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Combining FastText and Glove Word Embedding for Offensive and Hate speech Text Detection
Open accessAbstract
Over the past decade, increased use of social media has led to an increase in hate content. To address this issue new solutions must be implemented to filter out this kind of inappropriate content. Because manual filtering is difficult, several studies have been conducted in order to automate the process. This paper introduces a method based on a combination of Glove and FastText word embedding as input features and a BiGRU model to identify hate speech from social media websites. The obtained results show that our proposed model (BiGRU Glove FT) is effective in detecting inappropriate content. This model detect hate speech on OLID dataset, using an effective learning process that classifies the text into offensive and not offensive language. The performance of the system attained 84%, 87%, 93%, 90% accuracy, precision, recall, and f1-score respectively.
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“The GloVe algorithm utilizes word co-occurrence data or global statistics to infer semantic relationships between words in the corpus, as opposed to word2vec's dependence on local context windows for deriving these associations (Badri et al. 2022; Gan et al. 2022; Curto et al. 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Badri et al. 2022
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“Pre-trained word embeddings can predict word contexts in an unsupervised manner, assuming that words in close proximity within a sentence have similar meanings (Badri et al. 2022; Kowsher et al. 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Badri et al. 2022
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
IOP Conference Series: Materials Science and Engineering
(2022) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1231/1/011001 · Cited by 12,131
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
Natural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,923
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878
Research works printed but not linked: 11
- Gaydhani, A., Doma, V., Kendre, S., Bhagwat, L., 2018. Detecting hate speech and offensive language on twitter using machine learning: An n-gram and tfdf based approach. arXiv preprint arXiv:1809.08651 . · arXiv
- Giglou, 2021, Profling haters on twitter using statistical and contextualized embeddings, CLEF
- Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V., 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 . · arXiv
- Mollas, I., Chrysopoulou, Z., Karlos, S., Tsoumakas, G., 2020. Ethos: an online hate speech detection dataset. arXiv preprint arXiv:2006.08328. · arXiv
- Pitsilis, G.K., Ramampiaro, H., Langseth, H., 2018. Detecting ofensive language in tweets using deep learning. arXiv preprint arXiv:1801.04433 . · arXiv
- Ramraj, 2016, Experimenting xgboost algorithm for prediction and classification of different datasets, International Journal of Control Theory and Applications, 9, 651
- Romaissa Beddiar, D., Saroar Jahan, M., Oussalah, M., 2021. Data expansion using back translation and paraphrasing for hate speech detection. arXiv e-prints, arXiv–2106. · DOI
- Saha, P., Mathew, B., Goyal, P., Mukherjee, A., 2019. Hatemonitors: Language agnostic abuse detection in social media. arXiv preprint arXiv:1909.12642 . · arXiv
- Sanh, V., Debut, L., Chaumond, J., Wolf, T., 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 . · arXiv
- Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V., 2019. Xlnet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237 . · arXiv
- Young, 2018, 13, 55
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Metadata
- Authors
- Badri, N., Kboubi, F., & Chaibi, A. H.
- Affiliations
- National School of Computer Science (TN)
- Corresponding author
- Badri, Nabil
- ORCID
- Badri, Nabil
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 207
- Pages
- 769-778
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W4312750750
- Cited by
- 65
- References
- 57
- Citations by year
- 2026: 17 · 2025: 14 · 2024: 16 · 2023: 17 · 2022: 1
- FWCI
- 6.63
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- hate speech detection, offensive language detection, GloVe embeddings, FastText word embeddings, bidirectional GRU
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