# Sentiment Analysis of Czech Texts: An Algorithmic Survey

## TL;DR

This paper surveys machine learning algorithms for sentiment analysis of Czech texts, highlighting the effectiveness of support vector machines and the limited gains from ensemble methods.

## Contribution

It provides an evaluation of various algorithms on Czech social media and review data, including optimal parameters and performance insights.

## Key findings

- Support vector machines outperform other classifiers
- Ensemble schemes do not significantly improve performance
- Optimal parameters are identified for each algorithm

## Abstract

In the area of online communication, commerce and transactions, analyzing sentiment polarity of texts written in various natural languages has become crucial. While there have been a lot of contributions in resources and studies for the English language, "smaller" languages like Czech have not received much attention. In this survey, we explore the effectiveness of many existing machine learning algorithms for sentiment analysis of Czech Facebook posts and product reviews. We report the sets of optimal parameter values for each algorithm and the scores in both datasets. We finally observe that support vector machines are the best classifier and efforts to increase performance even more with bagging, boosting or voting ensemble schemes fail to do so.