A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments

Seok Jun Bu; Sung Bae Cho

doi:10.1007/978-3-319-92639-1_47

A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments

Seok Jun Bu, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

18 Citations (Scopus)

Abstract

The cyberbullying is becoming a significant social issue, in proportion to the proliferation of Social Network Service (SNS). The cyberbullying commentaries can be categorized into syntactic and semantic subsets. In this paper, we propose an ensemble method of the two deep learning models: One is character-level CNN which captures low-level syntactic information from the sequence of characters and is robust to noise using the transfer learning. The other is word-level LRCN which captures high-level semantic information from the sequence of words, complementing the CNN model. Empirical results show that the performance of the ensemble method is significantly enhanced, outperforming the state-of-the-art methods for detecting cyberbullying comment. The model is analyzed by t-SNE algorithm to investigate the mutually cooperative relations between syntactic and semantic models.

Original language	English
Title of host publication	Hybrid Artificial Intelligent Systems - 13th International Conference, HAIS 2018, Proceedings
Editors	Alvaro Herrero, Hector Quintian, Jose Antonio Saez, Emilio Corchado, Francisco Javier de Cos Juez, Jose Ramon Villar, Enrique A. de la Cal
Publisher	Springer Verlag
Pages	561-572
Number of pages	12
ISBN (Print)	9783319926384
DOIs	https://doi.org/10.1007/978-3-319-92639-1_47
Publication status	Published - 2018
Event	13th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2018 - Oviedo, Spain Duration: 2018 Jun 20 → 2018 Jun 22

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10870 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	13th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2018
Country/Territory	Spain
City	Oviedo
Period	18/6/20 → 18/6/22

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2016-0-00562, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly).

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-92639-1_47

Cite this

Bu, S. J., & Cho, S. B. (2018). A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments. In A. Herrero, H. Quintian, J. Antonio Saez, E. Corchado, F. J. de Cos Juez, J. R. Villar, & E. A. de la Cal (Eds.), Hybrid Artificial Intelligent Systems - 13th International Conference, HAIS 2018, Proceedings (pp. 561-572). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10870 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-92639-1_47

Bu, Seok Jun ; Cho, Sung Bae. / A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments. Hybrid Artificial Intelligent Systems - 13th International Conference, HAIS 2018, Proceedings. editor / Alvaro Herrero ; Hector Quintian ; Jose Antonio Saez ; Emilio Corchado ; Francisco Javier de Cos Juez ; Jose Ramon Villar ; Enrique A. de la Cal. Springer Verlag, 2018. pp. 561-572 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The cyberbullying is becoming a significant social issue, in proportion to the proliferation of Social Network Service (SNS). The cyberbullying commentaries can be categorized into syntactic and semantic subsets. In this paper, we propose an ensemble method of the two deep learning models: One is character-level CNN which captures low-level syntactic information from the sequence of characters and is robust to noise using the transfer learning. The other is word-level LRCN which captures high-level semantic information from the sequence of words, complementing the CNN model. Empirical results show that the performance of the ensemble method is significantly enhanced, outperforming the state-of-the-art methods for detecting cyberbullying comment. The model is analyzed by t-SNE algorithm to investigate the mutually cooperative relations between syntactic and semantic models.",

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note = "Funding Information: Acknowledgements. This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2016-0-00562, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly). Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 13th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2018 ; Conference date: 20-06-2018 Through 22-06-2018",

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Bu, SJ & Cho, SB 2018, A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments. in A Herrero, H Quintian, J Antonio Saez, E Corchado, FJ de Cos Juez, JR Villar & EA de la Cal (eds), Hybrid Artificial Intelligent Systems - 13th International Conference, HAIS 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10870 LNAI, Springer Verlag, pp. 561-572, 13th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2018, Oviedo, Spain, 18/6/20. https://doi.org/10.1007/978-3-319-92639-1_47

A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments. / Bu, Seok Jun; Cho, Sung Bae.
Hybrid Artificial Intelligent Systems - 13th International Conference, HAIS 2018, Proceedings. ed. / Alvaro Herrero; Hector Quintian; Jose Antonio Saez; Emilio Corchado; Francisco Javier de Cos Juez; Jose Ramon Villar; Enrique A. de la Cal. Springer Verlag, 2018. p. 561-572 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10870 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Bu, Seok Jun

AU - Cho, Sung Bae

N1 - Funding Information: Acknowledgements. This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2016-0-00562, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly). Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - The cyberbullying is becoming a significant social issue, in proportion to the proliferation of Social Network Service (SNS). The cyberbullying commentaries can be categorized into syntactic and semantic subsets. In this paper, we propose an ensemble method of the two deep learning models: One is character-level CNN which captures low-level syntactic information from the sequence of characters and is robust to noise using the transfer learning. The other is word-level LRCN which captures high-level semantic information from the sequence of words, complementing the CNN model. Empirical results show that the performance of the ensemble method is significantly enhanced, outperforming the state-of-the-art methods for detecting cyberbullying comment. The model is analyzed by t-SNE algorithm to investigate the mutually cooperative relations between syntactic and semantic models.

AB - The cyberbullying is becoming a significant social issue, in proportion to the proliferation of Social Network Service (SNS). The cyberbullying commentaries can be categorized into syntactic and semantic subsets. In this paper, we propose an ensemble method of the two deep learning models: One is character-level CNN which captures low-level syntactic information from the sequence of characters and is robust to noise using the transfer learning. The other is word-level LRCN which captures high-level semantic information from the sequence of words, complementing the CNN model. Empirical results show that the performance of the ensemble method is significantly enhanced, outperforming the state-of-the-art methods for detecting cyberbullying comment. The model is analyzed by t-SNE algorithm to investigate the mutually cooperative relations between syntactic and semantic models.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Villar, Jose Ramon

A2 - de la Cal, Enrique A.

PB - Springer Verlag

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Bu SJ, Cho SB. A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments. In Herrero A, Quintian H, Antonio Saez J, Corchado E, de Cos Juez FJ, Villar JR, de la Cal EA, editors, Hybrid Artificial Intelligent Systems - 13th International Conference, HAIS 2018, Proceedings. Springer Verlag. 2018. p. 561-572. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-92639-1_47

A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments

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