Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
The novel corona-virus (also known as Covid-19) has led to a pandemic, impacting more than 200 countries across the globe. With this huge scale, there are a very large number of tweets coming out from every corner of the world, about Covid-19. Analyzing the tweets and detecting the major topics and concerns people are posting about, can help us to better understand the situation, and come up with better planning. In this work, we propose a model based on sentence Transformer to detect the main topics of Tweets in recent months. The proposed model first learns sentence-level representation of tweets, and then group them based on their embedding similarities into some groups, and then detect the most important words in each cluster based on their frequency and average similarity to other words. Through experimental results, we show that our model can detect very informative topics, by processing the tweets on sentence level (which can preserve the overall meaning of the tweets). The proposed model is trained in an unsupervised fashion, and can be applied to any dataset our textual data from social media websites.
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