Writers who fear that computers will someday displace them may shudder to learn that the machines won’t just write the books, they will read them too.

In recent months, both researchers and literary critics have harnessed computational power to “read” books in an effort to divine qualities human writers and readers haven’t the bandwidth to discover themselves (“The Taxonomy of Titles in the 18th Century Literary Marketplace” — anyone? Anyone? Bueller?).

Among them is a trio of computer scientists at New York’s Stony Brook University who created an algorithm to predict the success of literary styles that boasts an 84 percent rate of accuracy when analyzing previously published works.

“In a paper published by the Association of Computational Linguistics, Vikas Ganjigunte Ashok, Song Feng, and Yejin Choi said the writing style of books was correlated with the success of the book,” writes Live Science contributor Joel N. Shurkin. Using a process called “statistical stylometry” to analyze literary stylings in an array of books across genres, the team identified the “characteristic stylistic elements more common in successful tomes than unsuccessful ones.”

It’s only a matter of time before researchers team up with an agency like Narrative Science, whose artificial intelligence algorithms pair data with “natural language communication? to produce written content, resulting in bestsellers by bots.”

Comp(uter)Lit

But who would read it? Other computers, thanks to Franco Moretti, who founded Stanford’s Literary Lab so that digital dalliances with texts could have a room of their own. His essay collection, Distant Reading, recently won the National Book Critics Circle Award for criticism and he has been lauded by Wired for his “data-centric approach to novels, which he graphs, maps, and charts … if his new methods catch on, they could change the way we look at literary history.”

At first glance, this may make you want to cue up the famous “Understanding Poetry” scene in Dead Poets Society wherein Robin Williams goads his glass to aggressively edit a couple dozen poetry primers of their analytic assumptions.

(You may review Dr. J. Evans-Pritchard’s chart at the I Love Charts tumblog.)

Actually, Moretti’s work is fascinating — look over his “pamphlet” on Network Theory, Plot Analysis, which teems with graphs on the intricate relationships in Hamlet (big, fat pdf here). However, some in lit-crit circles aren’t enthused with Moretti’s critical approach and suggest putting words into a numbers cruncher can only result in damage to both.

“He thinks that literary criticism ought to be a science,” writes Joshua Rothman for the New Yorker’s Page-Turner blog. “The basic idea in Moretti’s work is that, if you really want to understand literature, you can’t just read a few books or poems over and over (“Hamlet,” “Anna Karenina,” “The Waste Land”). Instead, you have to work with hundreds or even thousands of texts at a time.”

This is precisely what the algorithms at Amazon and Apple’s respective bookstores do, which can sometimes produce discomfiting results that say more about readers’ proclivities than perhaps we care to know. For example, we can forgive readers for their fascination with fan-fic-turned-softcore, but why the hell is Mein Kampf a bestselling ebook? Vocativ contributor Chris Faraone asks “Is this what happens when Mein Kampf becomes available in the privacy of our own iPads? Could it be a cultural curiosity much like what’s happened with sleazy romance novels, which surveys show are increasingly consumed in more clandestine e-form?”

What’s almost more chilling are the results of Amazon’s recommendation engine, which aggregates information from millions of purchases in an effort to upsell consumers on additional product. Click-through the titles on the “Customers Who Bought This Item Also Bought”??” prompt for a glimpse into the data-driven future.

So, worse case scenario, Ashok, Feng and Choi’s stylometry breaks down the secret bestseller recipe, Narrative Science bots implement it and later Franco Moretti can explain how we became enslaved to a bestselling Nazi computer overlord via our Kindles.

Somewhere Philip K. Dick is crying.

Update: July 4, 2024

This is the result of inputting “Literary Criticism by Robots” into ChatGPT.

Literary Criticism by Robots

Introduction

The intersection of artificial intelligence (AI) and literary criticism opens new horizons for analyzing texts. This concept involves using AI algorithms, particularly natural language processing (NLP) and machine learning (ML), to examine literature. Robots can provide unique insights by processing vast amounts of text, identifying patterns, themes, and stylistic elements that might escape human critics.

Advantages of Robot Literary Criticism

1. Data Processing Capability: Robots can analyze massive volumes of text quickly, uncovering trends and connections across different works and periods.
2. Objectivity: AI can approach texts without personal bias, potentially offering a more impartial analysis.
3. Pattern Recognition: Advanced algorithms can detect subtle patterns and structures in language and narrative that might be overlooked by human readers.
4. Comparative Analysis: Robots can compare and contrast numerous texts simultaneously, providing a broad spectrum analysis that is difficult for individual critics to achieve.

Methodologies

1. Text Mining and NLP: Techniques such as sentiment analysis, topic modeling, and keyword extraction help in understanding the underlying themes and emotions in a text.
2. Stylometric Analysis: This involves examining an author’s style through word frequency, sentence structure, and other linguistic features to attribute authorship or analyze stylistic evolution.
3. Machine Learning Models: Algorithms can be trained to classify texts, predict narrative outcomes, or even generate new literary works based on learned patterns.

Case Studies

1. Shakespearean Analysis: AI has been used to examine authorship of Shakespeare’s plays, identifying distinct linguistic patterns that suggest collaboration with other playwrights.
2. Sentiment Analysis in Victorian Novels: Robots can analyze the emotional arcs of characters, mapping out sentiments throughout a novel to provide a deeper understanding of character development and thematic progression.
3. Modern Literature Trends: AI can track trends in contemporary literature, such as the rise of dystopian themes or the evolution of narrative techniques in digital storytelling.

Challenges and Limitations

1. Interpretative Depth: While robots excel at pattern recognition, they might struggle with the nuanced interpretation of metaphors, allegories, and complex human emotions.
2. Bias in Training Data: AI models are only as good as the data they are trained on. Biases in training data can lead to skewed or incomplete analyses.
3. Creativity and Subjectivity: Literary criticism often involves subjective and creative insights that robots may not be capable of replicating.

Future Prospects

1. Enhanced Collaboration: Combining AI analysis with human critique can enrich literary criticism, offering a blend of data-driven insights and human interpretative depth.
2. Interactive Literary Tools: AI-powered tools can assist writers in developing plots, characters, and dialogues, pushing the boundaries of creative writing.
3. Evolving Narratives: AI might contribute to the creation of adaptive narratives in interactive media, where stories evolve based on user interaction.

Conclusion

Literary criticism by robots is an evolving field that holds promise for the future of literary studies. By leveraging the power of AI, we can uncover new dimensions of texts, enhance our understanding of literature, and explore the interplay between human creativity and machine intelligence. While challenges remain, the potential benefits make it a fascinating area of exploration for both literary scholars and AI researchers.

HT Ivan Hewett.