ArticlePeerJ. Computer science2022
RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian.
Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 8 citations in OpenAlex.
- ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Special issue on analysis and mining of social media data.PeerJ. Computer science · 2024Article
- The voice of Twitter: observable subjective well-being inferred from tweets in Russian.PeerJ. Computer science · 2022Article
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Authors and funding
1 author at 1 institution in 1 country.
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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
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.