Enhanced Scalable Learning for Identifying and Ranking for Big Data Using Social Media Factors

In this paper describe a valuable information from online sources has become a prominent research area in information technology in recent years. In recent period, social media services provide a vast amount of user-generated data, which have great potential to contain informative news-related content. For these resources to be useful, must find a way to filter noise and only capture the content that, based on its similarity to the news media is considered valuable. In addition, the project includes a new concept called sentiment analysis. Since many automated prediction methods exist for extracting patterns from sample cases, these patterns can be used to classify new cases. The proposed system contains the method to transform these cases into a standard model of features and classes. As a result, the behavior of individuals is collected through their posts in a forum and then they are classified as positive/negative posts. The cases are encoded in terms of features in some numerical form, requiring a transformation from text to numbers and assign the positive and negative values to each word to classify the word in the document.