Predicting tags for stack overflow questions using different classifiers

摘要

The adequacy of any online education forum depends on the user's experience based on users interests and demands. So it is the fundamental requirement to design a system which considers users interest in to account when putting content online. Many online websites such as Quora, GeeksforGeeks, and StackExchange have large scale of data in terms of questions and answers of users. Large-Scale datasets are available on these websites that can be mined and pre-processed using text classification and can be used to know users query regarding a particular topic. Information that is provided should be relevant to users interest. We propose a system that will take significant amount of data from a website and use that data for different approaches to predict the tag for the website Stack overflow posts and achieve a better accuracy for 1000 most frequent tags.