Resources and Experiments on Sentiment Classification for Georgian.

This paper presents, to the best of our knowledge, the first ever publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. We describe the characteristics of these resources and the process of their creation in detail. We also report the results of various experiments on the performance of both lexicon- and machine learning-based models for Georgian sentiment classification. We consider both three- (positive, neutral, negative) and four-tier (positive, neutral, negative, mixed) classifications. The machine learning models explored include, logistic regression, support vector machines (SVMs), and transformer-based models. We also explore approaches based on transfer learning and translation (into a well-supported language). The results obtained for Georgian are on a par with state-of-the-art results in sentiment classification for well studied languages when using training data of comparable size.