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\title{Sarcasm and Sentiment Detection in Arabic:\\
Investigating the Interest of Character-level Features }

\author{Dhaou Ghoul \\
  STIH Lab, Sorbonne University, Paris \\
  \texttt{dhaou.ghoul@gmail.com} \\\And
  Gaël Lejeune \\
  STIH Lab, Sorbonne University, Paris  \\
  \texttt{gael.lejeune@paris-sorbonne.fr} \\}

\date{}

\begin{document}

\maketitle

\begin{abstract}
\input{parts/00_abstract.tex}
\end{abstract}


\section{Introduction}
\input{parts/01_introduction.tex}
\section{Data}
\label{sec:data}
\input{parts/03_dataset.tex} 

\section{Methods developed for both tasks}
\label{sec:sys}

We did not developed a specific method for each task, we rather tried to find features that could be useful for both tasks. We first present a simple baseline (Section \ref{sec:run3}) that shows the interest of character-level features. Afterwards we develop more sophisticated methods: a simple Deep Learning approach (Section \ref{sec:run2}) and an ensemble classifier taking advantage of both character-level and word-level features (Section \ref{sec:run1}).

\subsection{Word-based or character-based models ?}
\label{sec:run3}
\input{parts/run3.tex}

\subsection{A deep Learning approach}
\label{sec:run2}
\input{parts/run2.tex}

\subsection{Voting Ensemble Classifier with word and character-level features}  
\label{sec:run1}
\input{parts/run1.tex}




\section{Results and Discussion}
\label{sec:resu}

\subsection{Subtask1: Sarcasm Detection}
\input{parts/result_subtask1.tex}

\subsection{Subtask2: Sentiment Analysis}
\input{parts/result_subtask2.tex}
 
\section{Conclusion}
\label{sec:conc}
\input{parts/conclusion.tex}
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\appendix

 

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