task_categories:text-classification language:ru size_categories:100K<n<1M format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype: int64
  - name: src
    dtype: string
  splits:
  - name: train
    num_bytes: 184513127
    num_examples: 290458
  download_size: 96118934
  dataset_size: 184513127
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-classification
language:
- ru
size_categories:
- 100K<n<1M
---

# Russian Sentiment Analysis Dataset

## Overview

This dataset is a comprehensive collection of Russian-language reviews and texts annotated for sentiment analysis. It is designed to support the development and evaluation of sentiment analysis models for the Russian language. The dataset is balanced across sentiment classes (`neutral`, `positive`, and `negative`) to ensure fair model training and evaluation.

## Dataset Composition

The dataset is compiled from multiple publicly available sources, including:

1. **AI-Forever/ru-reviews-classification**: A collection of Russian reviews with sentiment labels.
2. **Lapki/Perekrestok-reviews**: Reviews from the Perekrestok retail chain.
3. **d0rj/geo-reviews-dataset-2023**: Geotagged reviews from various Russian locations.
4. **Linis 2015 & Linis 2016**: Crowdsourced sentiment-annotated datasets from the Linis Crowd project ([Linis Crowd](https://linis-crowd.org/)).
5. **RuReviews**: A dataset of Russian reviews ([GitHub](https://github.com/sismetanin/rureviews)).
6. **Kaggle News Dataset**: News articles with sentiment annotations ([Kaggle](https://www.kaggle.com/competitions/sentiment-analysis-in-russian/data)).
7. **RuSentiment**: A dataset of Russian social media posts with sentiment labels ([GitHub](https://github.com/strawberrypie/rusentiment)).
8. **Bank Reviews Dataset**: Reviews of Russian banks ([Kaggle](https://www.kaggle.com/datasets/romanberdyshev/bank-reviews-dataset)).
9. **Kinopoisk's Movies Reviews**: Movie reviews from Kinopoisk ([Kaggle](https://www.kaggle.com/datasets/mikhailklemin/kinopoisks-movies-reviews)).
10. **Big Bank App Reviews from AppStore**: Reviews of Sber app from the AppStore ([Kaggle](https://www.kaggle.com/datasets/kicelevalexey/bank-app-reviews-rus)).
11. **Russian Anime Reviews Dataset**: Reviews of anime from Russian-speaking users ([Kaggle](https://www.kaggle.com/datasets/theodaimones888/russian-anime-reviews-dataset)).

All source datasets were carefully balanced across sentiment classes (`neutral`, `positive`, and `negative`) to ensure a robust and unbiased dataset.

## Dataset Structure

The dataset contains the following columns:
- **text**: The text of the review or comment.
- **label**: The sentiment label, where:
  - `0`: Neutral
  - `1`: Positive
  - `2`: Negative
- **src**: Source dataset.

## Usage

You can load the dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("k1tub/sentiment_dataset")

# Example usage
print(dataset['train'][0])  # View the first example