Suspense...
Chelsea Davis, Abigail Droge, Tasha Eccles, Laura Eidem, Morgan Frank, Erik Fredner, J.D. Porter, Andrew Shephard, Oleg Sobchuk, Hannah Walser, and Mark Algee-Hewitt
Key Questions
Recap from last time
Native Son MDWs (April 2014)
Tracking features in suspenseful moments
Overview
Tagging Basics
Our tags line up
Novel Tagging Statistics
Suspense MDWs | Unsuspense MDWs | ||||
Short story | Novel corpus | Novel tags | Short story | Novel corpus | Novel tags |
upon | lorenzo | cairo | governor | utopia | colonel |
eyes | digger | tom | masters | amelia | major |
thought | carlyle | chamber | rhoda | barton | pyramid |
saw | gran | temple | robert | ferris | dinner |
horror | geoffrey | breath | loved | edith | discussed |
sound | randy | fast | married | emerson | whoever |
half | ryan | instant | dinner | sax | peter |
dead | monroe | stranger | pretty | tuileries | inherent |
hope | vic | van | lived | cecil | presumably |
descent | greenwood | shoot | liked | dublin | relations |
How closely do our three MDW groups track suspense in a novel?
Topic Modeling
Topics vs. Semantic Fields
Violence (hand-generated)
murder
poison
strangle
assassin
rifle
assault
deadly
wounded
torture [... 55 total items]
Assault (topic modeled)
back
ran
toward
shouted
head
screamed
began
around
feet [... 36 total items]
Example Topics
Suspense Detector
Maltese Falcon Results
Negative Presence and Suspense
Neural Network
Given the subjective nature of our identification/tagging process, we turned to the best computational model to duplicate this subjective process: a Neural Network
Our features seem to correlate with suspense, BUT suspense is complex
Could we use an equally complex model to combine our features and train it to recognize the same suspenseful passages as we did?
Neural Net: Schematic Diagram
Output: sections we identified as suspenseful or unsuspensful through our tagging experiment
Input: All topic model fields, MDW fields (except those extracted from tags) and age of acquisition scores - all scored in .5% slices of each text
neural 3
Neural Network: Success Rate
Neural Network: Feature Loadings
Neural Network: Feature Loadings
Automatically Tagged Text
<highSuspense3> the coroner did acquit me of all blame he had even complimented her on her presence of mind and courage she remembered for an inquest it couldnt have gone better and mrs hamilton had been kindness itself to her only hugo but she wouldnt think of hugo suddenly in spite of the heat in the carriage she shivered and wished she wasnt going to the sea a picture rose clearly before her mind cyrils head bobbing up and down swimming to the rock up and down up and down and herself swimming in easy practised strokes after him cleaving her way through the water but knowing only too surely that she wouldnt be in time the sea its deep warm bluemornings spent lying out on the sands hugo hugo who had said he loved her she must not think of hugo she opened her eyes and frowned across at the man opposite her a tall man with a brown face light eyes set rather close together and an arrogant almost cruel mouth she thought to herself i bet hes been to some interesting parts of the world and seen some interesting things iii philip lombard summing up the girl opposite in a mere flash of his quick moving eyes thought to himself quite attractive a bit school mistressy perhaps a cool customer he should imagine and one who could hold her own in love or war hed rather like to take her on he frowned no cut out all that kind of stuff this was business hed got to keep his mind on the job what exactly was up he wondered that little jew </highSuspense3>
Automatically Tagged Text
Agatha Christie: And Then There Were None - Tagged by Neural Network
Automatically Tagged Text
William Beckford: Vathek - Tagged by Neural Network
Automatically Tagged Text
William Gibson: Neuromancer - Tagged by Neural Network
Unsuspenseful Texts
Carlyle Sartor Resartus and Roth American Pastoral
What’s Next?
Appendix: Extra Material
Tagged Short Stories
Expansion
Abigail’s tags
Chelsea’s tags