Analysis of sentiments in e-health trends using Twitter

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.