Finding the Potential Accepted Answer on Stack Overflow: a Text Mining Approach

Stack Overflow serves as a widely-used, community-driven platform where developers seek assistance with programming-related issues. While the platform allows users to post questions and receive multiple answers, a significant portion of these questions do not culminate in an accepted solution. This lack of a clearly identified best answer often results in confusion for both the original poster and future visitors, as well as increased time spent navigating through numerous responses. To address this challenge, we present a method for automatically identifying the most promising answer among unaccepted ones. Our approach involves the application of text mining techniques to extract ۱۳ informative features from a large dataset comprising ۱۵,۴۶۴ questions, ۳۷,۲۷۵ answers, and ۷۲,۰۲۵ comments. These features capture various textual, structural, and user-related aspects of the posts. The extracted data are then used to train machine learning models aimed at predicting the answer most likely to be accepted. The study focuses solely on English-language content available on Stack Overflow. The proposed method demonstrates promising performance, achieving an overall accuracy of ۷۱% and an F۱ score of ۷۰%. These results suggest that automated answer recommendation can significantly enhance the user experience by reducing ambiguity and improving the efficiency of information retrieval on Q&A platforms.

کلیدواژه ها:

Stack Overflow ، Data mining ، Text Mining ، Machine-learning ، Sentiment analysis