Film Script Analysis -- Case Study 9 -- Alien vs Predator¶

-----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 [8]:

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

Alien-vs.-Predator

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 [10]:

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

Out[10]:

	Scene_Names	Scene_action	Scene_Characters	Scene_Dialogue	Contents
127	INT. RYUSHI STATION AUTOSHOP CRAWLER CABIN	THOMAS is laidout in the back seat of the craw...	[GUTTIEREZ]	[Rob? We got problems. ]	THOMAS is laidout in the back seat of the cra...
149	INT. RYUSHI STATION COMMAND CENTER	Everybody in admin looks tense.	[HIROKO]	[Three... ]	Everybody in admin looks tense. HIROKO Three...
248	EXT. SWAMP DAWN	The sun begins to rise as HIROKO exits the hel...	[HIROKO, BROKEN TUSK, VOICE, HIROKO]	[Hold on. This is gonna hurt some. HIROKO get...	The sun begins to rise as HIROKO exits the he...
242	EXT. CHOPPER PREDAWN	The chopper clips the tree boughs, and bounces.	None	None	The chopper clips the tree boughs, and bounces.
15	INT. RYUSHI STATION EXECUTIVE OFFICE	HIROKO NOGUCHI is sweating heavily, a black fo...	[HIROKO, HIROKO, CASSIE, HIROKO, CASSIE, HIROKO]	[Fuck Holo off. The SWORDSMEN immediately fli...	HIROKO NOGUCHI is sweating heavily, a black f...
145	INT. RYUSHI STATION CRANE CAB NIGHT	HIROKO enters the cab at a run. She flips a r...	[HIROKO, HIROKO, HIROKO]	[Access panel... access panel... She finds it....	HIROKO enters the cab at a run. She flips a ...
83	INT. RYUSHI STATION COMMAND CENTER	PARSONS sits bolt upright in his chair as the ...	[PARSONS]	[Oh my God... ]	PARSONS sits bolt upright in his chair as the...
181	EXT. RYUSHI STATION BIG BERTHA NIGHT	GUTTIEREZ and DE VRIES exit the crawler in a b...	[DE VRIES]	[C amon, man Let's shag it GUTTIEREZ begins c...	GUTTIEREZ and DE VRIES exit the crawler in a ...
5	EXT. SPACE	A large spacecraft is suspended in orbit aroun...	None	None	A large spacecraft is suspended in orbit arou...
159	INT. FIRST CRAWLER CAB	GUTTIEREZ pushes forward on the crawler's cont...	None	None	GUTTIEREZ pushes forward on the crawler's con...

In [11]:

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

Out[11]:

(249, 5)

In [12]:

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

Out[12]:

	characters	Character_dialogue
14	KAMEN	Just got back. Missed anything?
256	CASSIE	The door's sealed from inside. Doc Revna's in...
385	HIROKO	Get the door KAMEN hastily slaps the 'close' b...
224	REVNA	Sure. She goes to fetch a cup.
591	HIROKO	Come on... She bears down on the trigger again...
167	KAMEN	Okay. Watch were you point it...
154	BEAUVAIS	We have got no option. We are gonna have to g...
553	PARSONS	Alright, let's open her up. Carefully, now......
295	GUTTIEREZ	Next time you have a thought like that? Keep i...
176	KAMEN	Yeah. Found it curled up in a corner of the t...

2. Character appearance plots¶

In [13]:

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]:

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

Movie Characters:  
 ['HIROKO', 'GUTTIEREZ', 'KAMEN', 'PARSONS', 'CASSIE', 'DE VRIES', 'YORK', 'ACKLAND', 'DILLER', 'URIOSTE', 'THOMAS', 'REVNA', 'BEAUVAIS', 'DRISCOLL', 'MINH', 'MATT', 'PREDATOR', 'GREASE MONKEY', 'REDHEADED GIRL', 'BROKEN TUSK']

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 [23]:

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 [24]:

interact = interaction(df_film, film)

In [25]:

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 [31]:

# 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 [28]:

interact.character_interaction_plot(G, page_ranked_nodes)

Let break it down to the Top 10 Characters¶

In [29]:

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

In [32]:

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 [33]:

xtr = character_mentions(df_film, movie_characters, film)

In [34]:

xter_mentions = xtr.most_mentioned()

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

In [35]:

xtr.top_xters_mentions(xter_mentions, 5)

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

In [36]:

print(movie_characters)

['HIROKO', 'GUTTIEREZ', 'KAMEN', 'PARSONS', 'CASSIE', 'DE VRIES', 'YORK', 'ACKLAND', 'DILLER', 'URIOSTE', 'THOMAS', 'REVNA', 'BEAUVAIS', 'DRISCOLL', 'MINH', 'MATT', 'PREDATOR', 'GREASE MONKEY', 'REDHEADED GIRL', 'BROKEN TUSK']

In [37]:

df_hi = xtr.talk_about_xters(df_film_dialogue, 'HIROKO')

In [38]:

df_gui = xtr.talk_about_xters(df_film_dialogue, 'GUTTIEREZ')

In [39]:

df_CK = xtr.talk_about_xters(df_film_dialogue, 'KAMEN')

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

In [40]:

df_hir = xtr.most_talked_with('HIROKO')

In [41]:

df_kam = 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 [42]:

etn = emotions_sentiments(df_film, film)

In [43]:

df_film_sentiment = etn.film_sentiment('darkslategray')

In [44]:

df_film_emotion = etn.film_emotional_arc()

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

In [45]:

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 [46]:

df_hik_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'HIROKO')

In [47]:

df_gut_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'GUTTIEREZ')

In [48]:

df_kam_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'KAMEN')

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 [49]:

info = scene_info_plots(df_film, film)

In [50]:

info.extract_scene_locations()

In [51]:

info.pie_plots()

5. Gender Distribution in the Movie¶

In [52]:

gd = gender(movie_characters, film)

In [58]:

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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