Sentiment Analysis of Czech Texts: An Algorithmic Survey
Table 7: AdaBoost scores for the top three algorithms
['[BOLD] Algorithm', '[BOLD] Facebook Mall  [BOLD] Test Acc', '[BOLD] Facebook Mall  [BOLD] Test F1', '[BOLD] Facebook Mall  [BOLD] Test Acc', '[BOLD] Facebook Mall  [BOLD] Test F1']
AdaBoost is one of the popular mechanisms for reinforcing the prediction capabilities of other algorithms by combining them in a weighted way. It tries to tweak future classifiers based on the wrong predictions of the previous ones and selects only the features known to improve prediction quality. On the negative side, AdaBoost is sensitive to noisy data and outliers which means that it requires careful data preprocessing. First, we experimented with a few estimators in Adaboost and got poor results in both datasets. Increasing the number of estimators increased accuracy and F1 scores until some point (about 5000 estimators) and was further useless. As we can see, no improvements over the top scores of each algorithm were gained. The results we got are actually slightly lower. As a final attempt, we combined SVM, LR, and NB in a majority voting ensemble scheme. Test accuracy and F1 scores on Facebook were 69.5 and 64.2 %, respectively. The corresponding results on Mall dataset were 92.1 and 91.5 %. Again, we see that the results are slightly lower than top scores of the tree algorithms and no improvement was gained on either of the datasets.