An example of how automated text analysis leads, through social network construction, to recognition of key plot lines and characters in a fiction book.

1. The Da Vinci Code
Network Analysis
Dmitry Zinoviev—Suffolk University—dzinoviev@suffolk.edu—2016

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
...The man was stocky and dark, almost Neanderthal,
dressed in a dark double­breasted suit that strained to cover
his wide shoulders. He advanced with unmistakable
authority on squat, powerful legs. He was speaking on his
cell phone but finished the call as he arrived. He motioned
for Langdon to enter.
“I am Bezu Fache,” he announced as Langdon pushed
through the revolving door. “Captain of the Central
Directorate Judicial Police.” His tone was fitting—a
guttural rumble... like a gathering storm...

4. Entity Extraction
...The man was stocky and dark, almost Neanderthal,
dressed in a dark double­breasted suit that strained to cover
his wide shoulders. He advanced with unmistakable
authority on squat, powerful legs. He was speaking on his
cell phone but finished the call as he arrived. He motioned
for Langdon to enter.
“I am Bezu Fache,” he announced as Langdon pushed
through the revolving door. “Captain of the Central
Directorate Judicial Police.” His tone was fitting—a
guttural rumble... like a gathering storm...

5. Possible Problems
...The man was stocky and dark, almost Neanderthal,
dressed in a dark double­breasted suit that strained to cover
his wide shoulders. He advanced with unmistakable
authority on squat, powerful legs. He was speaking on his
cell phone but finished the call as he arrived. He motioned
for Langdon to enter.
“I am Bezu Fache,” he announced as Langdon pushed
through the revolving door. “Captain of the Central
Directorate Judicial Police.” His tone was fitting—a
guttural rumble... like a gathering storm...
One entity → several names
Non-entities
Also: one name → several entities
Anonymous references

6. Co-occurence Detection
...The man was stocky and dark, almost Neanderthal,
dressed in a dark double­breasted suit that strained to cover
his wide shoulders. He advanced with unmistakable
authority on squat, powerful legs. He was speaking on his
cell phone but finished the call as he arrived. He motioned
for Langdon to enter.
“I am Bezu Fache,” he announced as Langdon pushed
through the revolving door. “Captain of the Central
Directorate Judicial Police.” His tone was fitting—a
guttural rumble... like a gathering storm...

7. Network Construction
Connection
thickness—
number of co-
occurrences
Circle area—
number of
references to the
entity in the text

8. Detecting Clusters
Blue: “detective”
(Robert, Sophie,
police, Paris)
Orange: “mystic”
(Leigh, Grail, Bible,
Britain)
Green: “conspiracy”
(Silas, Teacher,
Jesuits, Castel
Gandolfo)

9. Betweenness
Label size—
betweenness
centrality (BC).
The higher is BC
—the more
“central” is the
character / place
Highest BC:
Robert, Sophie,
Leigh, Bishop
Aringarosa, Holy
Grail, Captain
Fache, Silas,
Jesus, Leonardo,
Church, and
Sauniere

10. 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!