  Recognizing the flow of time in a story is a crucial aspect of understanding
it. Prior work related to time has primarily focused on identifying temporal
expressions or relative sequencing of events, but here we propose
computationally annotating each line of a book with wall clock times, even in
the absence of explicit time-descriptive phrases. To do so, we construct a data
set of hourly time phrases from 52,183 fictional books. We then construct a
time-of-day classification model that achieves an average error of 2.27 hours.
Furthermore, we show that by analyzing a book in whole using dynamic
programming of breakpoints, we can roughly partition a book into segments that
each correspond to a particular time-of-day. This approach improves upon
baselines by over two hours. Finally, we apply our model to a corpus of
literature categorized by different periods in history, to show interesting
trends of hourly activity throughout the past. Among several observations we
find that the fraction of events taking place past 10 P.M jumps past 1880 -
coincident with the advent of the electric light bulb and city lights.
