Towards NLP with Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks for Offensive Language Identification in Social Media

2019/03/02 by Andrei-Bogdan Puiu, Puiu, Andrei-Bogdan, Andrei-Octavian Brabete +1 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Spam and Phishing Detection #cs.CL

paper · pdf · doi:10.48550/arxiv.1903.00665

openalex publication_date 2019/03/02 · arxiv created 2019/04/02 · arxiv updated 2019/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This short paper presents the design decisions taken and challenges encountered in completing SemEval Task 6, which poses the problem of identifying and categorizing offensive language in tweets. Our proposed solutions explore Deep Learning techniques, Linear Support Vector classification and Random Forests to identify offensive tweets, to classify offenses as targeted or untargeted and eventually to identify the target subject type.

Citations

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Long Short-Term Memory

Cited by

• SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)

Related

• Combination of multiple Deep Learning architectures for Offensive Language Detection in Tweets
• Identifying and Categorizing Offensive Language in Social Media
• Offensive Language Analysis using Deep Learning Architecture
• Identifying Offensive Posts and Targeted Offense from Twitter
• Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets