Suchergebnisse - LSTM and SVM NLP Machine learning Transformer SVM offensive language

Arabic Offensive Language Classification: Leveraging Transformer, LSTM, and SVM

Autoren: Rasheed, Areeg Fahad Zarkoosh, M. Abbas, Safa et al.

Weitere Verfasser: Rasheed, Areeg Fahad Zarkoosh, M. Abbas, Safa et al.

Quelle: 2023 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). :1-6

Schlagwörter: Transformer, LSTM and SVM NLP Machine learning Transformer SVM offensive language, SVM, Machine learning, offensive language, and SVM NLP, [INFO] Computer Science [cs], LSTM, 10. No inequality

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-04572679v1
https://hal.science/hal-04572679v1/document
https://doi.org/10.1109/icmlant59547.2023.10372866

RU-OLD: A Comprehensive Analysis of Offensive Language Detection in Roman Urdu Using Hybrid Machine Learning, Deep Learning, and Transformer Models.

Autoren: Zain, Muhammad Hussain, Nisar Qasim, Amna et al.

Quelle: Algorithms; Jul2025, Vol. 18 Issue 7, p396, 19p

Schlagwörter: MACHINE learning, DEEP learning, NATURAL language processing, TRANSFORMER models, URDU language, INVECTIVE

Reviews & Products: YOUTUBE (Web resource)

ORUD-Detect: A Comprehensive Approach to Offensive Language Detection in Roman Urdu Using Hybrid Machine Learning–Deep Learning Models with Embedding Techniques.

Autoren: Hussain, Nisar Qasim, Amna Mehak, Gull et al.

Quelle: Information; Feb2025, Vol. 16 Issue 2, p139, 23p

Schlagwörter: MACHINE learning, LOW-resource languages, SUPPORT vector machines, LONG short-term memory, URDU language

Adversarially Robust Multitask Learning for Offensive and Hate Speech Detection in Arabic Text Using Transformer-Based Models and RNN Architectures.

Autoren: Alshahrani, Eman S. Aksoy, Mehmet S.

Quelle: Applied Sciences (2076-3417); Sep2025, Vol. 15 Issue 17, p9602, 30p

Schlagwörter: ARABIC language, MACHINE learning, TRANSFORMER models, SWEARING (Profanity), TEXT mining, INVECTIVE, RECURRENT neural networks

Advancements in Offensive Language Detection: A Comprehensive Review and Experimental Analysis.

Autoren: Nalini, C. Shanthakumari, R. Maria, Y. Agashia et al.

Quelle: Journal of Information Assurance & Security; 2024, Vol. 19 Issue 4, p162-179, 18p

Schlagwörter: TRANSFORMER models, MACHINE learning, SUPERVISED learning, DATA scrubbing, SUPPORT vector machines, DEEP learning, NATURAL language processing

Cyberbullying Text Identification based on Deep Learning and Transformer-based Language Models

Autoren: Khalid Saifullah Muhammad Ibrahim Khan Suhaima Jamal et al.

Quelle: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, Vol 11, Iss 1 (2024)

Schlagwörter: FOS: Computer and information sciences, Computer engineering. Computer hardware, 0209 industrial biotechnology, Artificial intelligence, Offensive Language, 02 engineering and technology, Cybercrime and Dark Web Activities, NLP, Online Harassment, Cyberbullying, Systems engineering, TK7885-7895, TA168, Identification (biology), Characterization and Detection of Android Malware, Engineering, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Automated Detection of Hate Speech and Offensive Language, transformers models, natural language processing, Biology, Transformer, large language modeling, Natural language processing, 4. Education, Botany, deep learning, Deep learning, Voltage, 16. Peace & justice, Computer science, Electrical engineering, Computer Science, Physical Sciences, Signal Processing, Information Systems

Zugangs-URL: https://doaj.org/article/8fa7c8b0dd03480c8c9af5a364287c1d

Detection of Hate Speech and Offensive Language in Arabic Text: A Systematic Literature Review.

Autoren: Alshahrani, Eman S. Aksoy, Mehmet S. Emam, Ahmed et al.

Quelle: Applied Computational Intelligence & Soft Computing; 4/13/2025, Vol. 2025, p1-32, 32p

Schlagwörter: NATURAL language processing, INTERNET content, HATE speech, ARTIFICIAL intelligence, DEEP learning, USER-generated content, SOCIAL media

Composite feature fusion for improved offensive language detection in Tamil social media using MHA-LSTM.

Autoren: Kalaivani, A. Thenmozhi, D.

Quelle: International Journal of Machine Learning & Cybernetics; Oct2025, Vol. 16 Issue 10, p8145-8161, 17p

Offensive language detection in low resource languages: A use case of Persian language.

Autoren: Mozafari, Marzieh Mnassri, Khouloud Farahbakhsh, Reza et al.

Quelle: PLoS ONE; 6/21/2024, Vol. 19 Issue 6, p1-30, 30p

Schlagwörter: PERSIAN language, LANGUAGE models, SOCIAL media, RESOURCE-limited settings, DEEP learning, TRANSFORMER models, USER-generated content

Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook.

