{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-transfer-learning-baselines-for","title":"Deep Transfer Learning Baselines for Sentiment Analysis in Russian","arxiv_id":null,"date":"2021-07-01","proceeding":null,"authors":["Sergey Smetanin","Mikhail Komarov"],"abstract":"Recently, transfer learning from pre-trained language models has proven to be effective in a variety of natural language processing tasks, including sentiment analysis. This paper aims at identifying deep transfer learning baselines for sentiment analysis in Russian. Firstly, we identified the most used publicly available sentiment analysis datasets in Russian and recent language models which officially support the Russian language. Secondly, we fine-tuned Multilingual Bidirectional Encoder Representations from Transformers (BERT), RuBERT, and two versions of the Multilingual Universal Sentence Encoder and obtained strong, or even new, state-of-the-art results on seven sentiment datasets in Russian: SentRuEval-2016, SentiRuEval-2015, RuTweetCorp, RuSentiment, LINIS Crowd, and Kaggle Russian News Dataset, and RuReviews. Lastly, we made fine-tuned models publicly available for the research community.","url_abs":"https://www.sciencedirect.com/science/article/abs/pii/S0306457320309730?dgcid=author","url_pdf":"https://www.sciencedirect.com/science/article/abs/pii/S0306457320309730?dgcid=author","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-transfer-learning-baselines-for","repo_url":"https://github.com/sismetanin/sentiment-analysis-in-russian","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-rusentiment","task":"Sentiment Analysis","dataset":"RuSentiment","model":"RuBERT-RuSentiment","rank_in_archive_order":1,"of":3,"metrics":{"Weighted F1":"75.71"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}