Standard
Toxic Comment Classification Service in Social Network. / Dolgushin, Mikhail; Ismakova, Dayana; Bidulya, Yuliya; Krupkin, Igor; Barskaya, Galina; Lesiv, Anastasiya.
Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings: 23-я Международная конференция, SPECOM 2021, Санкт-Петербург, Россия, 27–30 сентября 2021 г., Труды. ред. / Alexey Karpov; Rodmonga Potapova. Том 12997 Springer. ред. Cham : Springer Nature, 2021. стр. 157-165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12997 LNAI).
Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Harvard
Dolgushin, M, Ismakova, D, Bidulya, Y, Krupkin, I, Barskaya, G & Lesiv, A 2021,
Toxic Comment Classification Service in Social Network. в A Karpov & R Potapova (ред.),
Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings: 23-я Международная конференция, SPECOM 2021, Санкт-Петербург, Россия, 27–30 сентября 2021 г., Труды. Springer изд., Том. 12997, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 12997 LNAI, Springer Nature, Cham, стр. 157-165, 23rd International Conference on Speech and Computer, Virtual, Online, Российская Федерация,
27/09/21.
https://doi.org/10.1007/978-3-030-87802-3_15
APA
Dolgushin, M., Ismakova, D., Bidulya, Y., Krupkin, I., Barskaya, G., & Lesiv, A. (2021).
Toxic Comment Classification Service in Social Network. в A. Karpov, & R. Potapova (Ред.),
Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings: 23-я Международная конференция, SPECOM 2021, Санкт-Петербург, Россия, 27–30 сентября 2021 г., Труды (Springer ред., Том 12997, стр. 157-165). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12997 LNAI). Springer Nature.
https://doi.org/10.1007/978-3-030-87802-3_15
Vancouver
Dolgushin M, Ismakova D, Bidulya Y, Krupkin I, Barskaya G, Lesiv A.
Toxic Comment Classification Service in Social Network. в Karpov A, Potapova R, Редакторы, Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings: 23-я Международная конференция, SPECOM 2021, Санкт-Петербург, Россия, 27–30 сентября 2021 г., Труды. Springer ред. Том 12997. Cham: Springer Nature. 2021. стр. 157-165. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-030-87802-3_15
Author
Dolgushin, Mikhail ; Ismakova, Dayana ; Bidulya, Yuliya ; Krupkin, Igor ; Barskaya, Galina ; Lesiv, Anastasiya. /
Toxic Comment Classification Service in Social Network. Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings: 23-я Международная конференция, SPECOM 2021, Санкт-Петербург, Россия, 27–30 сентября 2021 г., Труды. Редактор / Alexey Karpov ; Rodmonga Potapova. Том 12997 Springer. ред. Cham : Springer Nature, 2021. стр. 157-165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{597bc611ebfa4a80b59025086ec4576a,
title = "Toxic Comment Classification Service in Social Network",
abstract = "The article discusses the development of an online tool for moderating the content of social network groups. The use of classification using machine learning methods is proposed as the main element of the system. The creation of the feature set of messages is assumed by extracting the content features of the text, as well as the use of word embeddings vectors. The authors conducted a series of experiments to find the best combination of vector representation, content features and classification method. Tests on a dataset of 11 thousand messages in Russian showed the result of 87% accuracy. The architecture of the group moderator{\textquoteright}s web application with the ability to automatically apply classification results to control users and display posts is proposed.",
keywords = "Feature extraction, Moderation, Social media, Text classification, Toxic detection",
author = "Mikhail Dolgushin and Dayana Ismakova and Yuliya Bidulya and Igor Krupkin and Galina Barskaya and Anastasiya Lesiv",
note = "Dolgushin, M., Ismakova, D., Bidulya, Y., Krupkin, I., Barskaya, G., Lesiv, A. (2021). Toxic Comment Classification Service in Social Network. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science(), vol 12997. Springer, Cham. https://doi.org/10.1007/978-3-030-87802-3_15; 23rd International Conference on Speech and Computer, SPECOM 2021 ; Conference date: 27-09-2021 Through 30-09-2021",
year = "2021",
doi = "10.1007/978-3-030-87802-3_15",
language = "English",
isbn = "9783030878016",
volume = "12997",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "157--165",
editor = "Alexey Karpov and Rodmonga Potapova",
booktitle = "Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings",
address = "Germany",
edition = "Springer",
url = "http://specom.nw.ru/2021/",
}
RIS
TY - GEN
T1 - Toxic Comment Classification Service in Social Network
AU - Dolgushin, Mikhail
AU - Ismakova, Dayana
AU - Bidulya, Yuliya
AU - Krupkin, Igor
AU - Barskaya, Galina
AU - Lesiv, Anastasiya
N1 - Conference code: 23
PY - 2021
Y1 - 2021
N2 - The article discusses the development of an online tool for moderating the content of social network groups. The use of classification using machine learning methods is proposed as the main element of the system. The creation of the feature set of messages is assumed by extracting the content features of the text, as well as the use of word embeddings vectors. The authors conducted a series of experiments to find the best combination of vector representation, content features and classification method. Tests on a dataset of 11 thousand messages in Russian showed the result of 87% accuracy. The architecture of the group moderator’s web application with the ability to automatically apply classification results to control users and display posts is proposed.
AB - The article discusses the development of an online tool for moderating the content of social network groups. The use of classification using machine learning methods is proposed as the main element of the system. The creation of the feature set of messages is assumed by extracting the content features of the text, as well as the use of word embeddings vectors. The authors conducted a series of experiments to find the best combination of vector representation, content features and classification method. Tests on a dataset of 11 thousand messages in Russian showed the result of 87% accuracy. The architecture of the group moderator’s web application with the ability to automatically apply classification results to control users and display posts is proposed.
KW - Feature extraction
KW - Moderation
KW - Social media
KW - Text classification
KW - Toxic detection
UR - http://www.scopus.com/inward/record.url?scp=85116382178&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87802-3_15
DO - 10.1007/978-3-030-87802-3_15
M3 - Conference contribution
AN - SCOPUS:85116382178
SN - 9783030878016
VL - 12997
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 157
EP - 165
BT - Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings
A2 - Karpov, Alexey
A2 - Potapova, Rodmonga
PB - Springer Nature
CY - Cham
T2 - 23rd International Conference on Speech and Computer, SPECOM 2021
Y2 - 27 September 2021 through 30 September 2021
ER -
