Film Script Analysis -- Case Study 14 -- Machine Gun Preacher.¶
-----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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#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
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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¶
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films = []
for f in glob.glob('Films/*'):
film_name = re.sub(r'.pkl|Films\\', '', f)
films.append(film_name)
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#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
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#Random 10 films from the 1000 films scraped from the internet
films_list = random.sample(films, 10)
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#Randomly select film to analyze
film = secrets.choice(films_list)
print(film)
Machine-Gun-Preacher
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##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')
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#Randomly generate 10 scenes from the movie script
df_film.sample(10)
Out[13]:
| Scene_Names | Scene_action | Scene_Characters | Scene_Dialogue | Contents | |
|---|---|---|---|---|---|
| 214 | EXT. JAIL JOHNSTOWN, PENNSYLVANIA DAY | Sam released, coming out a door and we see Lyn... | None | None | Sam released, coming out a door and we see Ly... |
| 5 | INT. PRISON CORRIDOR RURAL PENNSYLVANIA DAY | And we see SAM CHILDERS coming down a corridor... | None | None | And we see SAM CHILDERS coming down a corrido... |
| 87 | INT. CRACK HOUSE JOHNSTOWN, PENN. DAY | Dark, junkie squalor. Two or three people slum... | None | None | Dark, junkie squalor. Two or three people slu... |
| 98 | EXT. DIRT ROAD DEEP IN THE BUSH DAY | Sam already out of the truck, moving into the ... | [DENG, DENG, SAM] | [Sam, wait... But he's not listening, moving t... | Sam already out of the truck, moving into the... |
| 208 | INT. CHAPEL SHEKINAH FELLOWSHIP CHURCH CENTR... | Sam, a building fury... | [SAM] | [DRAWING UP BATTLE LINES AGAINST HIS ENEMY WHE... | Sam, a building fury... SAM DRAWING UP BATTLE... |
| 57 | INT. CHILDERS HOUSE NIGHT | The first thing we notice is the relative opul... | [LYNN, LYNN, SAM, PAIGE, SAM, LYNN, SAM, PAIGE... | [Paige, go get some more beans. Paige heads to... | The first thing we notice is the relative opu... |
| 144 | INT. LRA JEEP DRIVING DAY | With two ADULT REBELS in this Jeep as they dri... | None | None | With two ADULT REBELS in this Jeep as they dr... |
| 231 | INT. SAM'S TUKUL CHILDREN'S ORPHANAGE NIGHT | And we see Sam here in the dark... moonlight f... | [WILLIAM, WILLIAM, WILLIAM, WILLIAM] | [I remember my parents when I sleep. I see the... | And we see Sam here in the dark... moonlight ... |
| 174 | EXT. MAIN GATE CHILDREN'S ORPHANAGE DAY | Seven or eight heavily armed vehicles coming t... | [NINETEEN, SAM, DENG, SAM] | [It's Garang It's Garang Palpable excitement... | Seven or eight heavily armed vehicles coming ... |
| 26 | EXT. COUNTRY ROAD NIGHT | Donnie's car pulls off the road and the Hitchh... | None | None | Donnie's car pulls off the road and the Hitch... |
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#check how many scenes the movie script has
df_film.shape
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(251, 5)
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#Randomly select characters and their corresponding dialogues
df_film_dialogue.sample(10)
Out[14]:
| characters | Character_dialogue | |
|---|---|---|
| 506 | SAM | I think that old 78 broke down five times on t... |
| 700 | SAM | Look at this John, when we found this little g... |
| 693 | SAM | It's only 90 days. |
| 688 | DONNIE | Sorry I am late. Sixteen was all jammed up... ... |
| 782 | SAM | It's all done, so save yer breath... where's t... |
| 472 | WILLIAM | Were you with the rebels? Anthony nods. Willia... |
| 653 | AGNETE | They say you are doing good. That you have spe... |
| 407 | NEW | Sam hustling across the compound joining Deng... |
| 282 | SAM | They ain't sleepin out here. Tell em they are ... |
| 758 | SAM | You don't mind, I had like to just sit here ri... |
2. Character appearance plots¶
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ext = extract_characters(df_film, df_film_dialogue, df_film_characters, film)
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movie_characters = ext.extract_character_plot()
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dia = scene_dialogues(df_film, film)
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df_xter_app = dia.character_appearances(movie_characters)
Lets check the appearance of the top 3 characters across the Movie¶
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#Movie characters....
