Open AccessImpact of the COVID-19 pandemic on the expression of emotions in social media
Author(s) -
Debabrata Ghosh,
AUTHOR_ID
Publication year - 2020
Publication title -
multiple criteria decision making
Language(s) - English
Resource type - Journals
ISSN - 2084-1531
DOI - 10.22367/mcdm.2020.15.02
Subject(s) - disgust , social media , surprise , random forest , naive bayes classifier , artificial intelligence , support vector machine , machine learning , computer science , sentiment analysis , anger , automatic summarization , decision tree , hyperparameter , logistic regression , emotion classification , classifier (uml) , psychology , natural language processing , social psychology , world wide web
In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors. Keywords: classification, COVID-19, emotion, emotion analysis, Naïve Bayes, Pandemic, Random Forest, SVM.