Sentiment analysis and classification of COVID-19 tweets using machine learning classifier

Abstract

In March of 2020, the World Health Organization identified COVID-19 as a new pandemic and issued a statement to that effect. This fatal virus was able to disperse and propagate throughout several countries all over the world. During the progression of the pandemic, social networking sites like Twitter generated significant and substantial volumes of data that helped improve the quality of decisions pertaining to health care applications. In this paper, we proposed a sentiment classification using various feature extraction and machine leavening techniques for social media dataset. The system has divided into four phase data collection, preprocessing and normalization, feature extraction and feature selection and finally classification. In first phase we collect data from social media sources such as twitter using Twitter API. In second phase the tweets, data was ready for preprocessing and it was sorted into three categories: positive, neutral, and negative. During the third phase, various features were extracted from the tweets by employing a number of widely utilized approaches, including as bag of words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, and FastText, to gather feature datasets. These methods were employed to extract distinct datasets for the features. The final phase different machine learning classification algorithms are applied for detection of sentiment using machine learning. In the extensive experimental analysis, the BoW performed better results with modified support vector machine (mSVM) than existing machine learning algorithms. The proposed mSVM performed superiorly to the other classifiers by 98.15% accuracy rate. Once the tweets are correctly classified as COVID-19 tweets, it is further categorized into three sentiments that is positive, negative and neural. Proposed mSVM achieves 93% of accuracy rate for positive sentiment which better as compared to other Machine Learning (ML) classifiers.