Predicting answer acceptability for question-answering system

Question-answering (QA) platforms such as Stack Overflow, Quora, and Stack Exchange have become favourite places to exchange knowledge with community users. Finding answers to simple or complex questions is easier on QA platforms nowadays. Due to a large number of responses from users all around the world, these CQA systems are currently facing massive problems. Stack Overflow allows users to ask questions and give answers or comments on others' posts. Consequently, Stack Overflow also rewards those users whose posts are appreciated by the community in the form of reputation points. The accepted answer provides maximum reputation points to the answerer. More reputation points allow getting more website privileges. Hence, each answerer needs to get their answer accepted. Very little research has been done to check whether the user's answers will be accepted or not. This paper proposes a model that predicts answer acceptability and its reason. The model's findings help the answerer know about the answer acceptance; if the model predicted the probability of acceptance is less, the answerer might revise their answer immediately. The comparison with the state-of-the-art literature confirmed that the proposed model achieves better performance.