Adapting sentiment analysis resources and methods to the realm of the Polish language

We begin with harvesting two corpora in Polish from the domains of mobile phone and book reviews, propose a simple automatic annotation method, and manually evaluate the annotation accuracy on a sample. Then, we compare methods of sentiment lexicon acquisition by automatic translation of similar English resources. We also propose a sentiment reversal model for opinion words and evaluate its usefulness using a basic rulebased classifier on a subset of corpus. Finally, we evaluate the influence of different text pre-processing methods and feature sets on the accuracy of sentiment classification in Polish, and show how the baseline bag-of-words model can be improved by incorporation of semantic features based on the harvested sentiment lexicon and the Polish WordNet.
Our endeavor delivers a reference and a new suite of tools that enables sentiment analysis also for Polish.