pkchwy/letterboxd-all-movie-data

Letterboxd Film Dataset

This dataset contains a comprehensive collection of 847,209 films from the Letterboxd platform, including movie information, user reviews, and ratings.

Dataset Summary

• Total Films: 847,209
• File Size: ~1.12 GB (1,120,572,122 bytes)
• Format: JSONL (JSON Lines)
• Language: Primarily English, with some multilingual content

Data Structure

Each line contains a JSON object with the following fields:

{
  "url": "https://letterboxd.com/film/come-and-see/",
  "title": "Come and See",
  "year": "1985",
  "directors": ["Elem Klimov"],
  "genres": ["War", "Drama"],
  "cast": ["Aleksei Kravchenko", "Olga Mironova", ...],
  "synopsis": "The invasion of a village in Byelorussia by German forces...",
  "rating": "4.62 out of 5",
  "poster_url": "https://a.ltrbxd.com/resized/film-poster/...",
  "reviews": [
    {
      "username": "cameron fetter",
      "review_text": "as soon as this film ended i went online...",
      "likes": "11662"
    }
  ]
}

Data Fields

Field	Type	Description
url	string	Letterboxd film page URL
title	string	Movie title
year	string	Release year
directors	array	Director names
genres	array	Movie genres
cast	array	Actor names
synopsis	string	Movie plot summary
rating	string	Average user rating (out of 5)
poster_url	string	Movie poster image URL
reviews	array	Popular user reviews (max 10)

Reviews Object

Each review contains:

• username: Reviewer's username
• review_text: Review content
• likes: Number of likes received

Usage

Loading Data with Python

import json

# Loading JSONL file
films = []
with open('full_dump.jsonl', 'r', encoding='utf-8') as f:
    for line in f:
        films.append(json.loads(line))

print(f"Loaded {len(films)} films")
print(f"First film: {films[0]['title']} ({films[0]['year']})")

Analysis with Pandas

import pandas as pd
import json

# Converting to DataFrame
data = []
with open('full_dump.jsonl', 'r', encoding='utf-8') as f:
    for line in f:
        data.append(json.loads(line))

df = pd.json_normalize(data)
print(df.head())

# Basic statistics
print(f"Most popular genres: {df['genres'].explode().value_counts().head()}")
print(f"Most productive years: {df['year'].value_counts().head()}")

Using Hugging Face Datasets

from datasets import load_dataset

# Loading dataset
dataset = load_dataset("pkchwy/letterboxd-all-movie-data")

# Exploring first example
print(dataset['train'][0])

# Filtering example - only films after 2020
recent_films = dataset['train'].filter(lambda x: int(x['year']) > 2020)

Use Cases

This dataset can be used for:

🎬 Movie Recommendation Systems

• Collaborative filtering based on reviews and ratings
• Content-based recommendation (genre, director, actor similarity)

📝 Natural Language Processing

• Sentiment analysis on film reviews
• Text classification (genre prediction from synopsis)
• Review quality assessment

📊 Data Analysis & Visualization

• Film industry trend analysis
• Director and actor popularity analysis
• Genre distribution and temporal changes

Data Quality

• Missing Data: Some films may lack synopsis, cast, or review information
• Language: Reviews are mostly in English, with some multilingual content
• Time Range: Films from 1890s to present day
• Scope: Popular films on Letterboxd platform (rating-based ranking)

Ethical Use and Limitations

• This dataset consists of publicly available Letterboxd data
• Contains no personal information (only usernames)
• Check Letterboxd terms of service before commercial use
• Possible sampling bias (popular films weighted)

Citation

If you use this dataset in your research or projects, please cite it as:

@dataset{letterboxd_film_dataset_2025,
  title={Letterboxd Film Dataset},
  author={Salih Mert Canseven},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/pkchwy/letterboxd-all-movie-data}
}

Or in text format:

Salih Mert Canseven. (2025). Letterboxd Film Dataset. Hugging Face. https://huggingface.co/datasets/pkchwy/letterboxd-all-movie-data

License

MIT License - Free for educational and research purposes. Citation required for any use.

Contact

For questions about the dataset, please use GitHub Issues.

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Source: Letterboxd.com