This is the summary of the article "Sentiment Analysis of Cancer Patients' Tweets during COVID-19 Pandemic" by Bilal Ahmad, published in the IEEE conference:
This article presents a sentiment analysis of tweets from cancer patients around the world to understand their point of view about their treatment during the COVID-19 pandemic. The researchers gathered more than 150,000 relevant tweets from Twitter (Jan 2020 to April 2020) and used polarity and subjectivity distribution to better recognize the positivity/negativity in the sentiment.
The results of the study showed that most tweets were reasonable (52.6%) and negative (24.3%). The polarity range of positive tweets was within the range of 0 to 0.5, indicating that the tweets were neither negative nor positive. This statistical evidence supports the use of natural language processing (NLP) to better understand patient behavior in real-time and make better decisions about cancer treatment.
The article concludes by discussing the implications of the findings for medical professionals. The researchers suggest that NLP can be used to better understand patient behavior in real-time and make better decisions about cancer treatment. They also suggest that NLP can be used to develop personalized interventions for cancer patients during the COVID-19 pandemic.
Here are some additional details from the article:
- The researchers used a variety of NLP techniques to analyze the tweets, including sentiment analysis, polarity distribution, and subjectivity distribution.
- They found that the most common topics in the tweets were treatment delays, fear of infection, and financial concerns.
- The researchers also found that the sentiment of the tweets changed over time, with more negative tweets being posted in the early stages of the pandemic.
Overall, the article provides valuable insights into the experiences of cancer patients during the COVID-19 pandemic. The findings of the study suggest that NLP can be used to better understand patient behavior in real-time and make better decisions about cancer treatment.
