Sentiment Analysis of Czech Texts: An Algorithmic Survey
Table 6: Top grid-search and test scores for each algorithm
['[BOLD] Algorithm', '[BOLD] Facebook Mall  [BOLD] GS Acc', '[BOLD] Facebook Mall  [BOLD] Test Acc', '[BOLD] Facebook Mall  [BOLD] Test F1', '[BOLD] Facebook Mall  [BOLD] GS Acc', '[BOLD] Facebook Mall  [BOLD] Test Acc', '[BOLD] Facebook Mall  [BOLD] Test F1']
We used the best performing vectorizer and classifier parameters to assess the classification performance of each algorithm in both datasets. For lower variance, the average of five measures is reported. The top scores on the two datasets differ a lot. That is because Facebook data classification is a multiclass discrimination problem (negative vs. neutral vs. positive), in contrast with Mall review analysis which is purely binary (negative vs. positive). As we can see, Logistic Regression and SVM are the top performers in Facebook data. NuSVM and Naïve Bayes perform slightly worse. MLP and Random Forest, on the other hand, fall discretely behind. On the Mall dataset, SVM is dominant in both accuracy and F1. It is followed by Logistic Regression, Naïve Bayes and NuSVM. Maximum Entropy is near whereas MLP and Random Forest are again considerably weaker. From the top three algorithms, Naïve Bayes was the fastest to train, followed by Logistic Regression. SVM was instead considerably slower. They used Naïve Bayes with α=0.005 and 5-fold cross-validation, same as we did here. Unfortunately, no other direct comparisons with similar studies are possible.