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
翻译:本文的重点是对RR/加拿大和r/Unkingdom Reddit的R/Canada和r/Undom 子改编的Covid-19相关信息进行感应分析。我们用人工注解和三种机器学习算法来分析这些电文中传达的情绪。我们用VADER和TextBlob来标注机器学习实验的信息。我们的结果表明,删除最短和最长的信息可以改进VADER和TextBlob关于所有三种算法中正面情绪和F情绪分类的协议。