Search Results for author:
Found 6 papers, 2 papers with code
Benchmarking Transformer-based Language Models for Arabic Sentiment and Sarcasm Detection
The introduction of transformer-based language models has been a revolutionary step for natural language processing (NLP) research.
Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic
This paper provides an overview of the WANLP 2021 shared task on sarcasm and sentiment detection in Arabic.
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic
Most of the participating teams utilised pre-trained language models.
Multitask Learning for Arabic Offensive Language and Hate-Speech Detection
Offensive language and hate-speech are phenomena that spread with the rising popularity of social media.
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset
Our analysis shows the highly subjective nature of these tasks, which is demonstrated by the shift in sentiment labels based on annotators{'} biases.
Mazajak: An Online Arabic Sentiment Analyser
Sentiment analysis (SA) is one of the most useful natural language processing applications.
Ranked #1 on
Sentiment Analysis
on ArSAS
