Sentiment Analysis of Covid-related Reddits
This work addresses sentiment analysis for COVID-19 discourse in specific online communities, but it is incremental as it applies existing methods to new data with minor optimizations.
This paper tackled sentiment analysis of COVID-19 related messages from Reddit subreddits by applying manual annotation and three machine learning algorithms, finding that removing shortest and longest messages improved VADER and TextBlob agreement on positive sentiments and increased the F-score of sentiment classification by all algorithms.
This paper focuses on Sentiment Analysis of Covid-19 related messages from the r/Canada and r/Unitedkingdom subreddits of Reddit. We apply manual annotation and three Machine Learning algorithms to analyze sentiments conveyed in those messages. We use VADER and TextBlob to label messages for Machine Learning experiments. Our results show that removal of shortest and longest messages improves VADER and TextBlob agreement on positive sentiments and F-score of sentiment classification by all the three algorithms