Arabic language is one of the most widely spoken language in the world, currently considered as the fifth language \cite{Chung2008} with more than 330 million speakers. 
It is the official language in more than 22 countries and is therefore an important language to handle for NLP systems.
 Its written form is commonly referred as Literary Arabic and divided into two categories:  Classical Arabic and Modern Standard Arabic (MSA). But Arabic has in fact many variants and research effort have been made on tasks like Arabic Dialects Identification\cite{abdul-mageed-etal-2020-nadi}.
More fine-grained tasks have been tackled by the NLP community and this paper describes our participation to such a task : the Shared Task on Sarcasm and Sentiment Detection in Arabic\cite{abufarha-etal-2021-arsarcasm-v2}. This shared task focuses on analysing tweets and identifying the sentiment (negative, positive or neutral) and whether a tweet is sarcastic or not. More precisely, the task are defined as follows:

\begin{itemize}
\item \textbf{Subtask 1:} (Sarcasm Detection): Identifying whether a tweet is sarcastic or not, this is a binary classification task.
\item \textbf{Substask 2:} (Sentiment Analysis): Identifying the sentiment of a tweet and assigning one of three labels (Positive, Negative, Neutral), multiclass classification task.
\end{itemize}

%Our submission to this campaign is dealing with the two subtasks. 
The paper is organized as follows: Section \ref{sec:data}, describes the dataset and Section \ref{sec:sys} is devoted to the methods we developed. Section \ref{sec:resu} details our results, future directions are given in Section \ref{sec:conc}.   
