Machine Learning Model for Sentiment Analysis of COVID-19 Tweets

Abstract

COVID-19 pandemic presents unprecedented challenges and enormously affects different aspects of individuals' lives worldwide. The implementation of different prevention measures, the economic and social disruption, and the significant rise in the mortality rate greatly affect the peoples' spectrum of emotions. Sentiment analysis, an important branch of artificial intelligence, uses machine learning techniques to understand public perspectives and gain more insights into how they think and feel. During the pandemic, sentiment analysis increasingly contributes towards making appropriate decisions. This research aims to analyze the public sentiment related to COVID-19 by exploring social perceptions shared on Twitter, one of the most ubiquitous social networks. This goal was achieved by building a machine learning model using a dataset of COVID-19 related English tweets. Different combinations of machine learning classification algorithms (Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB)) and feature extraction techniques (Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram) were built and applied to the dataset for binary (positive, negative) and ternary (positive, negative, and neutral) classifications. A comparative study for the performance of the different models was then conducted, and the results concluded that XGB classification algorithm with unigram and bigram for binary classification achieved the highest accuracy of 90%. This sentiment analysis model can assist countries and governments in measuring the impact of the pandemic and the applied prevention measures on people's emotional and mental health and take early actions to reduce their impact or prevent them from becoming severe cases.

Supervised machine learning models for depression sentiment analysis

“…Compared to other models in the study, the XGB Classifier had the third highest accuracy score. Additionally, one previous study by Aljabri et al ( 2022 ) reported a high accuracy of 90% for the XGB Classifier, further highlighting its effectiveness. The XGB Classifier's ability to capture complex patterns and interactions in the data likely contributed to its successful performance.…”

“…The SVM model, although not consistently outperforming the other models across previous studies, still demonstrates competitive accuracy scores. For instance, in the study by Aljabri et al (2022), SVM achieved an accuracy of 88%. Similarly, in the current study, SVM/SVC performed well with an accuracy score of 96.2%.…”

On the development of an information system for monitoring user opinion and its role for the public

“…XGBoost [ 68 ] is considered one of the most superior and advanced methods among all ML algorithms, which uses the principle of boosting. This method also implements an ensemble technique as an RF algorithm.…”

Sentiment Analysis of Nepali COVID-19 Tweets using BERT-LSTM

“…However, there is a lack of research concerning COVID-19 tweet datasets, especially in the context of the Nepali language. Examination of sentiment in the English and Nepali language using machine and deep learning has been the subject to numerous studies [1], [4], [5], [7], [9], [11], [12] .…”

“…In the past, due to COVID-19, social media posts, especially tweets, increased rapidly. Much research [2][3][4] has been conducted in the area of sentiment analysis related to COVID-19 tweets for languages such as English and other widely spoken languages. Still, there has been a relative lack of research on the sentiment analysis of tweets connected to the COVID-19 in Nepali language.…”