RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian

PeerJ Computer Science. 2022. No. 8. Article e1039.

Smetanin S.

The Russian language is still not as well resourced as English, especially in the field of sentiment analysis of Twitter content. Though several sentiment analysis datasets of tweets in Russia exist, they all are either automatically annotated or manually annotated by one annotator. Thus, there is no inter-annotator agreement, or annotation may be focused on a specific domain. In this paper, we present RuSentiTweet, a new sentiment analysis dataset of general domain tweets in Russian. RuSentiTweet is currently the largest in its class for Russian, with 13,392 tweets manually annotated with moderate inter-rater agreement into 5 classes: Positive, Neutral, Negative, Speech Act, and Skip. As a source of data, we used Twitter Stream Grab, a historical collection of tweets obtained from the general Twitter API stream, which provides a 1% sample of the public tweets. Additionally, we released a RuBERT-based sentiment classification model that achieved F1=0.6594 on the test subset.

Keywords: twitter, natural language processing, sentiment analysis, sentiment analysis, classification, twitter, классификация текстов, анализ тональности, public datasets, Applied Data Science, Natural Language Processing (NLP), Social Network Sentiment Analysis, Dataset