Film Script Analysis -- Case Study 17 -- Code of Silence¶

-----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 [6]:

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

Code-of-Silence

In [10]:

##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
93	EXT. LATIN STREET DAY	A funeral cortege makes it's way sedately down...	None	None	A funeral cortege makes it's way sedately dow...
187	INT. TRAIN STATION NIGHT	Eddie runs through the cavernous lobby.	None	None	Eddie runs through the cavernous lobby.
119	EXT. LAKE DAY	Pines reflect in the dappled lake as Tony Luna...	None	None	Pines reflect in the dappled lake as Tony Lun...
38	EXT. DRAINPIPE DAY	As he slides down three stories.	None	None	As he slides down three stories.
57	INT. EDDIE'S CAR DAY	Eddie and Kosalas are parked on an airport acc...	[EDDIE, EDDIE, KOSALAS]	[I am going to have to fix that. Eddie has to ...	Eddie and Kosalas are parked on an airport ac...
121	INT. UNMARKED CAR DAY		[KOSALAS, EDDIE, MUSIC]	[Music's at the scene. Eddie picks up the mike...	KOSALAS Music's at the scene. Eddie picks up ...
177	INT. POLICE COMMUNICATIONS ROOM DAY	Beneath the lighted map of Area 4, the police ...	[DISPATCHER, SECOND DISPATCHER, SECOND DISPATC...	[We need a verification on a backup for Unit ...	Beneath the lighted map of Area 4, the police...
179	INT. TAVERN DAY	Cragie and Kosalas sit at the bar. The TV is o...	[DISPATCHER, OFFICER]	[request verification for backup for unit 146...	Cragie and Kosalas sit at the bar. The TV is ...
124	INT. LIBRARY DAY	Gamiani tells Diana about her mother and grand...	None	None	Gamiani tells Diana about her mother and gran...
39	INT. PAINTER'S HALLWAY DAY	The painters run down the stairs toward the fr...	None	None	The painters run down the stairs toward the f...

In [9]:

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

Out[9]:

(230, 5)

In [12]:

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

Out[12]:

	characters	Character_dialogue
320	EDDIE	Your partner's selling you out, Kosalas. He k...
39	LUNA	Okay, by the numbers. He puts on his goggles.
309	DONATO	Siddown, partner. I got a proposition for you.
166	VOICE	Arretez Halten Zie Stop Do not move No se mue...
229	DONATO	Say, partner.
171	KATES	You are looking at the perfect cop. The damn ...
147	PIRELLI	They had a shot on the tube of you guys comin...
73	PARTIDA	Where's the officer who was involved in the o...
167	ENGINEER	After the gyros are locked, any movement of t...
361	PARTIDA	Did you see that weapon in Vega's hand before...

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]:

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

Movie Characters:  
 ['EDDIE', 'KOSALAS', 'DONATO', 'DIANA', 'CRAGIE', 'PARTIDA', 'KATES', 'PIRELLI', 'MUSIC', 'BRENNAN', 'SCALESE', 'SPIDER', 'TONY LUNA', 'LUIS', 'DOC', 'FLANNIGAN', 'KOBAS', 'ODELL', 'SECOND HOOD', 'DISPATCHER', 'VICTOR', 'LUNA', 'SANCHEZ', 'GAMIANI', 'ENGINEER', 'FIRST HOOD', 'COP', 'SPOTTER', 'VOICE', 'POMPAS', 'MOLLY', 'THERESA', "WOMAN'S VOICE", 'OFFICER', 'DEGAS', 'SECOND DISPATCHER']

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(movie_characters[1:3])
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 [33]:

# 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 [34]:

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 [35]:

xtr = character_mentions(df_film, movie_characters, film)

In [36]:

xter_mentions = xtr.most_mentioned()

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

In [37]:

xtr.top_xters_mentions(xter_mentions, 3)

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

In [38]:

print(movie_characters)

['EDDIE', 'KOSALAS', 'DONATO', 'DIANA', 'CRAGIE', 'PARTIDA', 'KATES', 'PIRELLI', 'MUSIC', 'BRENNAN', 'SCALESE', 'SPIDER', 'TONY LUNA', 'LUIS', 'DOC', 'FLANNIGAN', 'KOBAS', 'ODELL', 'SECOND HOOD', 'DISPATCHER', 'VICTOR', 'LUNA', 'SANCHEZ', 'GAMIANI', 'ENGINEER', 'FIRST HOOD', 'COP', 'SPOTTER', 'VOICE', 'POMPAS', 'MOLLY', 'THERESA', "WOMAN'S VOICE", 'OFFICER', 'DEGAS', 'SECOND DISPATCHER']

In [40]:

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

In [41]:

df_kos = xtr.talk_about_xters(df_film_dialogue, 'KOSALAS')

In [42]:

df_dona = xtr.talk_about_xters(df_film_dialogue, 'DONATO')

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

In [43]:

df_edd = xtr.most_talked_with('EDDIE')

In [45]:

df_donato = 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 [46]:

etn = emotions_sentiments(df_film, film)

In [47]:

df_film_sentiment = etn.film_sentiment('darkslategrey')

In [48]:

df_film_emotion = etn.film_emotional_arc()

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

In [49]:

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 [50]:

df_edd_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'EDDIE')

In [51]:

df_kos_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'KOSALAS')

In [52]:

df_dia_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'DIANA')

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 [53]:

info = scene_info_plots(df_film, film)

In [54]:

info.extract_scene_locations()

In [55]:

info.pie_plots()

5. Gender Distribution in the Movie¶

In [56]:

gd = gender(movie_characters, film)

In [57]:

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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