Film Script Analysis -- Case Study 18 -- Inventing the Abbots¶

-----20 Movies Script Visualizations will be done------This could be done for the 1000+ movie script I segmented--- I just randomly chose 20 movie scripts from the segmented movies....

The aim of this project is to provide detailed insights into different movies analyzed focusing on the characters, their dialogues, scene locations, emotional and sentiment analysis of the whole movie and the individual characters, character's interaction with one another and finally gender distribution in the each movie analyzed.¶

To execute this project, the following objectives were done:¶

Web scraping of the movie scripts (Over 1000+ movies were scraped from imsdb website)
Movies segmentation into Scenes --> Scene Location, Scene Action/Description, Scene Dialogues, Scene Characters (All the movies scraped were segmented except those that do not follow the "Screenplay format i.e. INT / EXT)"
Character extraction and appearances plot ---> Here, characters were plotted based on how many times they appeared and spoke in each scene and across the movie.
Character Interaction Mapping --> We mapped out the connection between all the characters in the movie and also the interaction between the Top 10 characters in the movie.
Here, we looked at the Most mentioned character based on the Scene dialogues and also the characters each character mention the most in their conversation.
Similar to Number 5., Here looked at who a specific character talks with the most in the Movie.
Emotional and Sentiment Analysis across the whole movie and for each individual character, However for this project we limited it to only the Top 10 characters. ---> This gives us the character's emotion when he/she appears in the movie.
Additional Scene Informations --> Exact Scene Locations, Scenes with dialogs and no dialogs, Scenes that occurred during the Day or in the Night, Scenes location based on Outdoor or Indoor appearances.
Gender Distribution in the movie

*(python Code) Modules for this project: imsbd_moviescript_scraper_AND_Scene_Segmentation.py, dialogue_appearance.py, characters_extract.py, xter_interaction.py, characters_mt.py, emotions.py, movie_info.py, gend_distribution_plot.py*

Tools: Python libraries

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In [1]:

#Import all the necessary python modules needed for this analysis

from characters_extract import extract_characters
from dialogue_appearance import scene_dialogues
from xter_interaction import interaction
from emotions import emotions_sentiments
from characters_mt import character_mentions
from gend_distribution_plot import gender
from movie_info import scene_info_plots

In [2]:

import glob
import random
import secrets
import re

import cufflinks as cf
import networkx as net
import itertools
# plotly
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import pandas as pd
import chart_studio.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
import plotly.express as px

1. Load in the Segmented Movie scripts stored in .pkl format¶

In [3]:

films = []
for f in glob.glob('Films/*'):
    film_name = re.sub(r'.pkl|Films\\', '', f)
    films.append(film_name)

In [4]:

#Number of Films we segmented into scenes, scene_actions, characters, and characters dialogue
print('Number of films available for Analysis:  ', len(films), ' Movies')

Number of films available for Analysis:   1037  Movies

In [13]:

#Random 10 films from the 1000 films scraped from the internet
films_list = random.sample(films, 10)

In [14]:

#Randomly select film to analyze
film = secrets.choice(films_list)
print(film)

Inventing-the-Abbotts

In [15]:

##load the scenes, dialogues, characters into dataframe

df_film = pd.read_pickle('Films/' + film + '.pkl')
df_film_dialogue = pd.read_pickle('Dialogues/' + film + '.pkl')
df_film_characters = pd.read_pickle('Characters/' + film + '.pkl')

In [17]:

#Randomly generate 10 scenes from the movie script
df_film.sample(10)

Out[17]:

