Implementation Of Text Mining For Emotion Detection Using The Lexicon Method (Case Study: Tweets About Covid-19)

Information and news about Covid-19 received various responses from social media users, including Twitter users. Changes in netizen opinion time to are interesting analyze, especially the patterns of public sentiment emotions contained these opinions. Sentiment emotional conditions can illustrate public's response pandemic Indonesia. This research has two objectives, first reveal types that emerged during Second, topics or words appear most frequently each emotion class. There seven be detected, namely anger, fear, disgust, sadness, surprise, joy, trust. The dataset used is Indonesian-language tweets, which were downloaded April August 2020. method for extraction features lexicon-based using EmoLex dictionary. result obtained a monthly graph related dataset.