# Towards Sentiment Analysis for Romanian Twitter Content
## 4 Experiments and Results
### Constructing The Models  
As mentioned in Section 3, we applied the processing pipeline described in Figure 1 on the Twitter US Airline Sentiment Tweets ([[https://www.kaggle.com/crowdflower/twitter-airline-sentiment](https://www.kaggle.com/crowdflower/twitter-airline-sentiment)]([https://www.kaggle.com/crowdflower/twitter-airline-sentiment](https://www.kaggle.com/crowdflower/twitter-airline-sentiment)), accessed on 1 September 2022) dataset, which we translated in Romanian with a public web translation service. Sections 3.2-3.6 present all the details regarding every processing step of the pipeline. All the experiments were conducted on a powerful machine with the following specifications: 2 \(\times\) Intel Xeon Gold 6230 CPUs (20 Core at 2.1 GHz), 128 GB DDR4 internal RAM, 8 \(\times\) NVIDIA Tesla V100 32 GB and the source code was implemented in Python 3.9. Table 1 presents the learning performance on the classifiers mentioned in Section 3.5 on the test set, with or without dimensionality reduction. We assess the classification performance with the help of the accuracy and the weighted F1-measure. We also report the performance obtained on the original English data set, applying the same data processing pipeline (without any dimensionality reduction), in order to see how much we lose by the automatic translation to Romanian. We observe that the classification performance of all considered models trained on the original English dataset is very close to the one obtained on the Romanian translation. The differences in all classification schemes are \(\pm\)1.5% which can be considered negligible. We expected this result, as it is in line with similar experiments done with automatic translation for other languages [27; 28; 29]. Furthermore, they confirm the validity of the processing and learning pipeline, as applied on the Romanian translated Twitter data. We note that dimensionality reduction does not bring in an increase in classification accuracy. Furthermore, similar accuracies of around 78% are obtained either with TF-IDF or with Word2 Vec feature extraction, but Doc2 Vec does not help, in any scenario. In terms of accuracy, Bernoulli NB, SVM, LR and DNN applied on TF-IDF encoding and CNN and LTSM applied on Word2 Vec are all almost similar. However, Bernoulli NB scores slightly better on weighted F1-measure, therefore, we are prompted to select Bernoulli NB as the best classifier for TF-IDF encoding. For Word2 Vec encoding, LSTM slightly outperforms CNN in both accuracy and weighted F1-measure. We took advantage of a very powerful machine to run all the experiments. Even so, time spent for hyperparameter optimization and model learning are not negligible. Table 2 lists the time spent for Evolutionary optimization and for learning the final model with the optimal parameter set, for each tested classifier. EO helped us to achieve a 1-3% improvement for the weighted F1-measure. In Table 2, we can also observe that in the majority of cases, the hyperparameter optimization process and the final model training times are higher for the Romanian classifiers. As stated in Section 3.3, Romanian is more verbose than English, thus, the number of tokens learned for Romanian is larger. This fact might have contributed to the generation of more complex models when compared to English. We shall note that, in general, learning a classical ML model takes less than a second and this is clearly less than the learning time required for a deep learning model, but