# Transfer Learning for Improving Results on Russian Sentiment Datasets

arXiv: https://arxiv.org/abs/2107.02499 · HTML: https://mlsanity.com/paper/2107.02499/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [Efficient Twitter Sentiment Classification using Subjective Distant Supervision](https://mlsanity.com/paper/1701.03051/) (2017)
- [Improving Results on Russian Sentiment Datasets](https://mlsanity.com/paper/2007.14310/) (2020)

## Built on by (most cited first, 1 of 1)

- [Lexicon-based Methods vs. BERT for Text Sentiment Analysis](https://mlsanity.com/paper/2111.10097/) (2021)

## Abstract (arXiv)

In this study, we test transfer learning approach on Russian sentiment benchmark datasets using additional train sample created with distant supervision technique. We compare several variants of combining additional data with benchmark train samples. The best results were achieved using three-step approach of sequential training on general, thematic and original train samples. For most datasets, the results were improved by more than 3% to the current state-of-the-art methods. The BERT-NLI model treating sentiment classification problem as a natural language inference task reached the human level of sentiment analysis on one of the datasets.