Since the dawn of internet, e-shopping vendors like Amazon have grown in popularity. Customers express their opinion or sentiment by giving feedbacks in the form of text. Sentiment analysis is the process of determining the opinion or feeling expressed as either positive, negative or neutral. Capturing the exact sentiment of a review is a challenging task. In this paper, the various preprocessing techniques like HTML tags and URLs removal, punctuation, whitespace, special character removal and stemming are used to eliminate noise. The preprocessed data is represented using feature selection techniques like term frequency-inverse document frequency (TF–IDF). The classifiers like K-Nearest Neighbour (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naive Bayes (NB) are used to classify sentiment of Amazon book reviews. Finally, we present a comparison of (i) Accuracy of various classifiers, (ii) Time elapsed by each classifier and (iii) Sentiment score of various books.