For this subtask, we use the exact same three methods.
 Table \ref{tab3} shows that according to macro-F the best model is the voting Ensemble Classifier with an F-score (POS and NEG) of 65.06\%. Surprisingly, the character-level method performed much better on the test than on the dev set and achieved better resultts than the ensemble method on the test set.

\begin{table*}[ht]
\input{tables/result_run2-table.tex}
\caption{\label{tab3} Results of our three methods for Subtask2 (Sentiment) in different training configurations (in blue the best scores, in bold the results of the submitted system, the main metric is in red)}
\end{table*}

The confusion matrix on the dev set is presented in Figure \ref{fig_mtx_sentiment} shows that positive tweets were harder to predict with only 230 True Positives, 90 False Positives and 306 False Negatives.
 Recall has been issue in all the experiments we performed.

\begin{figure}[!h]
\begin{center}
\includegraphics[width=9cm]{figures/matice_confusion_m_ensemble_sentiment.png}
\end{center}
\caption{\label{fig_mtx_sentiment} Confusion Matrix on the Sentiment dev set for the ensemble method}
\end{figure}
 
 %we see that 229 Neutral tweets and 129 Positive tweets were classified as Negative (358 False Positives in total). It shows that the negative class   and 272 FN), 177 Positive tweets and 240 Negative tweets were classified as Neutral tweets(417 FP and 287 FN) and 58 Neutral tweets and 32 Negative tweets were classified as Positive tweets (90 FP and 306 FN).     

%According to the classification report which is produced from the dev set in Table \ref{tab4}, we see that the Precision is high in Negative, Neutral and Positive class. But, Recall and F1-score both are high in Negative, Neutral class and not in Positive class (43.28\% and 54.02\% respectively). 
%Finally, we think that, the low rate Recall for Positive class is due to the imbalance train data (2200 tweets). 

\begin{table}[!h]
%\input{tables/classification_report.tex}
\input{tables/classification_report_reversed.tex}
\caption{\label{tab4}Classification report for the Voting Classifier on the Dev set for Subtask2 (acc. : 71.19\%)}
\end{table}
