Identification of Optimal Model for Multi-Class Classification of COVID Tweets

Covid-19 is the worst-hit pandemic that has affected humankind to date. It sent all major nations around the globe into lockdowns for at least half of 2020. The lockdown started to increase unrest in the population, and even some of them started sharing the emotions infused by the unrest and lockdown over social media platforms in the form of posts, stories, articles. The emotions that underlie those posts can be categorized into three categories positive, negative and neutral, and the individual posts can be classified into respective labels. We considered one of the social platforms' Twitter and collected Twitter tweets. The dataset included the text from the tweet along with emotion. The dataset was pre-processed, including removing stop words from the dataset, stemming and lemmatizing the words from tweets text. Our work focused on various models that can be used to analyze sentiment and classification. The work includes implementing standard classification models like Naive-Bayes multinomial Classifier, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree Classifier, Logistic Regression, Deep Learning models - Long short-term memory (LSTM), Gated recurrent unit (GRU), Bidirectional long-short term memory (Bidirectional LSTM), Bidirectional Encoder Representations from Transformers (BERT). The results from all these models are compared and tried to establish the most efficient model based on accuracy. The BERT model outperformed all other methods when compared to other models developed using Machine Learning (ML) and Deep Learning (DL) techniques.