Emotion Analysis from text is a recent research field originated from Sentiment Analysis. The Sentiment Analysis identify the sentiments and classify them to positive, neutral, or negative sentiments based on the text. Emotion Analysis ocus to detect and recognize emotions through the text expression which are anger, disgust, fear, happiness, sadness and surprise. The real time applications of emotion analysis can be widely applied in software engineering, website customization, education, and gaming domains. Emotion Analysis can be done by gathering social media data such as twitter, reviews, blogs. Emotion Analysis can be done on the public opinion on a particular product or on a particular topic. In this paper we are focusing to do emotion analysis of the public on real time recent corona tweets. The tweets are collected through twitter application interface. The collected real tweets are applied to pre-processing strategies to remove inconsistent and redundant factors and the pre-processed tweets are visualized through word cloud. The emotions scores are obtained using nrc_sentiment dictionary which contains basic emotions and both sentiments of positive or negative. The emotions score levels of the public are identified from the Corona Tweets and depicted through graphical analysis. © 2021, Springer Nature Singapore Pte Ltd.
An Automatic Emotion Analysis of Real Time Corona Tweets