In sentiment analysis, detecting irony is considered a major challenge. The key problem with detecting irony is the difficulty to recognize the implicit and indirect phrases which signifies the opposite meaning. In this paper, we present Sa‘7r ساخرthe Saudi irony dataset, and describe our efforts in constructing it. The dataset was collected using Twitter API and it consists of 19,810 tweets, 8,089 of them are labeled as ironic tweets. We trained several models for irony detection task using machine learning models and deep learning models. The machine learning models include: K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB). While the deep learning models include BiLSTM and AraBERT. The detection results show that among the tested machine learning models, the SVM outperformed other classifiers with an accuracy of 0.68. On the other hand, the deep learning models achieved an accuracy of 0.66 in the BiLSTM model and 0.71 in the AraBERT model. Thus, the AraBERT model achieved the most accurate result in detecting irony phrases in Saudi Dialect.

This paper provides an overview of the shard task on detecting offensive language, hate speech, and fine-grained hate speech at the fifth workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5). The shared task comprised of three subtasks; Subtask A, involving the detection of offensive language, which contains socially unacceptable or impolite content including any kind of explicit or implicit insults or attacks against individuals or groups; Subtask B, involving the detection of hate speech, which contains offensive language targeting individuals or groups based on common characteristics such as race, religion, gender, etc.; and Subtask C, involving the detection of the fine-grained type of hate speech which takes one value from the following types: (i) race/ethnicity/nationality, (ii) religion/belief, (iii) ideology, (iv) disability/disease, (v) social class, and (vi) gender. In total, 40 teams signed up to participate in Subtask A, and 17 of them submitted test runs. For Subtask B, 26 teams signed up to participate and 12 of them submitted runs. And for Subtask C, 23 teams signed up to participate and 10 of them submitted runs. 10 teams submitted papers describing their participation in one subtask or more, and 8 papers were accepted. We present and analyze all submissions in this paper.

In this paper, we describe our efforts on the shared task of sarcasm and sentiment detection in Arabic (Abu Farha et al., 2021). The shared task consists of two sub-tasks: Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). Our experiments were based on fine-tuning seven BERT-based models with data augmentation to solve the imbalanced data problem. For both tasks, the MARBERT BERT-based model with data augmentation outperformed other models with an increase of the F-score by 15% for both tasks which shows the effectiveness of our approach.

With the rise of hate speech phenomena in Twittersphere, significant research efforts have been undertaken to provide automatic solutions for detecting hate speech, varying from simple ma-chine learning models to more complex deep neural network models. Despite that, research works investigating hate speech problem in Arabic are still limited. This paper, therefore, aims to investigate several neural network models based on Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN) to detect hate speech in Arabic tweets. It also evaluates the recent language representation model BERT on the task of Arabic hate speech detection. To conduct our experiments, we firstly built a new hate speech dataset that contains 9,316 annotated tweets. Then, we conducted a set of experiments on two datasets to evaluate four models: CNN, GRU, CNN+GRU and BERT. Our experimental results on our dataset and an out-domain dataset show that CNN model gives the best performance with an F1-score of 0.79 and AUROC of 0.89.

This paper provides an overview of the offensive language detection shared task at the 4th workshop on Open-Source Arabic Corpora and Processing Tools (OSACT4). There were two subtasks, namely: Subtask A, involving the detection of offensive language, which contains unacceptable or vulgar content in addition to any kind of explicit or implicit insults or attacks against individuals or groups; and Subtask B, involving the detection of hate speech, which contains insults or threats targeting a group based on their nationality, ethnicity, race, gender, political or sport affiliation, religious belief, or other common characteristics. In total, 40 teams signed up to participate in Subtask A, and 14 of them submitted test runs. For Subtask B, 33 teams signed up to participate and 13 of them submitted runs. We present and analyze all submissions in this paper.

This paper introduces MADAD, a general-purpose annotation tool for Arabic text with focus on readability annotation. This tool will help in overcoming the problem of lack of Arabic readability training data by providing an online environment to collect readability assessments on various kinds of corpora. Also the tool supports a broad range of annotation tasks for various linguistic and semantic phenomena by allowing users to create their customized annotation schemes. MADAD is a web-based tool, accessible through any web browser; the main features that distinguish MADAD are its flexibility, portability, customizability and its bilingual interface (Arabic/English).