Little Miss Sunshine

Film Information

Little Miss Sunshine is a 2006 American tragicomedy road film and the feature film directorial debut of the husband–wife team of Jonathan Dayton and Valerie Faris. The screenplay was written by first-time writer Michael Arndt. The film stars Greg Kinnear, Steve Carell, Toni Collette, Paul Dano, Abigail Breslin, and Alan Arkin, as members of a family taking the youngest to compete in a child beauty pageant.

All information in this section came from Wikipedia.

Clip Information

This scene shows a conversation at the dinner table. Frank recently came to live with the family of his sister, Sheryl, after attempting suicide. At the table are Sheryl, her brother Frank, her husband Richard, her children Dwayne and Olive, and Richard’s father Edwin.

Abbrev	Film	Clip Start	Clip Stop	Duration
LittleMiss	Little Miss Sunshine (2006)	00:13:10.000	00:15:20.000	130

Characteristic	Value
Format	MPEG-4
File Size	91.5 MiB
Duration	130.005
Frame Rate	23.976
Video Width	1920
Video Height	796
Video BitRate	5.6 MB/s
Audio Channels	6
Audio SamplingRate	48000
Audio BitRate	341.1 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	2.395	Why did you want to kill yourself?
2.495	4.994	No, don't answer the question, Frank.
5.104	7.534	- Richard! Richard! - He's not gonna answer the question. Frank.
7.634	10.074	- I wanted to kill myself- - Don't listen to him.
10.174	12.304	- I was very unhappy. - He's sick in his head.
12.404	14.804	- Richard! - I'm sorry! I don't think it's an appropriate conversation...
14.914	17.074	- for a seven-year-old. - She's gonna find out anyway.
17.184	21.084	- Okay. - Go on, Frank.
24.284	26.514	Why were you unhappy?
26.624	30.084	Um, well, there are a lot of reasons.
30.194	35.103	Mainly, though, I fell in love with someone who didn't love me back.
35.203	36.763	Who?
36.873	40.103	One of my grad students. I was very much in love with him.
40.213	42.583	Him?
42.683	47.013	It was a boy? You fell in love with a boy?
47.123	50.243	- Yes, I did. Very much so. - That's silly.
50.353	53.053	You're right. It was silly. It was very, very silly.
53.163	56.223	- There's another word for it. - Dad.
56.333	60.233	- So, that's when you tried to kill yourself? - Well, no.
60.333	63.892	The boy that I was in love with fell in love with another man- Larry Sugarman.
64.002	66.262	- Who's Larry Sugarman? - Larry Sugarman is, perhaps...
66.372	69.532	the second most highly regarded Proust scholar in the U.S.
69.642	73.202	- Who's number one? - That would be me, Rich.
73.312	75.712	- Really? - Mm-hmm.
75.812	77.942	So that's when.
78.052	80.412	No. What happened was I was a bit upset...
80.522	82.812	so I said some things that I shouldn't have said...
82.922	85.052	and I did some things that I shouldn't have done...
85.152	87.212	and subsequently I was fired from my job...
87.322	90.491	and forced to move out of my apartment and move into a motel.
94.261	96.751	- And that's when you tried to- - Well, no.
96.871	99.061	Actually, all of that was okay.
99.171	103.761	What happened was two days ago the MacArthur Foundation, in its infinite wisdom...
103.871	108.071	awarded a genius grant to Larry Sugarman.
108.181	110.441	And that's when I-
110.551	113.141	Decided to check out early.
113.251	116.811	Yes. Yes. And I failed at that as well.
116.921	120.910	Olive, the important thing to understand here...
121.020	123.250	is that Uncle Frank gave up on himself.
123.360	127.290	He made a series of foolish choices- I'm sorry- and he gave up on himself...
127.400	129.960	which is something winners never do.

Holistic Ratings

A total of 78 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 75 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	1.547	1.183	2.275	0.704
Timepoint	Variance	0.071	0.057	0.101	0.032
Residual	Variance	0.578	0.561	0.596	0.263

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)	0.91	0.881	0.929	75	Relative

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