Aspect-Level Sentiment Analysis in Czech

Abstract

This paper presents a pioneering research on aspect-level sentiment analysis in Czech. The main contribution of the paper is the newly created Czech aspectlevel sentiment corpus, based on data from restaurant reviews. We annotated the corpus with two variants of aspect-level sentiment ‐ aspect terms and aspect categories. The corpus consists of 1,244 sentences and 1,824 annotated aspects and is freely available to the research community. Furthermore, we propose a baseline system based on supervised machine learning. Our system detects the aspect terms with Fmeasure 68.65% and their polarities with accuracy 66.27%. The categories are recognized with F-measure 74.02% and their polarities with accuracy 66.61%.