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.
|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|
|Number of pages||12|
|Publication status||Published - 2018|
|Event||13th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2018 - Oviedo, Spain|
Duration: 2018 Jun 20 → 2018 Jun 22
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||13th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2018|
|Period||18/6/20 → 18/6/22|
Bibliographical noteFunding 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).
© Springer International Publishing AG, part of Springer Nature 2018.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)