Autoren: Naseeb, Amna Zain, Muhammad Hussain, Nisar et al.

Quelle: Algorithms; Jun2025, Vol. 18 Issue 6, p331, 18p

Schlagwörter: SOCIAL media, INTERNET content moderation, MACHINE learning, SUPPORT vector machines, HATE speech, DEEP learning

Beyond words: a hybrid transformer-ensemble approach for detecting hate speech and offensive language on social media.

Autoren: Iftikhar, Uzair Ali, Syed Farooq Mustafa, Ghulam et al.

Quelle: PeerJ Computer Science; Oct2025, p1-31, 31p

Schlagwörter: HATE speech, SOCIAL media, TRANSFORMER models, INVECTIVE, BOOSTING algorithms, LANGUAGE models, ENSEMBLE learning

Reviews & Products: TWITTER (Web resource)

A Multi-Architecture Approach for Offensive Language Identification Combining Classical Natural Language Processing and BERT-Variant Models.

Autoren: Yadav, Ashok Khan, Farrukh Aslam Singh, Vrijendra

Quelle: Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 23, p11206, 27p

Schlagwörter: MACHINE learning, CONVOLUTIONAL neural networks, NATURAL language processing, TRANSFORMER models, SUPPORT vector machines

SOLD: Sinhala offensive language dataset: SOLD: Sinhala offensive language dataset: T. Ranasinghe et al.

Autoren: Ranasinghe, Tharindu Anuradha, Isuri Premasiri, Damith et al.

Quelle: Language Resources & Evaluation; Mar2025, Vol. 59 Issue 1, p297-337, 41p

Schlagwörter: LOW-resource languages, NATURAL language processing, MACHINE learning, ARTIFICIAL intelligence, INTERNET content

Chinese Cyberbullying Detection Using XLNet and Deep Bi-LSTM Hybrid Model.

Autoren: Chen, Shifeng Wang, Jialin He, Ketai

Quelle: Information; Feb2024, Vol. 15 Issue 2, p93, 18p

Schlagwörter: DEEP learning, LANGUAGE models, NATURAL language processing, INFORMATION technology, SOCIAL media, CYBERBULLYING

Turkish Cyberbullying Detection with Fine-Tuned Pre-Trained Language Models.

Alternate Title: İnce Ayar Yapılmış Ön Eğitimli Dil Modelleri ile Türkçe Siber Zorbalık Tespiti. (Turkish)

Autoren: BİLGİN, Metin BEKAR, Bilge Nur

Quelle: International Journal of InformaticsTechnologies; Apr2025, Vol. 18 Issue 2, p115-127, 13p

Schlagwörter: NATURAL language processing, LANGUAGE models, INFORMATION & communication technologies, CYBERBULLYING, TURKISH language

Pashto offensive language detection: a benchmark dataset and monolingual Pashto BERT.

Autoren: Ijazul Haq Weidong Qiu Jie Guo et al.

Quelle: PeerJ Computer Science; Oct2023, p1-26, 26p

Schlagwörter: DEEP learning, SOCIAL media, LANGUAGE models, ONLINE social networks, CONVOLUTIONAL neural networks, RESOURCE-limited settings

Firma/Körperschaft: X Corp.

Artificial Intelligence-Driven Cyberbullying Detection: A Survey of Current Techniques.

Autoren: Alfaleh, Kholood Alabdultif, Abdulatif Aladhadh, Suliman

Quelle: Journal of Information Technology Management (JITM); 2024, Vol. 16 Issue 4, p38-63, 26p

Schlagwörter: INTERNET content, ARTIFICIAL intelligence, INVECTIVE, CYBERBULLYING, SOCIAL media, DEEP learning

Offensive Text Span Detection in Romanian Comments Using Large Language Models.

Autoren: Paraschiv, Andrei Ion, Teodora Andreea Dascalu, Mihai

Quelle: Information; Jan2024, Vol. 15 Issue 1, p8, 21p

Schlagwörter: LANGUAGE models, NATURAL language processing, SOCIAL media, ROMANIAN language, TRANSFORMER models, DEEP learning, TEXT recognition

A Unified Framework for Analyzing Textual Context and Intent in Social Media.

Autoren: Prakash, V. Jothi Vijay, S. Arul Antran

Quelle: ACM Transactions on Intelligent Systems & Technology; Dec2024, Vol. 15 Issue 6, p1-25, 25p

Schlagwörter: WILCOXON signed-rank test, EMOTION recognition, HATE speech, CROSS-stitch, STATISTICAL significance

Multilingual Hate Speech Identification in Hindi, English and Marathi with Deep Learning Techniques.

Autoren: K., Brahmanaidu B., Vishnuvardhan

Quelle: Frontiers in Health Informatics; 2024, Vol. 13 Issue 5, p106-115, 10p

Schlagwörter: SOCIAL media, HATE speech, DEEP learning, ENGLISH language, LINGUISTIC context