Whether the Health Care Practices For the Patients With Comorbidities Have Changed After the Outbreak of COVID-19; Big Data Public Sentiment Analysis

TL;DR

This study analyzes social media sentiment to assess how COVID-19 has impacted healthcare practices for cancer patients, revealing moderate positivity and highlighting NLP's role in understanding patient perspectives during the pandemic.

Contribution

It introduces a novel sentiment analysis of Twitter data to evaluate cancer patients' perceptions of healthcare changes post-COVID-19, demonstrating NLP's potential in healthcare decision-making.

Findings

Majority of tweets are reasonably positive (52.6%)

Sentiment polarity indicates a moderate positive tendency

NLP can aid real-time understanding of patient behavior

Abstract

After the pandemic of SARS-CoV-2, it has influenced the health care practices around the world. Initial investigations indicate that patients with comorbidities are more fragile to this SARS-CoV-2 infection. They suggested postponing the routine treatment of cancer patients. However, few meta-analyses suggested evidences are not sufficient to hold the claim of the frailty of cancer patients to COVID-19, and they are not in favour of shelving the scheduled procedures. There are recent studies in which medical professionals, according to their competence, are referring to change the routine practices on how to manage the applicable therapeutic resources judiciously to combat this vital infection. This is a different study that reveals the cancer patients' viewpoint about how health care practices have been changed in their opinion during this pandemic year? Are they satisfied with their treatment or not? To serve the purpose, we gathered more than 60000 relevant tweets from Twitter to analyse the sentiment of cancer patients around the world. Our findings demonstrate that there is a surge in argument about cancer and its treatment after the outbreak of COVID-19. Most of the tweets are reasonable (52.6%) compared to the negative ones (24.3). We developed polarity and subjectivity distribution to better recognise the positivity/negativity in the sentiment. Results reveal that the polarity range of positive tweets is within the range of 0 to 0.5. Which means the tendency in the tweets is not so much positive but surely not negative. It is a piece of modest statistical evidence in support of how natural language processing (NLP) can be accepted to better understand the patient's behaviour in real-time, and it may facilitate the medical professional to make better decision to organise the routine management of cancer patients.