Título da Dissertação
Tabela 5.4: Performance results with 100 sized vectors for BoE
['[BOLD] Classifier', '[BOLD] Features', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-score']
According to the evaluation metrics we conclude that the bag-of-word and bag-of-embeddings combined produced better classification models. The model produced by the Linear SVM performed slightly better than the LR and the RF. Interesting to note is that \glsBoW features have influence on the precision scores obtained from our results, producing more conservative classifiers. Regarding the recall results, we can see that the Logistic Regression using only bag-of-embeddings features was the model with best results; perhaps if the precision is taken into consideration, the same conclusions will not be possible. It is worth noting that combining Bag-of-words and Bag-of-embedding with size 100 was the group of features with best performance taking into consideration the evaluation metrics used in this experiment. After the selection of our classification model, we decided to classify all the Portuguese dataset and draw some statistics from the results. From a total of 7.8M tweets, our classifier was able identified 37,300 travel-related entries.