论文标题
共同相关红色的情感分析
Sentiment Analysis of Covid-related Reddits
论文作者
论文摘要
本文重点介绍了来自R/Canada和Reddit的R/Unitedkingdom Subreddits的Covid-19相关消息的情感分析。我们应用手动注释和三种机器学习算法来分析这些消息中传达的情绪。我们使用Vader和TextBlob为机器学习实验标记消息。我们的结果表明，删除最短和最长的消息可以改善Vader和TextBlob关于所有三种算法的积极情感和情感分类的F-评分的协议
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
