The document discusses using network analysis to analyze Stephen King's novel "The Shining" without reading the text. It describes extracting named entities, detecting co-occurrences between entities, calculating similarity, constructing a network graph, identifying clusters in the graph, and analyzing centrality to identify important characters and locations. The network provides a navigational chart of relationships in the book.

1. The Shining. Network Analysis
Dmitry Zinoviev—Suffolk University—dzinoviev@suffolk.edu—2017

2. Why Network Analysis?
● Identify core actors and places
● Identify relationships between actors, and actors
and places
● Identify groups (“clusters”) of actors
● ...ideally without reading the text

3. Starting with e-Text
Wendy suddenly felt bad, almost crying bad... She hung the dish
towel over the bar by the sink and went downstairs, buttoning the
top two buttons of her house dress. Jack and his pride! Hey no, Al,
I don't need an advance... The whole building smelled of sour age,
and what sort of place was this for Danny after the small neat
brick house in Stovington? The people living above them on the
third floor weren't married, and while that didn't bother her,
their constant, rancorous fighting did. It scared her. The guy up
there was Tom, and after the bars had closed and they had
returned home, the fights would start in earnest – the rest of the
week was just a prelim in comparison. The Friday Night Fights,
Jack called them, but it wasn't funny.

4. [Named] Entity Extraction
Wendy suddenly felt bad, almost crying bad... She hung the dish
towel over the bar by the sink and went downstairs, buttoning the
top two buttons of her house dress. Jack and his pride! Hey no, Al,
I don't need an advance... The whole building smelled of sour age,
and what sort of place was this for Danny after the small neat
brick house in Stovington? The people living above them on the
third floor weren't married, and while that didn't bother her,
their constant, rancorous fighting did. It scared her. The guy up
there was Tom, and after the bars had closed and they had
returned home, the fights would start in earnest – the rest of the
week was just a prelim in comparison. The Friday Night Fights,
Jack called them, but it wasn't funny.

5. Possible Problems
Wendy suddenly felt bad, almost crying bad... She hung the dish
towel over the bar by the sink and went downstairs, buttoning the
top two buttons of her house dress. Jack and his pride! Hey no, Al,
I don't need an advance... The whole building smelled of sour age,
and what sort of place was this for Danny after the small neat
brick house in Stovington? The people living above them on the
third floor weren't married, and while that didn't bother her,
their constant, rancorous fighting did. It scared her. The guy up
there was Tom, and after the bars had closed and they had
returned home, the fights would start in earnest – the rest of the
week was just a prelim in comparison. The Friday Night Fights,
Jack called them, but it wasn't funny.
Anonymous references
Non-entities
One entity several names→
One name several entities→

6. Co-Occurrence Detection
Wendy suddenly felt bad, almost crying bad... She hung the dish
towel over the bar by the sink and went downstairs, buttoning the
top two buttons of her house dress. Jack and his pride! Hey no, Al,
I don't need an advance... The whole building smelled of sour age,
and what sort of place was this for Danny after the small neat
brick house in Stovington? The people living above them on the
third floor weren't married, and while that didn't bother her,
their constant, rancorous fighting did. It scared her. The guy up
there was Tom, and after the bars had closed and they had
returned home, the fights would start in earnest – the rest of the
week was just a prelim in comparison. The Friday Night Fights,
Jack called them, but it wasn't funny.
● Within 35 words?
● Within one
paragraph?
● Within one chapter?

7. Similarity Calculation
● Quantitative similarity on the scale from -1
(totally dissimilar) to 1 (totally similar)
● Two entities are similar if they act in similar
chapters
● Two chapters are similar if they refer to similar
entities

8. Network Construction
Connection thickness—
similarity.
Font size—number of
references to the entity in the
text
Hatfield
Stovington
Jack
Dick
Watson
Al
Hallorann
Roger
Lloyd
Grady
Durkin
Tony
DerwentHotel
Florida
Overlook
Torrance
Boulder
Dad
Redrum
Sidewinder
Ullman
Shockley
Edmonds
Mom
Colorado
George
Denver
Wendy
Danny

9. Detecting Clusters
Pink: Initial setup
Orange: Danny's perspective and
ability to read minds
Green: Information about hotel
Hatfield
Stovington
Jack
Dick
Watson
Al
Hallorann
Roger
Lloyd
Grady
Durkin
Tony
DerwentHotel
Florida
Overlook
Torrance
Boulder
Dad
Redrum
Sidewinder
Ullman
Shockley
Edmonds
Mom
Colorado
George
Denver
Wendy
Danny

10. Betweenness
Node size—betweenness centrality
(BC). The higher is BC—the more
“central” is the character / place
Highest BC: Schockley, Edmonds,
Florida, Dad, Durkin, Al, Watson,
Lloyd, Ullman, Sidewinder.
Hatfield
Stovington
Jack
Dick
Watson
Al
Hallorann
Roger
Lloyd
Grady
Durkin
Tony
DerwentHotel
Florida
Overlook
Torrance
Boulder
Dad
Redrum
Sidewinder
Ullman
Shockley
Edmonds
Mom
Colorado
George
Denver
Wendy
Danny

11. Summary
● Identified three plot lines, including characters
and locations
● Identified “most important” characters
● Network as a navigational chart of the book
● Almost no reading involved!