Introduction

Bouchet-Valat, Milan; Jayet, Cyril

doi:10.3917/anso.192.0305

Cited by 12 publications

(12 citation statements)

References 3 publications

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“… ”tm”[ 82 83 ] ”SnowballC”[ 84 ] ”wordcloud”[ 85 ] ”RColorBrewer”[ 86 ] ”readxl”[ 87 ] ”ldatuning”[ 88 ] ”topicmodels”[ 89 ] ”tidytext”[ 90 ] ”ggplot2”[ 91 ] ”dplyr”[ 92 ] ”readtext”[ 93 ] ”quanteda”[ 30 ] ”summarytools”[ 94 ] …”

Section: Discussionunclassified

Use of Internet of Things for Chronic Disease Management

Dadkhah¹,

Mehraeen²,

Rahimnia³

et al. 2021

Journal of Medical Signals &Amp; Sensors

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Most of the countries with elderly populations are currently facing with chronic diseases. In this regard, Internet of Things (IoT) technology offers promising tools for reducing the chronic disease burdens. Despite the presence of fruitful works on the use of IoT for chronic disease management in literature, these are rarely overviewed consistently. The present study provides an overview on the use of IoT for chronic disease management, followed by ranking different chronic diseases based on their priority for using IoT in the developing countries. For this purpose, a structural coding was used to provide a list of technologies adopted so far, and then latent Dirichlet allocation algorithm was applied to find major topics in literature. In order to rank chronic diseases based on their priority for using IoT, a list of common categories of chronic diseases was subjected to fuzzy analytic hierarchy process. The research findings include lists of IoT technologies for chronic disease management and the most-discussed chronic diseases. In addition, with the help of text mining, a total of 18 major topics were extracted from the relevant pieces of literature. The results indicated that the cardiovascular disease and to a slightly lesser extent, diabetes mellitus are of the highest priorities for using IoT in the context of developing countries.

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Section: Discussionunclassified

Use of Internet of Things for Chronic Disease Management

Dadkhah¹,

Mehraeen²,

Rahimnia³

et al. 2021

Journal of Medical Signals &Amp; Sensors

View full text Add to dashboard Cite

show abstract

“…To reduce data sparsity, in the STM analysis, we further stemmed words using the Porter (1980) algorithm, dropped responses if they contained fewer than five words, and dropped words if they appeared in fewer than five responses ( Banks et al, 2018 ). Data cleaning was carried out in R version 3.6.3 ( R Core Team, 2020 ) using the tidyverse ( Wickham et al, 2019 ), stringi ( Gagolewski, 2020 ), qdap ( Rinker, 2020 ), hunspell ( Ooms, 2018 ), SnowballC ( Bouchet-Valat, 2020 ), and tidytext ( Silge and Robinson, 2016 ) packages.…”

Section: Methodsmentioning

confidence: 99%

How Did People Cope During the COVID-19 Pandemic? A Structural Topic Modelling Analysis of Free-Text Data From 11,000 United Kingdom Adults

2022

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BackgroundThe COVID-19 pandemic has had substantial impacts on lives across the globe. Job losses have been widespread, and individuals have experienced significant restrictions on their usual activities, including extended isolation from family and friends. While studies suggest population mental health worsened from before the pandemic, not all individuals appear to have experienced poorer mental health. This raises the question of how people managed to cope during the pandemic.MethodsTo understand the coping strategies individuals employed during the COVID-19 pandemic, we used structural topic modelling, a text mining technique, to extract themes from free-text data on coping from over 11,000 UK adults, collected between 14 October and 26 November 2020.ResultsWe identified 16 topics. The most discussed coping strategy was ‘thinking positively’ and involved themes of gratefulness and positivity. Other strategies included engaging in activities and hobbies (such as doing DIY, exercising, walking and spending time in nature), keeping routines, and focusing on one day at a time. Some participants reported more avoidant coping strategies, such as drinking alcohol and binge eating. Coping strategies varied by respondent characteristics including age, personality traits and sociodemographic characteristics and some coping strategies, such as engaging in creative activities, were associated with more positive lockdown experiences.ConclusionA variety of coping strategies were employed by individuals during the COVID-19 pandemic. The coping strategy an individual adopted was related to their overall lockdown experiences. This may be useful for helping individuals prepare for future lockdowns or other events resulting in self-isolation.

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“…Examples include personal pronouns and their variants, as well as major prepositions. Further, a corpus was created for the data by using the TM (Feinerer et al, 2008 ) and SnowballC packages (Bouchet-Valat, 2020 ). Also, the procedure of stemming and lemmatization was incorporated to group the transformed forms of a word so that they can be analyzed as a single item, identified by the dictionary form of the word.…”

Section: Methodsmentioning

confidence: 99%

A Tale of Two Cities: COVID-19 and the Emotional Well-Being of Student-Athletes Using Natural Language Processing

Floyd

Gulavani

et al. 2021

Front. Sports Act. Living

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Student-athletes at the Division I institutions face a slew of challenges and stressors that can have negative impacts in eliciting different emotional responses during the COVID-19 pandemic. We employed machine-learning-based natural language processing techniques to analyze the user-generated content posted on Twitter of Atlantic Coast Conference (ACC) student-athletes to study changes in their sentiment as it relates to the COVID-19 crisis, major societal events, and policy decisions. Our analysis found that positive sentiment slightly outweighed negative sentiment overall, but that there was a noticeable uptick in negative sentiment in May and June 2020 in conjunction with the Black Lives Matter protests. The most commonly expressed emotions by these athletes were joy, trust, anticipation, and fear, suggesting that they used social media as an outlet to share primarily optimistic sentiments, while still publicly expressing strong negative sentiments like fear and trepidation about the pandemic and other important contemporary events. Athletic administrators, ACC coaches, support staff, and other professionals can use findings like these to guide sound, evidence-based decision-making and to better track and promote the emotional wellness of student-athletes.

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Introduction

Cited by 12 publications

References 3 publications

Use of Internet of Things for Chronic Disease Management

Use of Internet of Things for Chronic Disease Management

How Did People Cope During the COVID-19 Pandemic? A Structural Topic Modelling Analysis of Free-Text Data From 11,000 United Kingdom Adults

A Tale of Two Cities: COVID-19 and the Emotional Well-Being of Student-Athletes Using Natural Language Processing

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