Good Will Hunting

Film Information

Good Will Hunting is a 1997 American psychological drama film directed by Gus Van Sant, and written by Ben Affleck and Matt Damon. It stars Robin Williams, Damon, Affleck, Stellan Skarsgård and Minnie Driver.

All information in this section came from Wikipedia.

Clip Information

In this clip, Will, a young man, attends his first psychotherapy session with Dr. Sean Maguire. They discuss painting and Dr. Maguire’s wife.

Abbrev	Film	Clip Start	Clip Stop	Duration
GoodWill	Good Will Hunting (1997)	00:39:23.200	00:41:53.200	150

Characteristic	Value
Format	MPEG-4
File Size	48.3 MiB
Duration	150.025
Frame Rate	23.976
Video Width	1920
Video Height	1040
Video BitRate	2.6 MB/s
Audio Channels	2
Audio SamplingRate	48000
Audio BitRate	128.7 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
0.000	2.260	Hey, you know you'd be better off shoving that cigarette up your ass. It'd probably be healthier for you.
2.398	6.525	- Yeah, I know. It really gets in the way of my yoga. - You work out, huh?
6.652	9.356	- What, you lift? - Yeah.
9.487	11.480	- Uh, Nautilus? - No, free weights.
11.614	13.191	- Oh, really? - Yeah. - Free weights, huh?
13.324	15.115	- Yeah, big time. - Yeah? - Just like that.
15.242	17.531	- What do you bench? - 285. What do you bench?
19.954	22.030	You paint that?
22.165	25.580	Yeah. You paint? Do you sculpt?
25.709	27.536	- No. - Do you like art?
30.130	32.039	Do you like music?
32.173	34.212	This is a real piece of shit.
34.342	36.215	Oh. Well, tell me what you really think.
36.343	39.676	Uh, just, uh, the linear and impressionistic mix...
39.805	42.046	makes a very muddled composition.
42.182	45.965	It's also a Winslow Homer rip-off, except you got whitey, uh, rowin' the boat there.
46.102	47.975	Well, it's art, Monet. It wasn't very good.
48.103	50.724	- That's not really what concerns me though. - What concerns you?
50.856	53.263	- Just the colouring. - You know what the real bitch of it is?
53.399	55.392	It's paint-by-number.
55.526	57.981	Is it colour-by-number? Because the colours are fascinating to me.
58.112	59.689	- Aren't they, really? What about that? - You bet.
59.821	62.739	I think you're about one step away from cuttin' your fuckin' ear off.
62.866	64.941	- Really? - Oh, yeah.
65.076	68.824	Think I should move to the south of France, change my name to Vincent?
68.954	70.781	You ever heard the sayin', "any port in a storm"?
70.914	73.831	- Yeah. - Yeah. Maybe that means you.
73.958	75.334	In what way?
75.460	78.745	- Uh, maybe you're in the middle of a storm, a big fuckin' storm. - Yeah, maybe.
78.879	81.880	The sky's fallin' on your head. The waves are crashin' over your little boat.
82.007	84.213	The oars are about to snap.
84.342	86.002	You just piss in you pants. You're cryin' for the harbour.
86.135	88.424	So maybe you do what you gotta do to get out.
88.554	90.879	You know, maybe you became a psychologist.
91.014	94.597	Bingo. That's it. Let me do my job now. You start with me. Come on.
96.978	100.726	- Maybe you married the wrong woman. - Maybe you should watch your mouth!
102.482	104.807	Watch it right there, chief, all right?
107.403	109.277	Ah.
113.408	115.531	Mmm, that's it, isn't it?
115.660	117.902	You married the wrong woman.
119.455	123.784	What happened? What, did she leave you?
123.917	126.206	Was she, you know, banging some other guy?
128.296	131.711	If you ever disrespect my wife again, I will end you.
131.841	133.880	I will fuckin' end you.
134.009	135.800	Got that, chief?
137.971	141.802	- Time's up. - Yeah.

Holistic Ratings

A total of 76 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 76 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	1.111	0.858	1.645	0.417
Timepoint	Variance	0.613	0.495	0.802	0.230
Residual	Variance	0.942	0.917	0.969	0.353

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)	0.981	0.976	0.985	76	Relative

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