Forrest Gump

Film Information

Forrest Gump is a 1994 American comedy-drama film directed by Robert Zemeckis and written by Eric Roth. It is based on the 1986 novel of the same name by Winston Groom and stars Tom Hanks, Robin Wright, Gary Sinise, Mykelti Williamson and Sally Field. The film follows several decades in the life of a slow-witted and kindhearted Alabama man named Forrest Gump (Hanks) and his experiences in the 20th-century United States. The film differs substantially from the novel.

All information in this section came from Wikipedia.

Clip Information

Forrest visits the grave of his wife, Jenny, delivering a letter that their son, little Forrest, wrote to her.

Abbrev	Film	Clip Start	Clip Stop	Duration
ForrestGump	Forrest Gump (1994)	02:09:19.000	02:12:57.000	218

Characteristic	Value
Format	MPEG-4
File Size	75.1 MiB
Duration	218.01
Frame Rate	23.976
Video Width	1920
Video Height	816
Video BitRate	2.8 MB/s
Audio Channels	2
Audio SamplingRate	48000
Audio BitRate	128.2 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
3.020	6.355	You died on a Saturday morning.
8.066	11.819	And I had you placed here under our tree.
16.700	21.537	And I had that house of your father's bulldozed to the ground.
25.667	27.335	Mama
28.420	33.424	always said that dying was a part of life.
36.845	39.180	I sure wish it wasn't.
41.850	45.603	Little Forrest is doing just fine.
46.938	48.022	But...
48.273	50.941	About to start school again soon,
52.778	56.571	and I make his breakfast, lunch and dinner every day.
58.741	64.746	I make sure he combs his hair and brushes his teeth every day.
67.875	70.460	Teaching him how to play Ping-Pong.
71.754	72.796	Okay.
73.047	74.214	He's really good.
74.298	76.424	Forrest, you go.
80.638	82.222	We fish a lot.
85.768	89.229	(SOBBING) And every night, we read a book. He's so smart, Jenny.
92.483	94.734	You'd be so proud of him.
96.445	97.904	I am.
98.948	102.158	He wrote you a letter.
103.744	105.787	And he says I can't read it.
105.913	109.457	I'm not supposed to, so I'll just leave it here for you.
125.516	126.850	Jenny,
132.648	135.859	I don't know if Mama was right,
137.194	138.695	or if it's Lieutenant Dan.
138.821	141.406	I don't know
141.490	145.994	if we each have a destiny,
148.038	153.168	or if we're all just floating around accidental-like on a breeze.
157.673	159.507	But I think
161.385	163.178	maybe it's both.
167.516	170.393	Maybe both is happening at the same time.
176.525	178.526	But I miss you, Jenny.
184.909	189.329	If there's anything you need, I won't be far away.

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 78 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	1.408	1.104	2.074	0.479
Timepoint	Variance	0.224	0.183	0.279	0.076
Residual	Variance	1.306	1.277	1.334	0.445

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)	0.932	0.916	0.943	78	Relative

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