	Scene_Names	Scene_action	Scene_Characters	Scene_Dialogue	Contents
69	EXT. HOLT HOME GARAGE NIGHT 71	Doug and Jacey play another hard fought game o...	[DOUG, JACEY, DOUG, JACEY, DOUG, JACEY, DOUG, ...	[I am gonna go to Penn like you. Jacey snorts ...	Doug and Jacey play another hard fought game ...
116	INT. HOLT HOME JACEY'S BEDROOM LATER THAT DA...	Jacey haphazardly packs his suitcases. His emo...	[HELEN, JACEY, HELEN]	[Why spend all night and half the day tomorrow...	Jacey haphazardly packs his suitcases. His em...
67	INT. ABBOTT HOME TENT NIGHT ...	The party is well underway, it's a younger cro...	[PAMELA, DOUG, PAMELA, DOUG, PAMELA, DOUG, PAM...	[How long have you been here?, A while., Are y...	The party is well underway, it's a younger cr...
106	EXT. HOLT HOME ...	Pamela dashes back to her car. 112 ...	[JACEY, JACEY]	[Jesus... Helen closes the front door and turn...	Pamela dashes back to her car. 112 ...
141	INT. REED HALL LOBBY AND STAIRS ...		[THAT NIGHT, JACEY, DOUG, JACEY, DOUG, JACEY, ...	[Doug enters the dormitory and starts up the s...	THAT NIGHT Doug enters the dormitory and star...
109	EXT. HOLT HOME STREET FRONT YARD DAY (MOMEN...		[JACEY, PAMELA, JACEY, PAMELA, DOUG, PAMELA, D...	[How is she?, She's all right., Can I see her?...	LATER Pam's convertible barely comes to a hal...
35	EXT. BUS DEPOT Next DAY 37	Helen stands on the curb waving goodbye as the...	None	None	Helen stands on the curb waving goodbye as th...
79	EXT. MRS. PORTER'S HOUSE DAY ...	Helen sits in a lawn chair in the back yard we...	[HELEN]	[That board doesn't look straight, Jacey. Come...	Helen sits in a lawn chair in the back yard w...
49	EXT. ABBOTT HOME WINDOW NIGHT ...	Doug squeezes out of the small window, his leg...	None	None	Doug squeezes out of the small window, his le...
24	EXT. DOWNTOWN HALEY INSIDE CADILLAC ...	with Lloyd behind the wheel, Joan in the front...	[LLOYD, ELEANOR, LLOYD, ELEANOR, LLOYD]	[Stay away from him., Who?, Jacey., Why?, Beca...	with Lloyd behind the wheel, Joan in the fron...

In [16]:

#check how many scenes the movie script has
df_film.shape

Out[16]:

(167, 5)

In [18]:

#Randomly select characters and their corresponding dialogues
df_film_dialogue.sample(10)

Out[18]:

	characters	Character_dialogue
204	PAMELA	I thought you came over to ask me out?
382	JACEY	Like hell you are.
832	PAMELA	I can't... do this.
560	DOUG	Front yard or back yard?
1021	DOUG	How can I what?
57	LLOYD	Fill 'er up. Hitest. Jacey sets the pump noz...
700	LLOYD	I have plans for my daughters, Mr. Holt, and t...
251	ALICE	It comes in a redandwhite tube just like a tub...
443	HELEN	Isn't that Pamela Abbott? In the distance Pame...
937	HELEN	Too much noise, please. Let's finish up. Keep ...

2. Character appearance plots¶

In [19]:

ext = extract_characters(df_film, df_film_dialogue, df_film_characters, film)

In [20]:

movie_characters = ext.extract_character_plot()

In [21]:

dia = scene_dialogues(df_film, film)

In [22]:

df_xter_app = dia.character_appearances(movie_characters)

Lets check the appearance of the top 3 characters across the Movie¶

In [23]:

#Movie characters....here, "NIGHT" is not a character
print('Movie Characters:  \n', movie_characters)

Movie Characters:  
 ['DOUG', 'JACEY', 'PAMELA', 'HELEN', 'ALICE', 'LLOYD', 'ELEANOR', 'JOAN', 'VICTOR', 'CLERK', 'WEBB', 'ACTION', 'PETER', 'STEVE', 'COED', 'NIGHT']

In [24]:

df_1st_count, df_1st_dialogue = dia.xter_count_perscene(movie_characters[0])

In [25]:

dia.scene_dialogue_plot(df_1st_count)

In [26]:

df_2nd_count, df_2nd_dialogue = dia.xter_count_perscene(movie_characters[1])
dia.scene_dialogue_plot(df_2nd_count)

In [27]:

df_third_count, df_third_dialogue = dia.xter_count_perscene(movie_characters[2])
dia.scene_dialogue_plot(df_third_count)

In [28]:

df_2_count, df_2_dialogue = dia.xter_count_perscene(movie_characters[:2])
dia.scene_dialogue_plot(df_2_count)

