Exploring the Feature Selection-Based Data Analytics Solutions for Text Mining Online Communities by Investigating the Influential Factors: A Case Study of Programming CQA in Stack Overflow

Abstract

Community question answering (CQA) services accumulate large amount of knowledge through the voluntary services of the community across the globe. In fact, CQA services gained much popularity recently compared to other Internet services in obtaining and exchanging information. Stack Overflow is an example of such a service that targets programmers and software developers. In general, most questions in Stack Overflow are usually ended up with an answer accepted by the askers. However, it is found that the number of unanswered or ignored questions has increased significantly in the past few years. Understanding the factors that contribute to questions being answered as well as questions remain ignored can help information seekers to improve the quality of their questions and increase their chances of getting answers from the community in Stack Overflow. In this study, we attempt to identify by data mining techniques the relevant features that will help predict the quality of questions, and validate the reliability of the features using some of the state-of-the-art classification algorithms. The features to be obtained have to be significant in the sense that they can help Stack Overflow to improve their existing CQA service in terms of user satisfaction in obtaining quality answers from their questions.