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| Model | Score | Rank | Dataset | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SentiRuEval-2016 | RuSentiment | KRND | LINIS Crowd | RuTweetCorp | RuReviews | |||||||||
| TC | Banks | |||||||||||||
| micro F1 | macro F1 | F1 | micro F1 | macro F1 | F1 | wighted | F1 | F1 | F1 | F1 | F1 | |||
| SOTA | n/s | 76.71 | 66.40 | 70.68 | 67.51 | 69.53 | 74.06 | 78.50 | n/s | 73.63 | 60.51 | 83.68 | 77.44 | |
| XLM-RoBERTa-Large | 76.37 | 1 | 82.26 | 76.36 | 79.42 | 76.35 | 76.08 | 80.89 | 78.31 | 75.27 | 75.17 | 60.03 | 88.91 | 78.81 |
| SBERT-Large | 75.43 | 2 | 78.40 | 71.36 | 75.14 | 72.39 | 71.87 | 77.72 | 78.58 | 75.85 | 74.20 | 60.64 | 88.66 | 77.41 |
| MBARTRuSumGazeta | 74.70 | 3 | 76.06 | 68.95 | 73.04 | 72.34 | 71.93 | 77.83 | 76.71 | 73.56 | 74.18 | 60.54 | 87.22 | 77.51 |
| Conversational RuBERT | 74.44 | 4 | 76.69 | 69.09 | 73.11 | 69.44 | 68.68 | 75.56 | 77.31 | 74.40 | 73.10 | 59.95 | 87.86 | 77.78 |
| LaBSE | 74.11 | 5 | 77.00 | 69.19 | 73.55 | 70.34 | 69.83 | 76.38 | 74.94 | 70.84 | 73.20 | 59.52 | 87.89 | 78.47 |
| XLM-RoBERTa-Base | 73.60 | 6 | 76.35 | 69.37 | 73.42 | 68.45 | 67.45 | 74.05 | 74.26 | 70.44 | 71.40 | 60.19 | 87.90 | 78.28 |
| RuBERT | 73.45 | 7 | 74.03 | 66.14 | 70.75 | 66.46 | 66.40 | 73.37 | 75.49 | 71.86 | 72.15 | 60.55 | 86.99 | 77.41 |
| MBART-50-Large-Many-to-Many | 73.15 | 8 | 75.38 | 67.81 | 72.26 | 67.13 | 66.97 | 73.85 | 74.78 | 70.98 | 71.98 | 59.20 | 87.05 | 77.24 |
| SlavicBERT | 71.96 | 9 | 71.45 | 63.03 | 68.44 | 64.32 | 63.99 | 71.31 | 72.13 | 67.57 | 72.54 | 58.70 | 86.43 | 77.16 |
| EnRuDR-BERT | 71.51 | 10 | 72.56 | 64.74 | 69.07 | 61.44 | 60.21 | 68.34 | 74.19 | 69.94 | 69.33 | 56.55 | 87.12 | 77.95 |
| RuDR-BERT | 71.14 | 11 | 72.79 | 64.23 | 68.36 | 61.86 | 60.92 | 68.48 | 74.65 | 70.63 | 68.74 | 54.45 | 87.04 | 77.91 |
| MBART-50-Large | 69.46 | 12 | 70.91 | 62.67 | 67.24 | 61.12 | 60.25 | 68.41 | 72.88 | 68.63 | 70.52 | 46.39 | 86.48 | 77.52 |
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}@INPROCEEDINGS{Smetanin2019Sentiment,
author={Sergey Smetanin and Michail Komarov},
booktitle={2019 IEEE 21st Conference on Business Informatics (CBI)},
title={Sentiment Analysis of Product Reviews in Russian using Convolutional Neural Networks},
year={2019},
volume={01},
pages={482-486},
doi={10.1109/CBI.2019.00062},
ISSN={2378-1963},
month={July}
}