Article: Analysis of sentiments in e-health trends using Twitter Journal: International Journal of Electronic Healthcare (IJEH) 2023 Vol.13 No.4 pp.338 - 351 Abstract: Sentiment analysis (opinion mining) is a natural language processing (NLP) technique used to determine the polarity of text - whether it's positive, negative, or neutral. The objective of this paper is to conduct sentiment analysis utilising social media 'X' (formerly Twitter), focusing on healthcare-related data. The findings imply that e-health is viewed favourably. Furthermore, our analysis show that the most common hashtags connected with e-health on 'X', ranged from health technology to health information. The project pipeline includes tweet extraction using different hashtags, processing text, feature extraction, visualisation and testing accuracy of tweet sentiments using different classifiers. Tools like pandas, tweepy, NumPy, text blob, Vader, etc. have been used for this analysis. The ultimate goal is to enhance healthcare standards through effective utilisation of streamlined data collection methods in predictive analytics, aiming to boost daily operations and proactive patient care. Inderscience Publishers - linking academia, business and industry through research

Title: Analysis of sentiments in e-health trends using Twitter

Authors: Vibha Prabhu; Rajesh R. Pai; Sumith Nireshwalya; Anushka Pandey

Addresses: Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal – 576104, Karnataka, India ' Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal – 576104, Karnataka, India ' Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal – 576104, Karnataka, India ' Tufts University, Medford, MA, USA

Abstract: Sentiment analysis (opinion mining) is a natural language processing (NLP) technique used to determine the polarity of text - whether it's positive, negative, or neutral. The objective of this paper is to conduct sentiment analysis utilising social media 'X' (formerly Twitter), focusing on healthcare-related data. The findings imply that e-health is viewed favourably. Furthermore, our analysis show that the most common hashtags connected with e-health on 'X', ranged from health technology to health information. The project pipeline includes tweet extraction using different hashtags, processing text, feature extraction, visualisation and testing accuracy of tweet sentiments using different classifiers. Tools like pandas, tweepy, NumPy, text blob, Vader, etc. have been used for this analysis. The ultimate goal is to enhance healthcare standards through effective utilisation of streamlined data collection methods in predictive analytics, aiming to boost daily operations and proactive patient care.

Keywords: sentiment analysis; e-health; machine learning; opinion mining; Twitter mining.

DOI: 10.1504/IJEH.2023.138257

International Journal of Electronic Healthcare, 2023 Vol.13 No.4, pp.338 - 351

Received: 18 Apr 2023
Accepted: 21 Jan 2024
Published online: 30 Apr 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Analysis of sentiments in e-health trends using Twitter

Keep up-to-date