The dataset includes airline reviews scrapped from Twitter. People have tweeted positive, negative and neutral tweets on airlines. This project analyse the sentiments of the tweets using Term frequency - Inverse Document Frequency. First, the tweets are preprocessed such as removal of hyperlinks, URLs, mentions(@), hashtags (#), emojies etc. Tweets undergo stopword removal, lemmatization and tokenization. A Document Term Matrix is created using Term frequency - Inverse Document Frequency Vectorizer. The matrix, retweet feature along with class labels are fed into Machine Learning classifier. Classifier such as Decision Tree, Random Forest, k-Support Vector Machine and k- Nearest Neighbours are trained to predict accurate sentiments. The models are trained and tested k-Fold times ( k = 5 ). Performance is evaluated using Average Accuracy, Average Precision, Average Recall and F-Score across all k-Folds.

Average Accuracy:

Decision Tree Claasifier : 67.46 % 

Random Forest Classifier: 73.04 %

Kernal-Support Vector Machine: 71.67 %

K-Nearest Neighbour: 60.54%

Clearly, Random Forest predicts better accurate results than other machine learning classifiers. k-Nearest Neighbour predict least accurate results.