Scientists devise algorithm to predict success of novels

Researchers analyzed all kinds of books, from science fiction to classic literature, and found a connection between successful writing styles and readability.
By Ananth Baliga   |   Jan. 9, 2014 at 2:23 PM

What's the secret to a best-selling novel? Researchers at Stony Brook University have figured it out, after developing an algorithm that can determine whether a novel will be commercially successful with 84 percent accuracy.

Using a technique called statistical stylometry, the analysis of variations in literary style between one writer or genre and another, researchers were able to distinguish highly successful literature from less successful literature by analyzing the first 1000 sentences of each book.


"Based on novels across different genres, we investigated the predictive power of statistical stylometry in discriminating successful literary works, and identified the stylistic elements that are more prominent in successful writings,” said Yejin Choi assistant professor at Stony Brook University.

Researchers found that successful novels made frequent use of conjunctions, prepositions, nouns, pronouns, determiners and adjectives. Less successful books had a higher percentage of verbs, adverbs, and foreign words. Such books also relied heavily on clichés, extreme and negative words.

Less successful books also rely on dull verbs that describe direct action, such as "took," "promised" and "cried," while more successful books use more verbs that describe thought-processing, such as "recognized" and "remembered."

The researchers downloaded classic literature from the Project Gutenberg archive, used more recent award-winning novels and analyzed low-ranking books on Amazon -- and included genres from science fiction to classic literature and even poetry.

Choi points out this is only a correlation and not a causation, saying, “We conjecture that the conceptual complexity of highly successful literary work might require syntactic complexity that goes against readability.”

[Stony Brook University]

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