Humor Detection in Product Question Answering Systems

Abstract

Community question-answering (CQA) has been established as a prominent web service enabling users to post questions and get answers from the community. Product Question Answering (PQA) is a special CQA framework where questions are asked (and are answered) in the context of a specific product. Naturally, humorous questions are integral part of such platforms, especially as some products attract humor due to their unreasonable price, their peculiar functionality, or in cases that users emphasize their critical point-of-view through humor. Detecting humorous questions in such systems is important for sellers, to better understand user engagement with their products. It is also important to signal users about flippancy of humorous questions, and that answers for such questions should be taken with a grain of salt. In this study we present a deep-learning framework for detecting humorous questions in PQA systems. Our framework utilizes two properties of the questions - Incongruity and Subjectivity, demonstrating their contribution for humor detection. We evaluate our framework over a real-world dataset, demonstrating an accuracy of 90.8%, up to 18.3% relative improvement over baseline methods. We then demonstrate the existence of product bias in PQA platforms, when some products attract more humorous questions than others. A classifier trained over unbiased data is outperformed by the biased classifier, however, it excels in the task of differentiating between humorous and non-humorous questions that are both related to the same product. To the best of our knowledge this work is the first to detect humor in PQA setting.