Sentiment analysis also known as opinion mining is a subfield within Natural Language Processing (NLP) that builds machine learning algorithms to classify a text according to the sentimental polarities of opinions it contains, e.g., positive or negative. In recent year, sentiment analysis has become a topic of great interest and development in both academics and industry. Analysing the sentiment of texts could benefit, for example, customer services, product analytics, market research etc. Take Ebay as an example. Customers on Ebay choose their preferred products based on the reviews from other users. an automatic sentiment classification system can not only help companies grasp the satisfaction level of the products, but also significantly assist new customers to locate their online shopping shelves. In this data analysis challenge, we are interested in developing such an automatic sentiment classification system that relies on machine learning techniques to learn from a large set of product reviews provided by Yelp. The levels of polarity of opinion we consider include strong negative, weak negative, neutral, weak positive, and strong positive. For example, “Website says open, Google says open, Yelp says open on Sundays. Our delivery was cancelled suddenly and no one is answering the phone. Shame” gives us a strong negative sentiment, whereas the sentiment of “They have great food & definitely excellent service. Tried their mochi mango flavored and it s definitely delis” is likely to be strong positive. The sentiment analysis task is often formulated as a classification problem, where a classifier is fed with a text and returns the corresponding sentiment label, e.g., positive, negative, or neutral. In other words, the problem of learning the sentimental polarities of opinions is reduced to a classi- fication problem. There are many machine learning methods that can be used in the classification task. They can be categorised into supervised method (like SVM) and unsupervised method (like clustering).