Sentiment Analysis of Czech Texts: An Algorithmic Survey

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.

arXiv comment7 pages, 2 figures, 7 tables. Published in proceedings of the 11th International Conference on Agents and Artificial Intelligence - ICAART 2019 and can be found at http://www.scitepress.org/PublicationsDetail.aspx?ID=1InVq6xKdwE=&t=1 The paper content is identical to the previous one, only updated publication metadata