Abstract
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 article, 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 five 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.
Reference78 articles.
1. An in-depth experimental comparison of RNTNs and CNNs for sentence modeling;Ahmadi,2017
2. LABR: a large scale Arabic book reviews dataset;Aly,2013
3. A survey of Twitter research: data model, graph structure, sentiment analysis and attacks;Antonakaki;Expert Systems with Applications,2021
4. Assessing the impact of text preprocessing in sentiment analysis of short social network messages in the Russian language;Araslanov,2020
5. Demographic changes are not good for the Russian language;Arefiev;Demoscope Weekly,2013
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献