Catch Me If You Can

Film Information

Catch Me If You Can is a 2002 American biographical crime comedy-drama film directed and produced by Steven Spielberg and starring Leonardo DiCaprio and Tom Hanks with Christopher Walken, Martin Sheen, Nathalie Baye, Amy Adams and James Brolin in supporting roles. The screenplay by Jeff Nathanson is based on the “autobiography” of Frank Abagnale, who claims that before his 19th birthday, he successfully performed cons worth millions of dollars by posing as a Pan American World Airways pilot, a Georgia doctor, and a Louisiana parish prosecutor. The truth of his story is questionable.

All information in this section came from Wikipedia.

Clip Information

Frank, a young airline pilot, is taking his father out to lunch at a fancy restaurant.

Abbrev	Film	Clip Start	Clip Stop	Duration
CatchMe	Catch Me If You Can (2002)	00:44:32.250	00:49:06.050	274

Characteristic	Value
Format	MPEG-4
File Size	126.2 MiB
Duration	273.816
Frame Rate	23.976
Video Width	1920
Video Height	1040
Video BitRate	3.5 MB/s
Audio Channels	6
Audio SamplingRate	48000
Audio BitRate	394.3 kB/s

Subtitles

The following wordcloud shows the words used in this clip, scaled by number of occurrences and colored by sentiment (orange = negative, green = positive, grey = neutral or unsure). Note that the words have been stemmed and lemmatized and stopwords have been removed.

The table below shows all subtitles in this clip with the start and stop time of each subtitle’s appearance in seconds.

Start	End	Subtitle
0.383	6.054	(THREE-PIECE COMBO PLAYS SOFT JAZZ)
6.055	7.389	Daddy!
7.390	8.791	(CHUCKLING)
10.193	12.194	My son, the birdman.
12.195	13.229	Some uniform, Frank.
13.230	15.231	What do you think?
15.232	16.898	Nice.
16.899	18.567	Sit down.
18.568	20.168	(CLEARS THROAT)
21.571	23.739	So, Dad... Daddy, have you gotten the postcards?
23.740	25.073	Of course.
25.074	27.376	This fork is ice cold.
27.377	31.046	No, no, Dad, th-that's a chilled salad fork.
31.047	35.717	(WHISPERING) It's a fancy restaurant, you know.
35.718	39.087	(SIGHING)
39.088	41.524	Well, here...
41.525	43.459	I-I got you something.
43.460	45.194	What's that?
45.195	47.396	Open it.
51.201	53.035	You know what those are, right?
53.036	55.037	Those are the keys
55.038	58.040	to a 1965 Cadillac DeVille convertible.
58.041	59.942	Brand-new, Dad.
59.943	62.879	Red with white interior
62.880	67.049	split seats, air conditioning, the works.
67.050	68.317	Are you giving me a Cadillac?
68.318	70.052	Yeah. I'm giving you a Cadillac.
70.053	71.821	Dad, sh-she's parked downstairs.
71.822	73.389	When we're done eating lunch
73.390	76.291	why don't you, you know, drive on over to Mom's house
76.292	78.460	pick her up, take a little joyride?
78.461	80.262	Do you know what would happen
80.263	81.564	if the IRS found out
81.565	85.067	I was driving around in a new coupe?
85.068	87.369	I took the train here, Frank.
87.370	89.739	I'm taking the train home.
91.408	93.442	All right.
98.448	101.551	I have plenty of money.
101.552	103.953	You know, if you ever, ever need anything...
103.954	105.287	You worried?
105.288	107.823	About me?
107.824	109.458	No, I'm not... I'm not worried.
109.459	111.895	You think I can't buy my own car?
111.896	116.465	Two mice fell in a bucket of cream, Frank.
116.466	118.333	Which one am I?
118.334	121.203	You're that second mouse.
122.839	126.475	I went by the store today.
126.476	128.444	I had to close the store for awhile.
128.445	130.012	It's all about timing, Frank.
130.013	131.614	The goddamn government knows that.
131.615	133.215	They hit you when you're down.
133.216	134.584	I wasn't going to let them
134.585	136.151	take it from me, so I just...
136.152	139.255	shut the doors myself, called their bluff.
143.827	145.995	Sooner or later, they'll forget about me.
145.996	148.430	I understand, I...
148.431	151.834	Have you told Ma?
153.503	158.040	She's so stubborn, your mother.
160.677	162.011	Don't worry.
162.012	164.379	I'm not going to let her go without a fight.
164.380	165.615	I been fighting for us...
165.616	167.249	(GASPS QUIETLY)
168.351	170.052	Dad?
172.188	174.790	Since the day we... we met.
174.791	176.659	Daddy, out of all those men
176.660	180.129	you were the one that took her home, remember that.
180.130	183.565	200 men, sitting in that tiny social hall
183.566	185.767	watching her dance.
188.038	190.539	What was the name of that town?
190.540	191.874	Montrichard, Dad.
191.875	193.375	Yeah.
193.376	196.211	I didn't speak a word of French
196.212	199.682	and six weeks later, she was my wi...
199.683	202.685	She's your wife.
202.686	206.956	My son bought me a Cadillac today.
206.957	210.993	I think that calls for a toast.
210.994	212.527	(SOFT JAZZ PLAYING)
215.498	218.567	(INHALING DEEPLY)
218.568	221.904	To the best damn pilot in the sky.
221.905	223.605	It's not what you think.
223.606	226.275	I'm just a copilot.
226.276	229.945	You see these people staring at you?
233.083	234.917	These are the most powerful people
234.918	236.552	in New York City
236.553	238.821	and they keep peeking over their shoulders
238.822	240.823	wondering where you're going tonight.
240.824	242.291	Where you going, Frank?
242.292	245.995	Dad, nobody's staring at me.
245.996	248.364	Some place exotic?
250.266	253.102	Just tell me where you're going.
254.637	257.006	Los An... Hollywood.
257.007	259.674	Hollywood.
261.611	264.446	(SNIFFLES)
264.447	266.615	(WHISPERING) The rest of us...
266.616	269.484	really are suckers.

