Automatic Question Tagging using Machine Learning and Deep learning Algorithms

Abstract

Stack Overflow is a well-known website which is utilized by nearly everyone who learns to code, share their knowledge and publicly participate in this question-answering forum. The questions posted on the Stack Overflow forum by a user requires a minimum of 1 tag to be manually entered in by them. Tagging most commonly means to associate some single word information about the context of given text or question. Tagging a question is useful in identifying the category that a question or text belongs. It is also beneficial in providing ease of access to a person having a requirement of specific categories of questions. On analysis of tags associated with the questions on the website, it was found that a large number of the questions are labelled by more than one tags, with many of them not being tagged accurately. Due to this situation, it becomes challenging for the users to search for relevant tags. So, the main aim of this research task is to explore methods and compare different techniques in order to create an auto tagging system with the aid of Machine learning and deep learning facilities, accompanied by data preprocessing steps. The dataset for this purpose was taken from Kaggle, known as StackSample dataset, which is a dataset containing 10 percent of the questions present on the website. The output of the research performed for this purpose provided satisfactory results with scope of improvement.