Literature DB >> 29602436

Assessing mobile health applications with twitter analytics.

Rajesh R Pai1, Sreejith Alathur2.   

Abstract

INTRODUCTION: Advancement in the field of information technology and rise in the use of Internet has changed the lives of people by enabling various services online. In recent times, healthcare sector which faces its service delivery challenges started promoting and using mobile health applications with the intention of cutting down the cost making it accessible and affordable to the people.
OBJECTIVES: The objective of the study is to perform sentiment analysis using the Twitter data which measures the perception and use of various mobile health applications among the citizens.
METHODS: The methodology followed in this research is qualitative with the data extracted from a social networking site "Twitter" through a tool RStudio. This tool with the help of Twitter Application Programming Interface requested one thousand tweets each for four different phrases of mobile health applications (apps) such as "fitness app", "diabetes app", "meditation app", and "cancer app". Depending on the tweets, sentiment analysis was carried out, and its polarity and emotions were measured.
RESULTS: Except for cancer app there exists a positive polarity towards the fitness, diabetes, and meditation apps among the users. Following a system thinking approach for our results, this paper also explains the causal relationships between the accessibility and acceptability of mobile health applications which helps the healthcare facility and the application developers in understanding and analyzing the dynamics involved the adopting a new system or modifying an existing one.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Causal loop diagram; Mobile health; Sentiment analysis; Technology adoption model; Twitter analytics

Mesh:

Year:  2018        PMID: 29602436     DOI: 10.1016/j.ijmedinf.2018.02.016

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  6 in total

1.  Diabetes on Twitter: A Sentiment Analysis.

Authors:  Elia Gabarron; Enrique Dorronzoro; Octavio Rivera-Romero; Rolf Wynn
Journal:  J Diabetes Sci Technol       Date:  2018-11-19

2.  A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums.

Authors:  Hamed Jelodar; Yongli Wang; Mahdi Rabbani; Gang Xiao; Ruxin Zhao
Journal:  J Med Syst       Date:  2020-04-07       Impact factor: 4.460

3.  Identifying Enablers of Participant Engagement in Clinical Trials of Consumer Health Technologies: Qualitative Study of Influenza Home Testing.

Authors:  Spurthy Dharanikota; Cynthia M LeRouge; Victoria Lyon; Polina Durneva; Matthew Thompson
Journal:  J Med Internet Res       Date:  2021-09-14       Impact factor: 5.428

4.  Over a decade of social opinion mining: a systematic review.

Authors:  Keith Cortis; Brian Davis
Journal:  Artif Intell Rev       Date:  2021-06-25       Impact factor: 8.139

Review 5.  Sentiment Analysis in Health and Well-Being: Systematic Review.

Authors:  Anastazia Zunic; Padraig Corcoran; Irena Spasic
Journal:  JMIR Med Inform       Date:  2020-01-28

6.  Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak.

Authors:  Ruchi Mittal; Amit Mittal; Ishan Aggarwal
Journal:  Soc Netw Anal Min       Date:  2021-10-27
  6 in total

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