Selective Classification of Danmaku Comments Using Distributed Representations
Koshiro Tamura, Marie Katsurai · 2021
Danmaku commenting has become popular for co-viewing on video-sharing platforms. However, there are usually a large number of irrelevant comments, that contaminate the quality of the information provided by videos. To address this problem, this paper presents a novel approach of classifying Danmaku comments into video categories. Specifically, we use BERT as the backbone architecture to extract semantic features from comments. We introduce a loss function that has an abstention option, which enables the detection of comments that do not fall into any predefined category. The experiments that we conducted using Nicovideo data demonstrated that our selective classification approach effectively discarded those that were irrelevant to a video’s content. We also present a method for subdividing the existing video categories based on the results of Danmaku comment classification. This entails a potential application of our method in hierarchical video clustering.