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- W4367555499 abstract "Danmaku commenting has become popular for co-viewing on video-sharing platforms, such as Nicovideo. However, many irrelevant comments usually contaminate the quality of the information provided by videos. Such an information pollutant problem can be solved by a comment classifier trained with an abstention option, which detects comments whose video categories are unclear. To improve the performance of this classification task, this paper presents Nicovideo-specific language representations. Specifically, we used sentences from Nicopedia, a Japanese online encyclopedia of entities that possibly appear in Nicovideo contents, to pre-train a bidirectional encoder representations from Transformers (BERT) model. The resulting model named Nicopedia BERT is then fine-tuned such that it could determine whether a given comment falls into any of predefined categories. The experiments conducted on Nicovideo comment data demonstrated the effectiveness of Nicopedia BERT compared with existing BERT models pre-trained using Wikipedia or tweets. We also evaluated the performance of each model in an additional sentiment classification task, and the obtained results implied the applicability of Nicopedia BERT as a feature extractor of other social media text." @default.
- W4367555499 created "2023-05-01" @default.
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- W4367555499 date "2023-05-01" @default.
- W4367555499 modified "2023-10-18" @default.
- W4367555499 title "Effective Language Representations for Danmaku Comment Classification in Nicovideo" @default.
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- W4367555499 doi "https://doi.org/10.1587/transinf.2022dap0010" @default.
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