https://oldena.lpnu.ua/handle/ntb/45496| Title: | Automated building and analysis of Ukrainian Twitter corpus for toxic text detection |
| Authors: | Bobrovnyk, Kateryna |
| Affiliation: | Taras Shevchenko National University of Kyiv |
| Bibliographic description (Ukraine): | Bobrovnyk K. Automated building and analysis of Ukrainian Twitter corpus for toxic text detection / Kateryna Bobrovnyk // Computational Linguistics and Intelligent Systems. — Lviv : Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 55–56. — (Student section). |
| Bibliographic description (International): | Bobrovnyk K. Automated building and analysis of Ukrainian Twitter corpus for toxic text detection / Kateryna Bobrovnyk // Computational Linguistics and Intelligent Systems. — Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 55–56. — (Student section). |
| Is part of: | Computational Linguistics and Intelligent Systems (2), 2019 |
| Journal/Collection: | Computational Linguistics and Intelligent Systems |
| Volume: | 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019 |
| Issue Date: | 18-Apr-2019 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Lviv |
| Keywords: | toxic text detection text corpus |
| Number of pages: | 2 |
| Page range: | 55-56 |
| Start page: | 55 |
| End page: | 56 |
| Abstract: | Toxic text detection is an emerging area of study in Inter-net linguistics and corpus linguistics. The relevance of the topic can be explained by the lack of Ukrainian social media text corpora that are publicly available. Research involves building of the Ukrainian Twitter corpus by means of scraping; collective annotation of 'toxic/non-toxic' texts; construction of the obscene words dictionary for future feature engineering; and models training for the task of text classi cation (com-paring Logistic Regression, Support Vector Machine, and Deep Neural Network). |
| URI: | https://ena.lpnu.ua/handle/ntb/45496 |
| ISSN: | 2523-4013 |
| Copyright owner: | © 2019 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors. |
| URL for reference material: | https://ssrn.com/abstract=3123710 http://dx.doi.org/10.2139/ssrn.3123710 https://github.com/kennethreitz/twitter-scraper https://fasttext.cc/docs/en/language-identi |
| References (International): | 1. Pradheep, T. and Sheeba, J.I. and Yogeshwaran, T. and Pradeep Devaneyan, S.: Au-tomatic Multi Model Cyber Bullying Detection from Social Networks. In: Proceedings of the International Conference on Intelligent Computing, Salem, Tamilnadu, India. (2017) Available at SSRN: https://ssrn.com/abstract=3123710 or http://dx.doi.org/10.2139/ssrn.3123710 2. Kennedy, G. W., McCollough, A.W., Dixon, E., Bastidas, A.,Ryan, J.,Loo, C., Sahay, S.: Hack Harassment: Technology Solutions to Combat Online Harassment. In: Proceedings of the First Workshop on Abusive Language Online, pp. 73–77, Vancouver, Canada (2017) 3. Rubtsova, Y.: Constructing a corpus for sentiment classication training. SOFT-WARE SYSTEMS 1(109), 72-78 (2015) 4. Twitter Scraper, https://github.com/kennethreitz/twitter-scraper. Last accessed 13 April 2019 5. Language identication, https://fasttext.cc/docs/en/language-identi cation.html. Last accessed 13 April 2019 |
| Content type: | Article |
| Appears in Collections: | Computational linguistics and intelligent systems. – 2019 р. |
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