As social media is in boom, it is becoming very easier for customers to share their views and comments and express their feelings regarding any products which are present in online social media. . If these data can be analyzed efficiently different suggestions can be provided to the company regarding to improvise their products sale. It becomes easier for the company to understand the customer’s reaction after seeing the advertisements of the products posted on social media. This research focuses on analyzing the sentiments of customers based on the comments and reviews of products available in Facebook. Sentimental Analysis is performed to analyze the customer comments as positive, negative and neutral and later they are labeled as 0 or 1. After the labeling process, a comparative analysis is performed using different classification algorithms. The classification algorithms used are K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naïve Bayes Classifier. The classification algorithm with the highest accuracy is identified to predict the sales of online products