Emotion and Sentiment Recognition using Natural Language Processing and Machine Learning Techniques during Disaster Situation
Peter Hart, Tasnia Rahman, Sathish Kumar · 2022 IEEE Symposium Series on Computational Intelligence (SSCI) · 2022
Detecting emotions of the residents during disaster scenario is important for the government agencies to properly take care of its constituents. COVID-19 is a global disaster scenario that has caused unprecedented shutdowns, unemployment, death, and isolation. The behavioral and emotional health impact of COVID-19 is investigated in this study through the use of sentiment analysis and emotion recognition. The dataset is formed by collecting tweets from the seven months before COVID-19 became prevalent in March 2020 and the following seven months after. VADER sentiment analysis method was used to determine if a tweet was positive, negative, or neutral. For emotion recognition, several machine learning algorithms were evaluated and Convolutional Neural Network (CNN) Long-Short Term Memory (LSTM) performed better than the other models. Hence, CNN-LSTM was used to classify the emotion of each tweet as either anger, fear, joy, or sadness. Each tweet has a longitude and latitude stored with it that was geocoded to give the exact location, which was used to compare the states within the USA, and finally compare the USA as a whole with Canada, and Mexico. Sentiment analysis shows that all countries have experienced an increase in negative tweets. Emotion recognition shows that compared to Canada and Mexico, USA has experienced a steep drop in emotional health.