Digital Humanities
Stanford Lit Lab Trains Neural Network To Identify Suspense in Stories
A group of researchers at the Stanford Literary Lab at Stanford University have developed a virtual reader using a neural network and trained it to identify suspenseful passages in works of literature.
"The
big goal of the research is to try and explain why we feel suspense
when confronted with certain aesthetic objects, even if we know the
outcome of them," said Mark Algee-Hewitt, assistant professor of
English at Stanford and leader of the project, in a prepared statement.
Algee-Hewitt
and a group of eight graduate students in the English department began
by tracking their own emotional responses to reading. One of the
various methods they used was to rate paragraphs on a scale of one to
ten based on the level of suspense they felt when reading it, according
to a report on Stanford's site. They then compared their ratings. While
some of the researchers disagreed on the degree of suspense they felt,
they did agree on the passages where suspense increased or decreased.
Using
digital humanities techniques, they analyzed the passages and
discovered specific words or topics that were associated with the
element of suspense. More suspenseful passages tended to include words
related to the imagination, the senses and movement, and topics related
to violence or dramatic weather.
The researchers took the next step of developing a virtual reader using a neural network —
a computer program that can be trained to identify new objects on its
own — and trained it to identify suspenseful passages based on the set
of criteria they had discovered through their analysis. "The neural
network achieved 81 percent accuracy in identifying passages it had
never seen before as either suspenseful or non-suspenseful," stated a
report on Stanford's site.
The researchers hope to publish a paper on the project.
About the Author
Leila Meyer is a technology writer based in British Columbia. She can be reached at [email protected].