Holistic Ratings

A total of 79 participants watched this film clip and then provided holistic ratings on how the entire clip made them feel. These holistic ratings were completed using five Positive Affect items (i.e., alert, determined, enthusiastic, excited, inspired) and five Negative Affect items (i.e., afraid, distressed, nervous, scared, upset), each rated on an ordinal scale from 0 to 4. The plot below shows the

Dynamic Ratings

A total of 79 participants watched this film clip and used the CARMA software to provide continuous (i.e., second-by-second) ratings of how it made them feel. These continuous ratings were made on a single emotional valence scale ranging from -4 (very negative) to 4 (very positive).

Time Series

We can plot the distribution of all valence ratings per second of the film clip to get a sense of how its emotional tone changes over time. The solid black line represents the mean of all ratings and the yellow, green, and purple ribbons represent the central 50%, 70%, and 90% of the ratings, respectively.

Inter-Rater Reliability

A Bayesian generalizability study was used to decompose the variance in ratings of this video clip into the following components: timepoint variance (in average ratings of each second, across raters), rater variance (in average ratings from each rater, across seconds), and residual variance (including second-by-rater interactions and measurement error). The lower and upper columns in the table below represent the boundaries of the 95% equal-tail credible interval. Note that we dropped the first 10 seconds of each clip (as rater “warmup” time).

Component	Term	Estimate	Lower	Upper	Percent
Rater	Variance	0.500	0.384	0.720	0.224
Timepoint	Variance	0.700	0.597	0.851	0.314
Residual	Variance	1.028	1.006	1.046	0.461

From these variance components, we can estimate inter-rater reliability of the ratings. There are many formulations of the two-way intraclass correlation (ICC), but the most relevant to our purposes here is the balanced average-measures consistency formulation or ICC(C,k).

Term	Estimate	Lower	Upper	Raters	Error
ICC(C,k)	0.982	0.979	0.985	79	Relative

Below, we can also visualize the posterior distributions of each of these parameters. Values with higher posterior density are more probable.