Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets

Abstract

The novel coronavirus disease (COVID-19) has dramatically affected people’s daily lives worldwide. More specifically, since there is still insufficient access to vaccines and no straightforward, reliable treatment for COVID-19, every country has taken the appropriate precautions (such as physical separation, masking, and lockdown) to combat this extremely infectious disease. As a result, people invest much time on online social networking platforms (e.g., Facebook, Reddit, LinkedIn, and Twitter) and express their feelings and thoughts regarding COVID-19. Twitter is a popular social networking platform, and it enables anyone to use tweets. This research used Twitter datasets to explore user sentiment from the COVID-19 perspective. We used a dataset of COVID-19 Twitter posts from nine states in the United States for fifteen days (from 1 April 2020, to 15 April 2020) to analyze user sentiment. We focus on exploiting machine learning (ML), and deep learning (DL) approaches to classify user sentiments regarding COVID-19. First, we labeled the dataset into three groups based on the sentiment values, namely positive, negative, and neutral, to train some popular ML algorithms and DL models to predict the user concern label on COVID-19. Additionally, we have compared traditional bag-of-words and term frequency-inverse document frequency (TF-IDF) for representing the text to numeric vectors in ML techniques. Furthermore, we have contrasted the encoding methodology and various word embedding schemes, such as the word to vector (Word2Vec) and global vectors for word representation (GloVe) versions, with three sets of dimensions (100, 200, and 300) for representing the text to numeric vectors for DL approaches. Finally, we compared COVID-19 infection cases and COVID-19-related tweets during the COVID-19 pandemic.

Sentiment Analysis of Covid-19 Tweets using Various Machine Learning Techniques with Hyperparameter Tuning on Twitter Database

“…Random forest gives better accuracy of 97% for the dataset. Feature extraction technique combine with machine Learning algorithm are used to produce the better accuracy [3].…”

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

“…The datasets for the Russian and Kazakh languages have been preprocessed, vectorized with the TF-IDF metric, and resampled with the Random oversampling, Random undersampling, and SMOTE techniques. Then the datasets were randomly split into training and testing sets as 70% and 30%, respectively, and classified with NB, SVM, LR, k-NN, DT, RF, and XGBoost [ 71 ] ML algorithms. The results of the classification of imbalanced Russian and Kazakh datasets are shown in Tables 3 and 4 .…”

“…Totally, 132,523 texts on various topics, including Covid-19, were gathered The best results of accuracy were achieved by DT (0.91–0.95) and RF (0.96–0.99) with the Random oversampling technique Akpatsa et al [ 16 ] This paper analyzed topics, discussions, and concerns about Covid-19 vaccination using Twitter datasets. The final dataset contains 15,239 unique tweets It achieved the following accuracies with an LR (0.83), an RF (0.83), an SVM (0.84), and an NB (0.77) Yeasmin et al [ 71 ] This research explored Twitter datasets to analyze sentiments on the Covid-19 topic. The dataset included tweets from different states of the USA for 15 days.…”

Analysing sentiment change detection of Covid-19 tweets

“…Artificial intelligence (AI) has been used to analyse large amounts of text data, including microblogging platforms like Twitter, for tasks such as sentiment analysis and toxicity detection [ 5 , 6 ]. AI tools have also been applied to social media posts and search queries in order to track the spread of the Covid-19 virus and monitor public sentiment [ 7 ]. Researchers from various fields have used Twitter as a source to study the impact of the pandemic on society, as it allows for easy aggregation and analysis of large amounts of tagged and specified data.…”