textliteraturedigital-humanitiesnlpsentimentgenreinfluence-network

Classic English Novels, Computed: Sentiment Arcs, Genres and an Author Influence Graph (343 Novels)

Updated Oct 20, 2026

$49

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Sample structure: 100 / 100

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Seller: Brick and Mortar

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Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License: Brick & Mortar data licence: internal commercial use incl. model training, no redistribution
Source / creator: Project Gutenberg full texts (public domain); Wikidata property P737 'influenced by' (CC0); computations by Brick and Mortar AI LLC.
Collection method: Seven years of computational-literature research, reduced to tables. The texts are Project Gutenberg's; every number is ours. Sentiment arcs: TextBlob polarity per 1% of each book, smoothed (9-part centred mean), classified into shapes (Rise, Fall, Rags to riches, ...). Genres: an unsupervised network on raw opening prose recovers eight genre communities (detective, science fiction, nautical adventure, ...). Influence: 205 documented author-to-author influences (Wikidata P737 plus model-attested edges, each marked with its source) and TF-IDF stylistic plus embedding conceptual similarity for all 2,926 author pairs. Useful for teaching, for digital-humanities baselines, and as labelled evaluation data for language models.
Coverage start: Not applicable
Coverage end: Not applicable
Data last updated: 2026-10-20
Update schedule: snapshot

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Sample structure score: 100 / 100

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

Assessed 10 sample records (CSV) on 2026-10-20. All records in the provided sample were checked.

Check	Points	Evidence
Populated cells	50 / 50	160 of 160 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated.
Consistent value types	30 / 30	160 of 160 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth.
Consistent record shape	20 / 20	10 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys.

Field-level findings and improvements

Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.

Field	Missing cells	Most common type	Other populated types
work_id	0 / 10	number	0 / 10
title	0 / 10	string	0 / 10
author	0 / 10	string	0 / 10
year	0 / 10	number	0 / 10
decade	0 / 10	number	0 / 10
genre_id	0 / 10	number	0 / 10
gutenberg_id	0 / 10	number	0 / 10
genre_name	0 / 10	string	0 / 10
words	0 / 10	number	0 / 10
shape	0 / 10	string	0 / 10
turn_segment	0 / 10	number	0 / 10
mean_polarity	0 / 10	number	0 / 10
start_polarity	0 / 10	number	0 / 10
end_polarity	0 / 10	number	0 / 10
polarity_range	0 / 10	number	0 / 10
arc_100	0 / 10	string	0 / 10

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About this data

Computed features for 343 public-domain English novels (1660-1928): a 100-point sentiment arc per book with its shape, eight genres recovered from opening prose alone, book-to-book arc similarity, and a 77-author graph of documented influences with stylistic and conceptual similarity for every author pair. One CSV, 343 rows; column meanings in the schema. No book text. Genre labels from third-party reading lists, used only for validation, are left out.

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Schema

Name	Type	Description
work_id	number
title	string
author	string
year	number
decade	number
genre_id	number
gutenberg_id	number
genre_name	string
words	number
shape	string
turn_segment	number
mean_polarity	number
start_polarity	number
end_polarity	number
polarity_range	number
arc_100	string

Sample Data

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