Annotating documents with keywords or `tags' is useful for categorizing documents and helping users find a document efficiently and quickly. Question and answer (Q&A) sites also use tags to categorize questions to help ensure that their users are aware of questions related to their areas of expertise or interest. However, someone asking a question may not necessarily know the best way to categorize or tag the question, and automatically tagging or categorizing a question is a challenging task. Since a Q&A site may host millions of questions with tags and other data, this information can be used as a training and test dataset for approaches that automatically suggest tags for new questions. In this paper, we mine data from millions of questions from the Q&A site Stack Overflow, and using a discriminative model approach, we automatically suggest question tags to help a questioner choose appropriate tags for eliciting a response.

Stack Overflow is a highly successful question-answering website in the programming community, which not only provide quick solutions to programmers’ questions but also is considered as a large repository of valuable software engineering knowledge. However, despite having a very engaged and active user community, Stack Overflow currently has more than 300K unanswered questions. In this paper, we perform an initial investigation to understand why these questions remain unanswered by applying a combination of statistical and data mining techniques. Our preliminary results indicate that although there are some topics that were never answered, most questions remained unanswered because they apparently are of little interest to the user community.