The introduction of transformer-based language models has been a revolutionary step for natural language processing (NLP) research. These models, such as BERT, GPT and ELECTRA, led to state-of-the-art performance in many NLP tasks. Most of these models were initially developed for English and other languages followed later. Recently, several Arabic-specific models started emerging. However, there are limited direct comparisons between these models. In this paper, we evaluate the performance of 24 of these models on Arabic sentiment and sarcasm detection. Our results show that the models achieving the best performance are those that are trained on only Arabic data, including dialectal Arabic, and use a larger number of parameters, such as the recently released MARBERT. However, we noticed that AraELECTRA is one of the top performing models while being much more efficient in its computational cost. Finally, the experiments on AraGPT2 variants showed low performance compared to BERT models, which indicates that it might not be suitable for classification tasks.

This paper provides an overview of the WANLP 2021 shared task on sarcasm and sentiment detection in Arabic. The shared task has two subtasks: sarcasm detection (subtask 1) and sentiment analysis (subtask 2). This shared task aims to promote and bring attention to Arabic sarcasm detection, which is crucial to improve the performance in other tasks such as sentiment analysis. The dataset used in this shared task, namely ArSarcasm-v2, consists of 15,548 tweets labelled for sarcasm, sentiment and dialect. We received 27 and 22 submissions for subtasks 1 and 2 respectively. Most of the approaches relied on using and fine-tuning pre-trained language models such as AraBERT and MARBERT. The top achieved results for the sarcasm detection and sentiment analysis tasks were 0.6225 F1-score and 0.748 F1-PN respectively.

Sarcasm is one of the main challenges for sentiment analysis systems. Its complexity comes from the expression of opinion using implicit indirect phrasing. In this paper, we present ArSarcasm, an Arabic sarcasm detection dataset, which was created through the reannotation of available Arabic sentiment analysis datasets. The dataset contains 10,547 tweets, 16% of which are sarcastic. In addition to sarcasm the data was annotated for sentiment and dialects. Our analysis shows the highly subjective nature of these tasks, which is demonstrated by the shift in sentiment labels based on annotators’ biases. Experiments show the degradation of state-of-the-art sentiment analysers when faced with sarcastic content. Finally, we train a deep learning model for sarcasm detection using BiLSTM. The model achieves an F1 score of 0.46, which shows the challenging nature of the task, and should act as a basic baseline for future research on our dataset.

Offensive language and hate-speech are phenomena that spread with the rising popularity of social media. Detecting such content is crucial for understanding and predicting conflicts, understanding polarisation among communities and providing means and tools to filter or block inappropriate content. This paper describes the SMASH team submission to OSACT4’s shared task on hate-speech and offensive language detection, where we explore different approaches to perform these tasks. The experiments cover a variety of approaches that include deep learning, transfer learning and multitask learning. We also explore the utilisation of sentiment information to perform the previous task. Our best model is a multitask learning architecture, based on CNN-BiLSTM, that was trained to detect hate-speech and offensive language and predict sentiment.

Sentiment analysis (SA) is one of the most useful natural language processing applications. Literature is flooding with many papers and systems addressing this task, but most of the work is focused on English. In this paper, we present “Mazajak”, an online system for Arabic SA. The system is based on a deep learning model, which achieves state-of-the-art results on many Arabic dialect datasets including SemEval 2017 and ASTD. The availability of such system should assist various applications and research that rely on sentiment analysis as a tool.