Film Script Analysis -- Case Study 15 -- Wild Wild West.¶

-----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)

Wild-Wild-West

In [7]:

##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 [8]:

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

Out[8]:

	Scene_Names	Scene_action	Scene_Characters	Scene_Dialogue	Contents
165	EXT. NEAR SPIDER CANYON WANDERER STOPPED DU...	PANNING an expanse of canvas comprising a 20fo...	[GORDON, NEW, WEST, COLEMAN, GORDON, WEST]	[Now Bernoulli's Principle states that the air...	PANNING an expanse of canvas comprising a 20f...
120	EXT. RIVER CANYON GORDON'S POV DAY ...	It's no gully. It's a canyon that drops 100 fe...	None	None	It's no gully. It's a canyon that drops 100 f...
29	INT. FAT CAN'S UPSTAIRS HALL NIGHT ...	West and Gordon, oblivious to the impending di...	[WEST]	[So now what...? 31 ...	West and Gordon, oblivious to the impending d...
138	EXT. PROMONTORY POINT LONG SHOT DAY ...	The Tarantula in full view. People start screa...	None	None	The Tarantula in full view. People start scre...
3	INT. WATER TOWER NIGHT ...	WEST is bathing with the beatiful SALOON GIRL.	[WEST]	[That's right, sugar, and you should feel free...	WEST is bathing with the beatiful SALOON GIRL...
88	INT. LOVELESS'S TRAIN DAY ...	Loveless turns to his three distaff warriors.	[LOVELESS]	[Amazonia, shall we disabuse our friends of th...	Loveless turns to his three distaff warriors....
177	EXT. TARANTULA BRIDGE DUSK ...	Now its rear end is tilted up in the air givin...	None	None	Now its rear end is tilted up in the air givi...
39	EXT. UNDERNEATH WANDERER DAY ...	West, eyes wide, is clinging on for dear life,...	None	None	West, eyes wide, is clinging on for dear life...
151	EXT. PROMONTORY POINT TARANTULA LEG DAY ...	West takes cover behind the steel girders, SIX...	None	None	West takes cover behind the steel girders, SI...
111	EXT. CORNFIELD PERIMETER GORDON AND WEST DA...	frustrated, as the train pulls away from them....	[WEST, GORDON]	[Gordon, get out your little tool kit and get ...	frustrated, as the train pulls away from them...

In [9]:

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

Out[9]:

(198, 5)

In [10]:

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

Out[10]:

	characters	Character_dialogue
95	PRESIDENT GRANT	Gentlemen I am leaving today for Utah where th...
98	WEST	Figures.
377	GORDON	Rita, no It's just... CLUNK... she drops it ...
417	WEST	You won't have to They drop OUT OF FRAME and w...
660	LOVELESS	But, of course, I would die along with you. I ...
644	GORDON	Not exactly. He gestures down Loveless's legs ...
481	WEST	Don't push it. 139
256	RITA	Excuse me? THE WILD WILD WEST Rev. 61598 ...
390	LOVELESS	And speaking of 'hard pumping...' Mr. Coleman,...
106	GORDON	As a matter of clarification, this isn't needl...

2. Character appearance plots¶

In [11]:

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

In [12]:

movie_characters = ext.extract_character_plot()

In [13]:

dia = scene_dialogues(df_film, film)

In [14]:

df_xter_app = dia.character_appearances(movie_characters)

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

In [15]:

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

Movie Characters:  
 ['WEST', 'GORDON', 'LOVELESS', 'RITA', 'PRESIDENT GRANT', 'COLEMAN', 'MUNITIA', 'MISS EAST', 'CHANTEUSE', 'NEW', 'SALOON GIRL', 'HUDSON', 'DUSK', 'DORA', 'EYECROSSED REB', 'LIPPENREIDER', 'AMAZONIA', 'MISS LIPPENREIDER', 'NIGHT', 'REB', 'MORTON', 'BIG REB', 'WEST AND GORDON', 'DRAGON LADY', 'DISTAFF CORPS', 'GEORGE WASHINGTON', 'WANDERER  DAY', 'SHOVEL GUY', 'METAL HEAD']

In [16]:

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

In [17]:

dia.scene_dialogue_plot(df_1st_count)

In [18]:

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

In [19]:

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

In [20]:

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

In [21]:

df_3_count, df_3_dialogue = dia.xter_count_perscene(movie_characters[:2])
dia.scene_dialogue_plot(df_3_count)

3. Character Interaction Mapping¶

In [22]:

interact = interaction(df_film, film)

In [23]:

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 [28]:

# 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 [25]:

interact.character_interaction_plot(G, page_ranked_nodes)

Let break it down to the Top 10 Characters¶

In [26]:

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

In [29]:

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 [30]:

xtr = character_mentions(df_film, movie_characters, film)

In [31]:

xter_mentions = xtr.most_mentioned()

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

In [32]:

xtr.top_xters_mentions(xter_mentions, 3)

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

In [33]:

print(movie_characters)

['WEST', 'GORDON', 'LOVELESS', 'RITA', 'PRESIDENT GRANT', 'COLEMAN', 'MUNITIA', 'MISS EAST', 'CHANTEUSE', 'NEW', 'SALOON GIRL', 'HUDSON', 'DUSK', 'DORA', 'EYECROSSED REB', 'LIPPENREIDER', 'AMAZONIA', 'MISS LIPPENREIDER', 'NIGHT', 'REB', 'MORTON', 'BIG REB', 'WEST AND GORDON', 'DRAGON LADY', 'DISTAFF CORPS', 'GEORGE WASHINGTON', 'WANDERER  DAY', 'SHOVEL GUY', 'METAL HEAD']

In [34]:

df_gd = xtr.talk_about_xters(df_film_dialogue, 'GORDON')

In [35]:

df_lv = xtr.talk_about_xters(df_film_dialogue, 'LOVELESS')

In [36]:

df_rt = xtr.talk_about_xters(df_film_dialogue, 'RITA')

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

In [37]:

df_lv = xtr.most_talked_with('LOVELESS')

In [38]:

df_gordon = 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 [39]:

etn = emotions_sentiments(df_film, film)

In [41]:

df_film_sentiment = etn.film_sentiment('aquamarine')

In [42]:

df_film_emotion = etn.film_emotional_arc()

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

In [43]:

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 [44]:

df_west_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'WEST')

In [45]:

df_loveless_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'LOVELESS')

In [46]:

df_rita_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'RITA')

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 [47]:

info = scene_info_plots(df_film, film)

In [48]:

info.extract_scene_locations()

In [49]:

info.pie_plots()

5. Gender Distribution in the Movie¶

In [50]:

gd = gender(movie_characters, film)

In [51]:

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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