Autonomous Tagging of Stack Overflow Questions

Abstract

Educational resources like question-and-answer websites like Stack Exchange and Quora are growing in popularity online. A large number of these gatherings depend on labeling, which includes a part marking a post with a suitable assortment of subjects that depict the post and make it more straightforward to find and sort. We give a multi-name order framework that naturally distinguishes clients' requests to upgrade the client experience. A straight SVM and a carefully selected portion of the researched highlight set are used to create a one-versus-rest classifier for a Stack Overflow dataset. By utilizing a subsample of the initial data that is restricted to 100 labels and at least 500 events of each label throughout the data, our characterization framework achieves an ideal F1 score of 62.35 percent.