# Towards Reliable Baselines for Document-Level Sentiment Analysis in the Czech and Slovak Languages
## 3 Summary of Relevant Experiments and Results Reported in [50]  
[PERSON] et al. [50] first created an entirely new Facebook dataset consisting of around 10,000 Czech posts, which they manually assigned into three main classes, positive, negative, and neutral. (There was also a small \"bipolar\" class, which was excluded from further experiments.) The authors also compiled two additional datasets, one consisting of 91,381 movie reviews from the Czech-Slovak Movie Database csfd.cz, the other consisting of 145,307 product reviews from a large Czech e-shop Mall.cz. Since reviews from these two additional sources were accompanied by star ratings, these were used for assigning the reviews into three classes: positive, negative, and neutral. We show selected examples from these three datasets in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Text**} & _Approximate English Translation (Not_ \\  & & _Part of the Dataset_ \\ \hline \hline Negative & ani náhodou... & _not even by chance_... \\ \hline Negative & ty saty kdo jim navrhnul byl asi vózralej & _The designer of their clothes must have been drunk_ \\ \hline Positive & Mám ji je skvěálí & _I have it it’s great!_ \\ \hline Positive & moje nejöbliben  
[PERSON] et al. [50] then used these three datasets to evaluate the utility of various combinations of data pre-processing techniques, types of features and feature selection methods for sentiment analysis in Czech (see Table 2 for a summary of their considered methods and approaches). In their experiments, they relied on Maximum Entropy (MaxEnt) and Support Vector Machines (SVM) as the two most promising algorithms identified in the published literature. For each dataset, they performed both a binary classification into the \"positive\" and \"negative\" classes, and a ternary classification, which also included the \"neutral\" class. In most cases, the Maximum Entropy algorithm outperformed the SVM. In the results reported below, we focus only on ternary classification with the Maximum Entropy algorithm, which was directly relevant to our own research work. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Text** & _Approximate English Translation (Not_ _Part of the Dataset_) \\ \hline \hline \multicolumn{3}{c}{**Movie reviews from csfd.cz**} \\ \hline \hline Negative & tak toto se opravdvn nepovedlo & so this really did not work out well \\ \hline Negative & Moc, ale moc spatny... & _Really, but really bad..._ \\ \hline Positive & Jednoduse geniàlní. & _Simply genius._ \\ \hline Positive & Film meho détstvi. Super. & _The film of my childhood. Super._ \\ \hline Neutral &...a púl hvězdicky [PERSON]... & _... and a half-star to [PERSON]..._ \\ \hline Neutral & Film o nicem... s dobrymi herci, & _Filn about nothing... with good actors,_ _but about nothing. !_ \\  & ale o nicem.! & **Product reviews from** **Malice**. \\ \hline \hline Negative & vadi mi, ze intenzata vúne b rzy vyprchá. & _it bothers me that the intensity of the fragrance evaporates soon._ \\ \hline Negative & Cékala jsem od tohoto výrobku vice. & _I expected more from this product._ \\ \hline Positive & splnilo ocekáváani, vyborny na cesty & _fulfilled expectations, excellent for travel_ \\ \hline Positive & Skvěj pomcnik při udrzbě pracky. & _Excellent helper for washing_ \\ \hline Neutral & Je hlucněš  
Since some of the pre-processing techniques are mutually exclusive (e.g., stemming and lemmatization), [PERSON] et al. [50] only evaluated their meaningful combinations. Moreover, all their pre-processing pipelines used tokenizing, part-of-speech (POS) Tagging, and stopword removal, and were thus distinguished by the presence or absence of the remaining pre-processing steps. (It also turned out that Named Entity Filtering never helped, so the \"winning\" pipelines never used it.) All the resulting data pre-processing pipelines were then combined with various feature types to determine the optimum configuration for each of the three datasets (see Section 3.1 for the results).