Sentiment analysis on covid-19 vaccine using Twitter data: A NLP approach

Sentiment analysis is the process of mining the perception of people towards a service, product, policy or imminent issue from textual data. In this project, tweets relevant to Covid-19 Vaccine are extracted utilizing the Tweepy library. Next, tweet texts are converted into usable form in order to do sentiment analysis. After this, SentiWordNet lexicon is used to label the sentiment of the tweets. Stop words removal, Lemmatizing, stemming operations are also applied on the COVID-19 Vaccine tweets text data. Count Vectorizer and Tfidf Vectorizer are applied for mathematical conversion of the preprocessed text. Then, nine classification techniques namely - Multinomial-NB, Bernoulli-NB, Logistic-Regression, Ridge Classifier, Passive-Aggressive-Classifier, Perceptron, Random Forest classifier, AdaBoostClassifier and Linear SVM are applied on the dataset obtained for sentiment classification and results are obtained in terms of accuracy. The best cross validation test score obtained is 0.785 with Logistic Regression Classifier and TfidfVectorizer.