The Emergence of Machine Learning, Recommender Systems, Sentiment Analysis and Classification, Socia

Recommender systems have become extremely common in recent years, and are applied in a variety of applications. Recommender systems are systems that predict the user’s rating and/or preference for movies, products, restaurants, life insurance, financial services, online dating, and Twitter.

Social media such as Facebook, MySpace, and Twitter have exploded as online services where people create and share content and network at a fast rate. In addition, due to their ease of use, speed and reach, social media is setting trends in topics that range from politics to technology and the entertainment. Social media is also form of collective wisdom (crowd sourcing) and combined with Machine Learning techniques is powerful tool at predicting real-world outcomes that can be used to make quantitative and quantitative predictions that outperform those of artificial markets.

Have you ever wondered if you can predict an activity in the future using tweets? (i.e. given a set of tweets and a future timeframe, to extract a set of activities that will be popular during that timeframe.) While this is a very generic question, our main focus will be on prediction of the Movies’ popularity because considerable interest among the social media users, diversity of the opinions and real-world outcomes can be easily observed from box-office revenue for movies. Our Artificial Intelligence system uses Twitter to forecast and predict the movie’s popularity, movie’s success and rank much before its release. Our Artificial Intelligence system based on Machine Learning, recommender system, wisdom of crowd and sentiment analysis of Tweets can outperform market-based predictors and showed the effetiveness of the forecasting power of the social media.

Sentiment Analysis: In addition, our Artificial intelligence system is used to predict rating of product reviews. The Multi-Domain Sentiment Dataset contains product reviews taken from Amazon.com from many product types (domains). Some domains (books and DVDs) have hundreds of thousands of reviews. Others (musical instruments) have only a few hundred. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. (http://www.cs.jhu.edu/~mdredze/datasets/sentiment

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