# Current Landscape of the Russian Sentiment Corpora

arXiv: https://arxiv.org/abs/2106.14434 · HTML: https://mlsanity.com/paper/2106.14434/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [Multilingual Twitter Sentiment Classification: The Role of Human Annotators](https://mlsanity.com/paper/1602.07563/) (2016)
- [Self-Explaining Structures Improve NLP Models](https://mlsanity.com/paper/2012.01786/) (2020)
- [Improving Results on Russian Sentiment Datasets](https://mlsanity.com/paper/2007.14310/) (2020)
- [Extracting Sentiment Attitudes From Analytical Texts](https://mlsanity.com/paper/1808.08932/) (2018)

## Abstract (arXiv)

Currently, there are more than a dozen Russian-language corpora for sentiment analysis, differing in the source of the texts, domain, size, number and ratio of sentiment classes, and annotation method. This work examines publicly available Russian-language corpora, presents their qualitative and quantitative characteristics, which make it possible to get an idea of the current landscape of the corpora for sentiment analysis. The ranking of corpora by annotation quality is proposed, which can be useful when choosing corpora for training and testing. The influence of the training dataset on the performance of sentiment analysis is investigated based on the use of the deep neural network model BERT. The experiments with review corpora allow us to conclude that on average the quality of models increases with an increase in the number of training corpora. For the first time, quality scores were obtained for the corpus of reviews of ROMIP seminars based on the BERT model. Also, the study proposes the task of the building a universal model for sentiment analysis.