We have Twitter Dataset. We have to convert given tweets into features which can be used for sentiment classification(Positive and Negative Tweets). Every tweet can be classified as having either a positive or negative sentiment. Example of few tweets are: *Few Positive Tweets: * @Msdebramaye I heard about that contest! Congrats girl!! UNC!!! NCAA Champs!! Franklin St.: I WAS THERE!! WILD AND CRAZY!!!!!! Nothing like it...EVER http://tinyurl.com/49955t3 Few Negative Tweets: no more taking Irish car bombs with strange Australian women who can drink like rockstars...my head hurts. Just had some bloodwork done. My arm hurts We have 100,000 tweets for training and 300,000 tweets for testing. The Ground truth is 1 for positive tweet and 0 for negative tweet. Let's try to make a sentiment Analyzer using this dataset.

We have used the Toys and Games 5-core dataset from Amazon Product review Dataset. We have to convert given reviews into features which can be used for rating the product. Each review can have an integer rating value from 1 to 5. *Few Reviews with rating 5: * I like the item pricing. My granddaughter wanted to mark on it but I wanted it just for the letters. Bought one a few years ago for my daughter and she loves it, still using it today. For the holidays we bought one for our niece and she loved it too Few Reviews with rating 1: no more taking Irish car bombs with strange Australian women who can drink like rockstars...my head hurts. Let's try to make a sentiment Analyzer using this dataset which has 167,597 reviews. The Ground truth is in range of 1-5 from 1 being bad review to 5 being good review.