Film Script Analysis -- Case Study 10 -- Badlands.¶

-----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 [5]:

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

In [7]:

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

Badlands

In [9]:

##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 [11]:

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

Out[11]:

	Scene_Names	Scene_action	Scene_Characters	Scene_Dialogue	Contents
55	EXT. RIVER	Kit is fishing with his crude net. He stops br...	[HOLLY, KIT]	[For days afterward I lived in dread. At times...	Kit is fishing with his crude net. He stops b...
43	EXT. TREEHOUSE SERIES OF ANGLES	Details of Kit and Holly building the treehous...	[HOLLY]	[We planned a huge network of tunnels under th...	Details of Kit and Holly building the treehou...
110	EXT. MOUNTAINS THEIR POV	Mountains in the distance closer now than ever...	None	None	Mountains in the distance closer now than eve...
108	EXT. CADILLAC ON PLAINS DAY	Two shots of the Cadillac heading across the B...	[HOLLY]	[Kit knew the end was coming. He wondered if h...	Two shots of the Cadillac heading across the ...
5	EXT. FRONT LAWN		[KIT, HOLLY, KIT, HOLLY, KIT, HOLLY, KIT]	[Hi, I am Kit. I am not keeping you from anyth...	KIT Hi, I am Kit. I am not keeping you from a...
16	EXT. FEEDLOT	Kit steps on top of a dead cow, as though to c...	[HOLLY]	[For instance,. he faked his signature wheneve...	Kit steps on top of a dead cow, as though to ...
85	EXT. BADLANDS EXTREME LONG SHOT	The car races across the open prairie.	[HOLLY]	[Fearing there had be roadblocks on the highwa...	The car races across the open prairie. HOLLY...
33	EXT. HOLLY'S HOUSE NIGHT	The house by night.	None	None	The house by night.
61	INT. BEDROOM	Cato falls on his bed and rolls over to face K...	[HOLLY, KIT]	[Kit never let on why he had shot Cato. He sai...	Cato falls on his bed and rolls over to face ...
18	EXT. RIVERBANK	Kit puts on his jacket and heads down a riverb...	[HOLLY, KIT, HOLLY, KIT, HOLLY, KIT, HOLLY, KI...	[Did it go the way it 'uz supposed to?, Yeah.,...	Kit puts on his jacket and heads down a river...

In [10]:

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

Out[10]:

(125, 5)

In [12]:

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

Out[12]:

	characters	Character_dialogue
220	CATO	I will catch up with you.
224	HOLLY	Hey, you are throwing big ones Suddenly, Kit s...
125	KIT	You know, before I met her, nobody could ask m...
237	HOLLY	Just looking.
457	KIT	You ever had to open it up like that before?
450	KIT	You are going to give me a cauliflower ear, Sh...
178	HOLLY	They hadn't found but one set of bones in the ...
359	HOLLY	Through desert and mesa, across the endless mi...
184	HOLLY	One of the chickens died last night. She pats ...
115	HOLLY	Don't call me stupid.

2. Character appearance plots¶

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

In [14]:

movie_characters = ext.extract_character_plot()

In [15]:

dia = scene_dialogues(df_film, film)

In [16]:

df_xter_app = dia.character_appearances(movie_characters)

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

In [17]:

#Movie characters 
print('Movie Characters:  \n', movie_characters)

Movie Characters:  
 ['KIT', 'HOLLY', 'DEPUTY', 'CATO', 'FATHER', 'RICH MAN', 'BOY', 'SHERIFF', 'GIRL', 'ROUGHNECK', 'CLERK', 'VISITOR', 'TROOPER', 'BUM', 'MECHANIC']

In [18]:

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

In [19]:

dia.scene_dialogue_plot(df_1st_count)

In [20]:

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

In [21]:

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

In [22]:

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

In [24]:

df_3_count, df_3_dialogue = dia.xter_count_perscene(['KIT', 'DEPUTY'])
dia.scene_dialogue_plot(df_3_count)

3. Character Interaction Mapping¶

In [25]:

interact = interaction(df_film, film)

In [26]:

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 [32]:

# 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 [29]:

interact.character_interaction_plot(G, page_ranked_nodes)

Let break it down to the Top 10 Characters¶

In [30]:

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

In [33]:

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 [34]:

xtr = character_mentions(df_film, movie_characters, film)

In [35]:

xter_mentions = xtr.most_mentioned()

Lets the check the appearance of the 5 most mentioned characters in the Movie¶

In [36]:

xtr.top_xters_mentions(xter_mentions, 5)

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

In [37]:

print(movie_characters)

['KIT', 'HOLLY', 'DEPUTY', 'CATO', 'FATHER', 'RICH MAN', 'BOY', 'SHERIFF', 'GIRL', 'ROUGHNECK', 'CLERK', 'VISITOR', 'TROOPER', 'BUM', 'MECHANIC']

In [39]:

df_kit = xtr.talk_about_xters(df_film_dialogue, 'KIT')

In [40]:

df_hol = xtr.talk_about_xters(df_film_dialogue, 'HOLLY')

In [41]:

df_dep = xtr.talk_about_xters(df_film_dialogue, 'DEPUTY')

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

In [42]:

df_kt = xtr.most_talked_with('KIT')

In [43]:

df_hol = xtr.most_talked_with(movie_characters[1])

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 [44]:

etn = emotions_sentiments(df_film, film)

In [45]:

df_film_sentiment = etn.film_sentiment('darkslategray')

In [46]:

df_film_emotion = etn.film_emotional_arc()

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

In [47]:

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

Lets check emotional arcs for specific characters¶

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

In [48]:

df_kit_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'KIT')

In [49]:

df_hol_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'HOLLY')

In [50]:

df_dep_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'DEPUTY')

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 [51]:

info = scene_info_plots(df_film, film)

In [52]:

info.extract_scene_locations()

In [53]:

info.pie_plots()

5. Gender Distribution in the Movie¶

In [54]:

gd = gender(movie_characters, film)

In [55]:

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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