
 We use the method developed for the 2020 NADI challenge \cite{ghoul:hal-03089957}. To build and train the model, we used the \textsc{FeatureUnion} in \textsc {Scikit-Learn} \cite{scikit-learn} which allows to combine easily different n-gram representations at the word level and the character level as shown in Figure \ref{fig_run1}. To train this model,we concatenate three vectors with the following features (weigthed with TF-IDF): word n-grams (1 to 5-grams), character n-grams (1 to 4-grams) and character n-grams inside words (1 to 5-grams).

 \begin{figure}[ht]
\centering 
\includegraphics[width=8cm]{figures/run1.png}
\caption{\label{fig_run1} Model of the Ensemble Classifier}
\end{figure} 

In this method, we use a set of simple classifiers to build an ensemble voting classifier that uses predicted class labels for majority rule voting. This ensemble is a combination of the following classifiers (\texttt{pen} stands for \texttt{penalty}):

\begin{itemize}
    \item SGDClassifier ($\alpha = 10^{-5}$, \texttt{pen}=`l2')
    \item LinearSVC (\texttt{pen}=`l2', Tolerance =$10^{-3}$%for stopping set to 0.001
\item Multinomial Naive Bayes ($\alpha = 10^{-2}$)
\item Ridge Classifier ($\alpha = 1$)
\end{itemize}

%The results for the dev  and test set without pre-processing of sarcasm detection subtask are reported in Table \ref{tab2} and the results of Sentiment Analysis subtask are reported in Table \ref{tab3}.
%TODO: ajouter une phrase de description des résultats
