The Impact of Features Extraction on the Sentiment Analysis

By using TF-IDF word level (Term Frequency-Inverse Document Frequency) performance of sentiment analysis is 3-4% higher than using N-gram features, analysis is done using six classification algorithms(Decision Tree, Support vector Machine, K-Nearest Neighbour, Random Forest, Logistic Regression, Naive Bayes) and considering F-Score, Accuracy, Precision, and Recall performance parameters.