README.md

metadata

language:
  - en
pretty_name: 'Plot Twists Over Time: How Movie Stories Have Changed Over 95 Years'
tags:
  - movies
  - embeddings
  - semantic-analysis
  - temporal-analysis
  - text-embeddings
  - plot-summaries
  - genre-classification
  - concept-extraction
  - wikidata
  - wikipedia
  - tmdb
  - bge-m3
  - text
license: cc-by-nc-4.0
task_categories:
  - text-classification
  - sentence-similarity
  - text-retrieval
  - feature-extraction
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: movie_id
      dtype: string
    - name: title
      dtype: string
    - name: year
      dtype: int32
    - name: plot
      dtype: string
    - name: genre
      dtype: string
    - name: embedding
      dtype: float32
      shape:
        - 1024

Movie Plot Embeddings Dataset

Project Title: Plot Twists Over Time: How Movie Stories Have Changed Over 95 Years

Dataset Summary

This dataset contains movie metadata, plot summaries, and semantic embeddings for ~92,000 feature-length films (1930-2024). Created for temporal semantic drift analysis, it includes metadata from Wikidata, TMDb, and Wikipedia, along with dense (1024-dim) and sparse embeddings generated using BAAI/bge-m3.

Key Statistics: ~92,000 movies | Year range: 1930-2024 | Embedding dimension: 1024 | Average plot length: ~1,500 characters

Dataset Structure

Core Files

• final_dataset.csv (200 MB): Movie metadata with 39 columns (title, year, genre, plot, directors, actors, TMDb metrics, etc.)
• final_dense_embeddings.npy (380 MB): Dense embeddings array (N, 1024) using BGE-M3 CLS token aggregation
• final_dense_movie_ids.npy (7 MB): Wikidata QIDs corresponding to embeddings (index-aligned: embeddings[i] ↔ movie_ids[i])
• final_sparse_embeddings.npz (500 MB): Sparse lexical weights for concept extraction (token_indices, weights, movie_ids)

Additional Files

• knn_faiss_novelty.csv: Novelty scores and neighbor information
• umap_cluster_trajectories.png: UMAP visualization of embeddings
• concept_space/: WordNet-based concept vocabulary and embeddings for semantic mapping

Data Collection Pipeline

Four-step pipeline: (1) Wikidata: Query SPARQL for movie metadata (1930-2024, ~8K/year), filter feature films; (2) TMDb: Enrich with popularity, votes, ratings via API; (3) Wikipedia: Extract plot summaries from sitelinks; (4) Embeddings: Generate dense/sparse embeddings using BGE-M3 with CLS token aggregation, parallel GPU processing.

Data Fields

Core: movie_id (Wikidata QID), title, year, imdb_id, tmdb_id

Metadata: release_date, country, duration, wikidata_class, wikipedia_link

Creative Team: directors, directors_id, actors, actors_id (pipe-separated for multiple)

Genre: genre (raw, comma-separated), genre_id, genre_cluster_ids, genre_cluster_names (processed)

Plot: plot (full text), plot_section, plot_length_chars, plot_length_tokens, num_different_tokens, token_shannon_entropy

TMDb Metrics: popularity, vote_average, vote_count

Financial (if available): budget, budget_currency, box_office, box_office_currency, box_office_worldwide

Other: set_in_period, awards, summary

Loading the Dataset

Basic Python Example

import numpy as np
import pandas as pd

# Load metadata and embeddings
df = pd.read_csv('final_dataset.csv', low_memory=False)
embeddings = np.load('final_dense_embeddings.npy')
movie_ids = np.load('final_dense_movie_ids.npy', allow_pickle=True)

# Merge embeddings with metadata
embeddings_df = pd.DataFrame({'movie_id': movie_ids, 'embedding': list(embeddings)})
combined_df = pd.merge(df, embeddings_df, on='movie_id', how='inner')

Using Utilities

from src.data_utils import load_final_data_with_embeddings

df = load_final_data_with_embeddings(
    csv_path="data/data_final/final_dataset.csv",
    data_dir="data/data_final",
    verbose=True
)

Embedding Details

• Model: BAAI/bge-m3 | Dimension: 1024 | Method: CLS token aggregation | Normalization: L2-normalized
• Alignment: final_dense_embeddings[i] corresponds to final_dense_movie_ids[i]
• Alternative chunking: Supports mean pooling, chunk-first, late chunking (stored with suffixes)

Concept Space

Pre-computed WordNet-based concept vocabulary: top 20K nouns (Zipf ≥ 4.0), embedded with BGE-small-en-v1.5. Files parameterized by Zipf threshold, vocabulary size, and model name.

Data Quality

Completeness: All movies have core fields; ~92K plots, ~91K TMDb data, ~75K directors, ~81K genres.

Filtering: Feature films only; plots filtered by length/entropy; explicit content excluded; duplicates removed.

Cleaning: Plot text normalized; genres clustered; Shannon entropy threshold: 4.8398.

Use Cases

Temporal Analysis: Compute decade centroids to analyze semantic drift over time.

Genre Classification: Use embeddings for clustering or classification tasks.

Similarity Search: Find similar movies using cosine similarity on embeddings.

Concept Extraction: Map plot nouns to concept space using sparse lexical weights.

Citation

@dataset{movie_plot_embeddings_2026,
  title={Plot Twists Over Time: How Movie Stories Have Changed Over 95 Years},
  author={Cheung, Ansel and Villa, Alessio and Markovinović, Bartol and López de Ipiña, Martín and Abraham, Niklas},
  year={2026},
  note={Dataset collected from Wikidata, TMDb, and Wikipedia for temporal semantic analysis of movie plots}
}

License

This dataset inherits licenses from source data:

• Wikidata: CC0 1.0 (Public Domain)
• Wikipedia: CC BY-SA 4.0
• TMDb: CC BY-NC 4.0 (non-commercial)

Dataset License: CC BY-NC 4.0 for non-commercial research. Commercial use requires TMDb licensing and Wikipedia compliance.

Code: MIT License (see main repository).

Attribution Required: Cite Wikidata, Wikipedia, TMDb contributors, and this dataset.

Acknowledgments

Wikidata, TMDb, Wikipedia, BAAI (BGE-M3), WordNet

Notes

• Embeddings are L2-normalized
• Movie IDs are Wikidata QIDs (format: "Q####")
• Plot text cleaned and normalized
• Genres may be multi-label (pipe-separated)
• Some fields may be NaN for older/less popular films