Sentiment classification of annual reports in Slovak language using symbolic, subsymbolic and statistic methods

Abstract

Background: Sentiment analysis as a tool for recognizing emotions from bank reports can serve to capture the intentions and opinions of corporations, change the emotional context during turbulent periods, and to verify the compliance of the context of the reports with banking conservatism. The purpose of the presented study is to describe the process of creating an own dataset from annual reports in the Slovak language with marked sentiment and the subsequent search for models capable of classifying the sentiment of reports as positive, negative, or neutral.Methods: Our methodology consists of a series of steps necessary for obtaining the text and its subsequent preprocessing to create a dataset. The study further deals with the iterative search for the best combination of hyperparameters for symbolic, subsymbolic, statistic and homogenous symbolic ensemble models in order to find the best classifying model.Results: The experimental results show that the subsymbolic methods are the most suitable for the classifying sentiment of Slovak annual reports. The proposed model can classify sentiment with 98% accuracy.Conclusion: The classification of sentiment in inflectional languages requires particularly precise pre-preparation to give. For the Slovak language, subsymbolic and statistical machine learning methods work best for the sentiment classification of annual reports.