This paper describes our systems submitted to the Second Nuanced Arabic Dialect Identification Shared Task (NADI 2021). Dialect identification is the task of automatically detecting the source variety of a given text or speech segment. There are four subtasks, two subtasks for country-level identification and the other two subtasks for province-level identification. The data in this task covers a total of 100 provinces from all 21 Arab countries and come from the Twitter domain. The proposed systems depend on five machine-learning approaches namely Complement Naïve Bayes, Support Vector Machine, Decision Tree, Logistic Regression and Random Forest Classifiers. F1 macro-averaged score of Naïve Bayes classifier outperformed all other classifiers for development and test data.

Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in the field of natural language processing. Hence, sarcasm and sentiment analysis has gained a major interest from the research community, especially in this language. Automatic sarcasm detection and sentiment analysis can be applied using three approaches, namely supervised, unsupervised and hybrid approach. In this paper, a model based on a supervised machine learning algorithm called Support Vector Machine (SVM) has been used for this process. The proposed model has been evaluated using ArSarcasm-v2 dataset. The performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task.

In this paper, we present the system submitted to “SemEval-2020 Task 12”. The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to design our system. We implemented a linear classifier with Stochastic Gradient Descent (SGD) as optimization algorithm. Our model reported 84.20%, 81.82% f1-score on development set and test set respectively. The best performed system and the system in the last rank reported 90.17% and 44.51% f1-score on test set respectively.