Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
sentiment-classification
Languages:
Russian
Size:
10K - 100K
License:
File size: 1,317 Bytes
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---
languages:
- ru
multilinguality:
- monolingual
pretty_name: Kinopoisk
size_categories:
- 10K<n<100K
task_categories:
- text-classification
- sentiment-analysis
task_ids:
- sentiment-classification
---
### Dataset Summary
Kinopoisk movie reviews dataset (TOP250 & BOTTOM100 rank lists).
In total it contains 36,591 reviews from July 2004 to November 2012.
With following distribution along the 3-point sentiment scale:
- Good: 27,264;
- Bad: 4,751;
- Neutral: 4,576.
### Data Fields
Each sample contains the following fields:
- **part**: rank list top250 or bottom100;
- **movie_name**;
- **review_id**;
- **author**: review author;
- **date**: date of a review;
- **title**: review title;
- **grade3**: sentiment score Good, Bad or Neutral;
- **grade10**: sentiment score on a 10-point scale parsed from text;
- **content**: review text.
### Python
```python3
import pandas as pd
df = pd.read_json('kinopoisk.jsonl', lines=True)
df.sample(5)
```
### Citation
```
@article{blinov2013research,
title={Research of lexical approach and machine learning methods for sentiment analysis},
author={Blinov, PD and Klekovkina, Maria and Kotelnikov, Eugeny and Pestov, Oleg},
journal={Computational Linguistics and Intellectual Technologies},
volume={2},
number={12},
pages={48--58},
year={2013}
}
```
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