# Towards Sentiment Analysis for Romanian Twitter Content
## 4 Experiments and Results
### Constructing The Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Encoding**} & \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**English Original Dataset**} & \multicolumn{5}{c}{**Romanian Translated Dataset**} \\ \cline{3-7}  & & & **Only PP** & **PP + PCA** & **PP + NMF** & **PP + LSA** \\ \hline \multirow{5}{*}{TF-IDF} & Bernoulli NB & 0.7737, 0.7725 & 0.7820, 0.7820 & 0.6778, 0.6249 & 0.7293, 0.7222 & 0.6701, 0.6077 \\  & SVM & 0.7741, 0.7667 & 0.7836, 0.7747 & 0.7620, 0.7471 & 0.7316, 0.6988 & 0.7690, 0.7565 \\ \cline{1-1}  & RF & 0.6670, 0.5521 & 0.6520, 0.5471 & 0.6366, 0.5716 & 0.6541, 0.5517 & 0.6410, 0.5787 \\ \cline{1-1}  & LR & 0.7745, 0.7590 & 0.7781, 0.7645 & 0.7573, 0.7347 & 0.7499, 0.7388 & 0.7600, 0.7430 \\ \cline{1-1}  & DNN & 0.7818, 0.7715 & 0.7720, 0.7623 & 0.7658, 0.7575 & 0.7502, 0.7307 & 0.7636, 0.7507 \\ \hline \multirow{2}{*}{Word2 Vec} & CNN & 0.7821, 0.7666 & 0.7769, 0.7600 & & & \\  & LTSM & 0.7750, 0.7635 & 0.7817, 0.7798 & & & \\ \hline \multirow{5}{*}{Doc2 Vec} & Bernoulli NB & 0.6244, 0.4801 & 0.6242, 0.4798 & & & \\  & SVM & 0.6305, 0.4801 & 0.6267, 0.4797 & & & \\ \cline{1-1}  & RF & 0.6244, 0.4790 & 0.6242, 0.4779 & & & \\ \cline{1-1}  & LR & 0.6275, 0.4800 & 0.6252, 0.4797 & & & \\ \cline{1-1}  & DNN & 0.6290, 0.4801 & 0.6273, 0.4798 & & & \\ \hline \multicolumn{2}{c}{Multilingual BERT} & 0.8302, 0.8257 & 0.8099, 0.8051 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classifier performance (accuracy and weighted F1-measure) with or without dimensionality reduction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Encoding**} & \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**English Original Dataset**} & \multicolumn{5}{c}{**Romanian Translated Dataset**} \\ \cline{3-7}  & & & **Only PP** & **PP + PCA** & **PP + NMF** & **PP + LSA** \\ \hline \multirow{5}{*}{TF-IDF} & Bernoulli NB & 1337, 0.285 & 1645, 0.368 & 387, 0.064 & 362, 0.051 & 385, 0.062 \\  & SVM & 920, 0.360 & 1048, 0.238 & 2038, 2.418 & 412, 0.149 & 2724, 2.353 \\ \cline{1-1}  & RF & 5735, 1.590 & 6158, 0.568 & 375, 0.295 & 449, 0.404 & 659, 0.905 \\ \cline{1-1}  & LR & 89603, 8.502 & 3588, 7.855 & 2245, 4.792 & 5125, 16.644 & 204, 0.693 \\ \cline{1-1}  & DNN & 11,513, 2.176 & 13,551, 2.230 & 3558, 1.015 & 9236, 5.280 & 6585, 2.567 \\ \hline \multirow{2}{*}{Word2 Vec} & CNN & 9143, 1.460 & 16,127, 4.209 & & & \\ \cline{1-1}  & LTSM & 17,172, 16.865 & 62,364, 20.926 & & & \\ \hline \multirow{5}{*}{Doc2 Vec} & Bernoulli NB & 271, 0.028 & 274, 0.021 & & & \\ \cline{1-1}  & SVM & 654, 0.190 & 580, 0.152 & & & \\ \cline{1-1}  & RF & 712, 1.171 & 263, 0.523 & & & \\ \cline{1-1}  & LR & 484, 1.595 & 428, 0.936 & & & \\ \cline{1-1}  & DNN & 17,842, 0.912 & 11,529, 4.594 & & & \\ \hline \multicolumn{2}{c}{Multilingual BERT a  a} & 416 & 444 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter optimization and final model learning time (seconds). searching for the best model parameters indeed is very time consuming. In the case of the models trained in Romanian, optimizing the Bernoulli NB on the dataset without dimensionality reduction took about 27 min. Searching for the best structure and capacity of the DNN took about 3 h and 45 min. Searching for best parameters for LTSM took more than 17 h, and we shall note that vector embedding size is only 200. Therefore, learning an LSTM proved to be a very prohibitive experiment in the absence of a well-equipped computing machine. As expected, BERT provides with state-of-the-art results for both the English and the Romanian datasets. The gap from our best result to BERT is bigger for English compared with Romanian. However, all come with a high computational cost, as learning just one BERT-based classifier takes about 7 to 8 min. This makes the hyperparameter optimization infeasible. If we would consider the specification of the Genetic Algorithm presented in Section 3.6, this would result in the worst case at 16,800 models learned, needing about 11 days of running the experiment.