print('Movie Characters: \n', movie_characters)
Movie Characters: ['SAM', 'DENG', 'LYNN', 'DONNIE', 'PAIGE', 'WILLIAM', 'AGNETE', 'BILL', 'BIKER', 'GARANG', 'DAISY', 'BETTY', 'VOICE', 'CONTRACTOR', 'BANK MANAGER', 'SHAVED HEAD', 'DRIFTER', 'ADMINISTRATOR', 'NINETEEN', 'PASTOR KRAUSE', 'SOLDIER', 'NEW', 'SHANNON', 'ORPHAN', 'MECHANIC', 'ROSE', "LYNN'S VOICE", 'CONGREGATION', 'WOMAN', 'DAN', "SAM'S VOICE", 'ADULT REBEL', 'ANTHONY', 'DRIVER']
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df_1st_count, df_1st_dialogue = dia.xter_count_perscene(movie_characters[0])
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dia.scene_dialogue_plot(df_1st_count)
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df_2nd_count, df_2nd_dialogue = dia.xter_count_perscene(movie_characters[1])
dia.scene_dialogue_plot(df_2nd_count)
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df_third_count, df_third_dialogue = dia.xter_count_perscene(movie_characters[2])
dia.scene_dialogue_plot(df_third_count)
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df_2_count, df_2_dialogue = dia.xter_count_perscene(movie_characters[:2])
dia.scene_dialogue_plot(df_2_count)
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df_3_count, df_3_dialogue = dia.xter_count_perscene(['SAM', 'LYNN'])
dia.scene_dialogue_plot(df_3_count)
3. Character Interaction Mapping¶
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interact = interaction(df_film, film)
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graph_list = interact.character_interaction()
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# 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)
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interact.character_interaction_plot(G, page_ranked_nodes)
Let break it down to the Top 10 Characters¶
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#Remember to Re-run the above multigraph code aafter running this code line
graph_list = interact.top10_character_interaction(movie_characters[:10])
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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¶
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xtr = character_mentions(df_film, movie_characters, film)
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xter_mentions = xtr.most_mentioned()
Lets the check the appearance of the 3 most mentioned characters in the Movie¶
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xtr.top_xters_mentions(xter_mentions, 3)
Lets check who specific characters mention the most in their dialogues in the movie¶
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print(movie_characters)
['SAM', 'DENG', 'LYNN', 'DONNIE', 'PAIGE', 'WILLIAM', 'AGNETE', 'BILL', 'BIKER', 'GARANG', 'DAISY', 'BETTY', 'VOICE', 'CONTRACTOR', 'BANK MANAGER', 'SHAVED HEAD', 'DRIFTER', 'ADMINISTRATOR', 'NINETEEN', 'PASTOR KRAUSE', 'SOLDIER', 'NEW', 'SHANNON', 'ORPHAN', 'MECHANIC', 'ROSE', "LYNN'S VOICE", 'CONGREGATION', 'WOMAN', 'DAN', "SAM'S VOICE", 'ADULT REBEL', 'ANTHONY', 'DRIVER']
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df_dg = xtr.talk_about_xters(df_film_dialogue, 'DENG')
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df_sm = xtr.talk_about_xters(df_film_dialogue, 'SAM')
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df_lynn = xtr.talk_about_xters(df_film_dialogue, 'LYNN')
Lets check who specific characters talk with the most in the movie¶
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df_sm = xtr.most_talked_with('SAM')
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df_deng = 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¶
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etn = emotions_sentiments(df_film, film)
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df_film_sentiment = etn.film_sentiment('#17becf')
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df_film_emotion = etn.film_emotional_arc()
Lets check the emotional content for the Top 10 characters in the Movie¶
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df_top10_emotions = etn.emotional_content_plot(df_film_dialogue, movie_characters, 11)
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df_sam_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'SAM')
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df_dg_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'DENG')
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df_lynn_emotions = etn.emotional_arc_xter_plot(df_film_emotion, 'LYNN')
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....¶
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info = scene_info_plots(df_film, film)
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info.extract_scene_locations()
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info.pie_plots()
5. Gender Distribution in the Movie¶
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gd = gender(movie_characters, film)
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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!
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