Embedded Emotion-based Classification of Stack Overflow Questions Towards the Question Quality Prediction
Amit Kumar Mondal, Mohammad Masudur Rahman, Chanchal Kumar Roy · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2016
Software developers often ask questions in Stack Overflow Q & A site, and their posted questions sometimes do not meet the standard guidelines.As a consequence, some of the questions are edited by expert users, some of them are down-voted, or some are even deleted permanently.Besides, the users (i.e., developers) might not get the expected solutions for their problems.In this paper, we study up-voted and down-voted questions from Stack Overflow, and analyze the relationship of embedded emotions with question quality.We use Sentiment140 API for identifying embedded emotions in the question texts, and then apply Feed-Forward Multilayer Perceptron (MLP) and Support Vector Machine (SVM) on the emotion data for developing a quality prediction model.Experiments using 38,920 Stack Overflow questions suggest about 70% precision and about 74% recall for our model with 10-fold cross-validation, and these findings clearly reveal the impact of human emotions upon the quality of a question.