was found that, compared with the information presented in the text, the fundraiser’s social network factors are key in improving the success of fundraising. The sentimental value of the text positively affects the success of fundraising, while the influence of the quantity of information is represented as an inverted, U-shaped relationship. The non-ideal R-squared indices reflected that the multiple linear regression models do not perform well regarding this prediction. Furthermore, this paper validated and analyzed the prediction efficiency of four machine-learning models, including a multiple regression model, a decision tree regression model, a random forest regression model, and an AdaBoost regression model, and the AdaBoost regressor showed the best efficiency, with an accuracy R2 of up to 97.7%. This study provides methods for the quantified processing of information contained in social welfare crowdfunding texts, as well as effective prediction on social welfare crowdfunding, and also seeks to raise the success rate of crowdfunding and thus features commercial and social value.

Using data mining to track the information spreading on social media about the COVID-19 outbreak

Purpose

COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.

Design/methodology/approach

The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.

Findings

Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.

Research limitations/implications

This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.

Originality/value

This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.

Analysis of Human Behavior by Mining Textual Data: Current Research Topics and Analytical Techniques

The goal of this study was to conduct a literature review of current approaches and techniques for identifying, understanding, and predicting human behaviors through mining a variety of sources of textual data with a focus on enabling classification of psychological behaviors regarding emotion, cognition, and social empathy. This review was performed using keyword searches in ISI Web of Science, Engineering Village Compendex, ProQuest Dissertations, and Google Scholar. Our findings show that, despite recent advancements in predicting human behaviors based on unstructured textual data, significant developments in data analytics systems for identification, determination of interrelationships, and prediction of human cognitive, emotional and social behaviors remain lacking.

From words to connections: Word use similarity as an honest signal conducive to employees’ digital communication

Bringing together considerations from three research trends (honest signals of collaboration, socio-semantic networks and homophily theory), we hypothesise that word use similarity and having similar social network positions are linked with the level of employees’ digital interaction. To verify our hypothesis, we analyse the communication of close to 1600 employees, interacting on the intranet communication forum of a large company. We study their social dynamics and the ‘honest signals’ that, in past research, proved to be conducive to employees’ engagement and collaboration. We find that word use similarity is the main driver of interaction, much more than other language characteristics or similarity in network position. Our results suggest carefully choosing the language according to the target audience and have practical implications for both company managers and online community administrators. Understanding how to better use language could, for example, support the development of knowledge sharing practices or internal communication campaigns.

Text Mining in Big Data Analytics

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.