README.md

metadata

language:
  - ru
multilinguality:
  - monolingual
pretty_name: Kinopoisk
size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
license: mit

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

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}
}