In [29]:

df_3_count, df_3_dialogue = dia.xter_count_perscene(movie_characters[1:3])
dia.scene_dialogue_plot(df_3_count)

3. Character Interaction Mapping¶

In [30]:

interact = interaction(df_film, film)

In [31]:

graph_list = interact.character_interaction()

Note the Net.MultiGraph code below will throw an error, BUT this does not stop it from displaying the Character Network Multigraph figure,¶

To run it, remove the hashtags i.e. comment it out¶

In [38]:

# G = net.MultiGraph()
# for scene in graph_list:
#     nodes = list(itertools.combinations(scene,2))
#     for pair in nodes:
#         G.add_edges_from([pair])
# page_ranked_nodes = net.pagerank_numpy(G,0.95)
# net.enumerate_all_cliques(G)
# between_nodes = net.betweenness_centrality(G, normalized=True, endpoints=True)

In [33]:

interact.character_interaction_plot(G, page_ranked_nodes)

Let break it down to the Top 10 Characters¶

In [34]:

#Remember to Re-run the above multigraph code aafter running this code line
graph_list = interact.top10_character_interaction(movie_characters[:10])

In [37]:

interact.character_interaction_plot(G, page_ranked_nodes)

4. Characters mentioned the most in Scene Dialogues (Most-talked about characters in the movie.......) and who each character mention the most in their specific dialogue¶

In [39]:

xtr = character_mentions(df_film, movie_characters, film)

In [40]:

xter_mentions = xtr.most_mentioned()

Lets the check the appearance of the 3 most mentioned characters in the Movie¶

In [41]:

xtr.top_xters_mentions(xter_mentions, 3)

Lets check who specific characters mention the most in their dialogues in the movie¶

In [42]:

print(movie_characters)

['DOUG', 'JACEY', 'PAMELA', 'HELEN', 'ALICE', 'LLOYD', 'ELEANOR', 'JOAN', 'VICTOR', 'CLERK', 'WEBB', 'ACTION', 'PETER', 'STEVE', 'COED', 'NIGHT']

In [43]:

df_edd = xtr.talk_about_xters(df_film_dialogue, 'DOUG')

In [44]:

df_jac = xtr.talk_about_xters(df_film_dialogue, 'JACEY')

In [45]:

df_pam = xtr.talk_about_xters(df_film_dialogue, 'PAMELA')

Lets check who specific characters talk with the most in the movie¶

In [46]:

df_dg = xtr.most_talked_with('DOUG')

In [47]:

df_pam = xtr.most_talked_with(movie_characters[2])

We could continue to check this for every character but I will pause here....¶

5. Emotional Arcs and Sentiment Analysis across the Movie and for each characters¶

In [48]:

etn = emotions_sentiments(df_film, film)

In [49]:

df_film_sentiment = etn.film_sentiment('darkslategrey')

In [50]:

df_film_emotion = etn.film_emotional_arc()

Lets check the emotional content for the Top 10 characters in the Movie¶

In [51]:

df_top10_emotions =  etn.emotional_content_plot(df_film_dialogue, movie_characters, 11)

Lets check emotional arcs for specific characters across the scenes¶

Emotions at scenes where the character is not present is set to ZERO¶

In [52]:

df_dg_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'DOUG')

In [53]:

df_pam_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'PAMELA')

In [54]:

df_jcy_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'JACEY')

As usual, this can be done for all characters in the movie, but we will pause here....¶

4. Movie additional information: Exact Scene location, Indoor/Outdoor Scenes percent, Time of Day occurences in the movie....¶

In [55]:

info = scene_info_plots(df_film, film)

In [56]:

info.extract_scene_locations()

In [57]:

info.pie_plots()

5. Gender Distribution in the Movie¶

In [58]:

gd = gender(movie_characters, film)

In [59]:

df_gender = gd.gender_types(px.colors.sequential.Viridis)

[nltk_data] Downloading package names to C:\Users\Adeboye
[nltk_data]     Adeniyi\AppData\Roaming\nltk_data...
[nltk_data]   Package names is already up-to-date!

This should give a screen writer a broad overview of the underlying insights that could be discovered from an existing screen play or movie script.¶

Thanks for visualizing this extensive film script analysis...¶

Remember all codes for this analysis are already in python modules....¶

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