HOW CAN WE ANALYSE EMOTIONS ON TWITTER DURING AN EPIDEMIC SITUATION? A FEATURES ENGINEERING APPROACH TO EVALUATE PEOPLE’S EMOTIONS DURING THE COVID-19 PANDEMIC.
Oumaima Stitini, Twil Ali, Soulaimane Kaloun, Omar Bencharef · 2021
The Coronavirus (COVID 19) pandemic has changed the way we live. Today, we live in a revolution in which the way we communicate and interact with others has forever changed. The interpretation of the COVID-19 awareness crisis and the assessment of public feelings expressed via social media under COVID-19 has become a critical task. In this research paper, using Coronavirus related Tweets, we classify public emotions associated with the pandemic. We may get an idea of how a person feels about this pandemic by examining the feelings of these tweets. For that, we give a methodological overview, the first of which concerns the approach to machine learning using traditional feature extraction with a 64% low classification accuracy, the second approach uses feature engineering to boost accuracy. Detecting emotion during an epidemic situation is an emerging research area generating interest, but which presents particular challenges due to the limited amount of resources available. In this article, we propose an emotion detection model that uses machine learning algorithms, especially feature engineering to classify the content of a tweet as joy, fear, anger or sadness. We first try to apply machine learning algorithms using traditional feature extraction and then we try to propose a feature engineering approach in order to boost and construct higheraccuracy. Our system's primary goal is to figure out how the pandemic has changed people's actions and interpret the emotions expressed through Twitter from the beginning of the pandemic.