Datasets:

Ishaank18
/

screenplay-features

license: cc-by-4.0
task_categories:
  - text-classification
tags:
  - screenplay
  - narrative
  - salience
  - linguistics
language:
  - en
size_categories:
  - 100K<n<1M

Screenplay Scene Salience Features

Pre-extracted linguistic and narrative features for screenplay scene salience detection from the MENSA dataset.

Dataset Description

This dataset contains 913 linguistic features extracted from movie screenplays in the MENSA dataset. Features are organized into 24 feature groups covering various aspects of linguistic, narrative, and discourse analysis.

Dataset Statistics

Split	Samples	Size
Train	117,503	172.9 MB
Validation	8,052	16.1 MB
Test	8,156	16.1 MB
Total	133,711	140.1 MB

Feature Groups (24 groups)

base
bert_surprisal
character_arcs
emotional
gc_academic
gc_basic
gc_char_diversity
gc_concreteness
gc_dialogue
gc_discourse
gc_narrative
gc_polarity
gc_pos
gc_pronouns
gc_punctuation
gc_readability
gc_syntax
gc_temporal
ngram
ngram_surprisal
plot_shifts
rst
structure
surprisal

Usage

Option 1: Load with Hugging Face datasets (Recommended)

from datasets import load_dataset

# Load a single feature group
ds = load_dataset("Ishaank18/screenplay-features", data_files="train/base.parquet")
df = ds['train'].to_pandas()

# Load multiple groups for training
ds = load_dataset("Ishaank18/screenplay-features", 
                  data_files={
                      "train": ["train/base.parquet", "train/gc_polarity.parquet", "train/emotional.parquet"]
                  })
df = ds['train'].to_pandas()

# Load all splits for evaluation
ds = load_dataset("Ishaank18/screenplay-features",
                  data_files={
                      "train": "train/gc_polarity.parquet",
                      "validation": "validation/gc_polarity.parquet",
                      "test": "test/gc_polarity.parquet"
                  })

Option 2: Load with pandas directly

import pandas as pd

# From HuggingFace URL
df = pd.read_parquet("hf://datasets/Ishaank18/screenplay-features/train/base.parquet")

# Or if you have the repo cloned locally
df = pd.read_parquet("train/base.parquet")

Option 3: Use custom loader (Easiest)

from feature_cache.load_hf import load_groups

# Load features and labels
X, y = load_groups(
    groups=["base", "gc_polarity", "emotional", "rst"],
    split="train",
    hf_repo="Ishaank18/screenplay-features"
)

# Load features only (no labels)
X = load_groups(
    groups=["base", "gc_polarity"],
    split="test",
    include_label=False,
    hf_repo="Ishaank18/screenplay-features"
)

Data Structure

Each parquet file contains:

movie_id (string): Unique movie identifier
scene_index (int): Scene index within the movie (0-indexed)
label (int): Salience label
- 0 = Non-salient scene
- 1 = Salient scene
Feature columns: Various linguistic/narrative features (float/int)

Example row structure:

movie_id	scene_index	label	feature_1	feature_2	...
tt0111161	42	1	0.85	12.3	...

Feature Categories

The features are organized into the following categories:

Base Features

Basic linguistic statistics (token count, sentence count, etc.)
Structural position features (act, scene positions)

GenreClassifier (GC) Features

gc_basic: Basic linguistic metrics
gc_char_diversity: Character diversity metrics
gc_concreteness: Concreteness scores
gc_dialogue: Dialogue-specific features
gc_discourse: Discourse markers and connectives
gc_narrative: Narrative structure features
gc_polarity: Sentiment polarity scores
gc_pos: Part-of-speech distributions
gc_pronouns: Pronoun usage patterns
gc_punctuation: Punctuation statistics
gc_readability: Readability metrics
gc_syntax: Syntactic complexity features
gc_temporal: Temporal expressions

Narrative Features

character_arcs: Character development metrics
plot_shifts: Plot progression indicators
structure: Narrative structure features
emotional: Emotional arc features

Linguistic Features

ngram: N-gram diversity metrics
rst: Rhetorical Structure Theory features
bert_surprisal: BERT-based surprisal scores
ngram_surprisal: N-gram-based surprisal