A comparative study of publicly available Russian sentiment lexicons

Abstract

Sentiment lexicons play an important role in the systems of sentiment analysis and opinion mining. The article takes a look into eight publicly available Russian sentiment lexicons of today. A joint analysis of these lexicons was done by finding unions and intersections of the lexicons and also analysing the distribution of parts of speech. In order to study the quality of the lexicons, a sentiment classification is made based on the SVM and the TF-IDF model. Text corpora from reviews of works of art (books and movies), organizations (banks and hotels) and goods (kitchen appliances) are made for this purpose. Lexicons are compared in terms of their classification quality, and also on the basis of a linear regression model that reflects the dependence of their F1-measure on their TF-IDF model size. The resulting union lexicon most fully reflects the sentiment lexica of the present day Russian language and can be used both in scientific research and in applied sentiment analysis systems.