IEICE TRANSACTIONS on Information
Effective Language Representations for Danmaku Comment Classification in Nicovideo
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.838-846
- Publication Date
- 2023/05/01
- Publicized
- 2023/01/16
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DAP0010
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
Hiroyoshi NAGAO
Doshisha University
Koshiro TAMURA
Doshisha University
Marie KATSURAI
Doshisha University
Keyword
Latest Issue
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Hiroyoshi NAGAO, Koshiro TAMURA, Marie KATSURAI, "Effective Language Representations for Danmaku Comment Classification in Nicovideo" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 838-846, May 2023, doi: 10.1587/transinf.2022DAP0010.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DAP0010/_p
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@ARTICLE{e106-d_5_838,
author={Hiroyoshi NAGAO, Koshiro TAMURA, Marie KATSURAI, },
journal={IEICE TRANSACTIONS on Information},
title={Effective Language Representations for Danmaku Comment Classification in Nicovideo},
year={2023},
volume={E106-D},
number={5},
pages={838-846},
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.},
keywords={},
doi={10.1587/transinf.2022DAP0010},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Effective Language Representations for Danmaku Comment Classification in Nicovideo
T2 - IEICE TRANSACTIONS on Information
SP - 838
EP - 846
AU - Hiroyoshi NAGAO
AU - Koshiro TAMURA
AU - Marie KATSURAI
PY - 2023
DO - 10.1587/transinf.2022DAP0010
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - 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.
ER -
English
