Breach Lab
Breach Lab
Breachthemes
by
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# Breach Lyrics
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import pandas as pd import re from collections import Counter import matplotlib.pyplot as plt import numpy as np import squarify # pip install squarify import seaborn as sns from wordcloud import WordCloud import plotly.express as px # Requires: pip install plotly
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file_path = ‘/Users/meredithsmith/Desktop/TØPAnalysis/BreachSongs2.xlsx’ df = pd.read_excel(file_path)
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import pandas as pd
1. Create a list of your song titles as strings
song_names = [ “City Walls”, “RAWFEAR”, “Drum Show”, “Garbage”, “The Contract”, “Downstairs”, “Robot Voices”, “Center Mass”, “Cottonwood”, “One Way”, “Days Lie Dormant”, “Tally”, “Intentions”, “Drag Path” ]
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City_Walls = “"”Square up with me, I can take your right, throw a left Pair up with me, I’m a take you right to the chest Warpath etched in the surface Lines of the yellow tape, they’re moving quiet like a Serpentine in a formation Buddy, that’s my team, pretty impatient Now they’re waiting on me It might be the furthest we’ve reached Now move it up, move it up, it’s a breach
I wonder where you are I wanted you to show me The way around the city walls The way on through I wonder where you are I wanted you to show me But now the night has fallen Abandoned by the sun
Square up with me, I can come to you, tell me when Pair up with me, I can run on you, in the end I write a promise in pencil, but my loyalty’s in pen Use a mistake as a crooked stencil, then we trace it back again From the mainland to the island of violence It was the same plan for a while, decided To send me up and rip you outta your seat You see, in a city with no entrance, there is not a retreat I’m wondering what you thought would happen, who you thought I would be Was this a sideswipe or did you picture this in a dream? Buckle down, this is possibly the furthest we’ve reached Now move it up, move it up, it’s a breach
I wonder where you are I wanted you to show me The way around the city walls The way on through I wonder where you are I wanted you to show me But now the night has fallen Abandoned by the sun Abandoned by the sun
My smile wraps around my head, splitting it in Two, two, two, two I don’t have a clue how I can keep the top half Glued, glued, glued, glued My smile wraps around my head, splitting it in Two, two, two, two I don’t have a clue how I can keep the top half Glued, glued, glued, glued
I wonder where you are I wanted you to show me
Entertain my, entertain my Entertain my, entertain my Entertain my, entertain my Entertain my, entertain my faith This is the last time, this is the last time Entertain my faith This is the last time that I try Address my soul, address my soul, address my soul Entertain my faith This is the last time that I try”””
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RAWFEAR = “"”Raw fear Raw fear
Raw fear moves me, sounds of empty Uzis Life is just forever nipping heels, never slowing down Raw fear moves me, sounds of empty Uzis Life is just forever gripping feels, never slowing down
You know what scares me? Nah, it’s stupid You’ve met my fears, they’re all regulars Wait, who’s the new kid? You know what kills me? Not getting stronger And now I can’t seem to sit still any longer
Raw fear moves me, sounds of empty Uzis Life is just forever nipping heels, never slowing down Raw fear moves me, sounds of empty Uzis Life is just forever gripping feels, never slowing down (I can’t seem to sit still any longer) Raw fear moves me, sounds of empty Uzis Life is just forever gripping feels, never slowing down
(Never slowing down) (Never slowing down) (Never, never slowing down) (Never slowing down) (Never, never slowing down) (Never slowing down) (Never, never slowing down)
Learning all that really matters is a slow and painful lesson (Never slowing down, never slowing down) It is not pass or fail, but a poisonous progression (Never slowing down, never slowing down) Try to micro-dose to immunity but you’ll never, ever get it (Never slowing down, never slowing down) And I go, ah, ah, ah (Never slowing down) What’s the matter? isn’t helping (Never slowing down) You go, ooh, ooh, ooh (Never slowing down) I wish her parents would stop yelling (Never slowing down) I could build a tower with the times I was pretending (Never slowing down) Try to micro-dose to immunity but I’ll never see the ending (Never slowing down) And I go, ah, ah, ah (Never slowing down) What’s the matter? isn’t helping (Never slowing down) You go, ooh, ooh, ooh (Never slowing down) I wish her parents would stop yelling You know what scares me? (Ah, ah, ah) Nah, it’s stupid And you go, ooh, ooh, ooh Wait, who’s the new kid? (Never slowing down) And you go, ah, ah, ah Not getting stronger And now I can’t seem to sit still any longer
Raw fear moves me, sounds of empty Uzis (Ah, ah, ah) Life is just forever nipping heels, never slowing down Raw fear moves me, sounds of empty Uzis (Ah, ah, ah) Life is just forever gripping feels, never slowing down Raw fear moves me, sounds of empty Uzis (Ah, ah, ah) Life is just forever nipping heels, never slowing down Raw fear moves me, sounds of empty Uzis (Ah, ah, ah) Life is just forever gripping feels, never slowing down”””,
Drum_Show = “"”Puttin’ on a drum show Ooh-ooh
Completed checklist for today Now they have to let you out of your cage Feelin’ stuck between a rock and a home Two places you do not want to go
So, so He’s puttin’ on a drum show
Even now, even now, even now He’ll take the longer way home Even now, even now, even now He’ll never ever say so He drives fast just to feel it, feel it (Feel it) He drives slow if his song’s not over (Feel it) Drown it out, drown it out
Show Ooh-ooh
He’d rather feel something than nothing at all So he swerves all around as his head starts to fall, turns it up Stuck between a rock and a home Two places he does not want to go
So, so He’s puttin’ on a drum show
Even now, even now, even now He’ll take the longer way home (I’ve been this way) Even now, even now, even now He’ll never ever say so (I want to change) He drives fast just to feel it, feel it (Feel it) He drives slow if his song’s not over (Feel it) Drown it out, drown it out
I’ve been this way I want to change I’ve been this way I want to change
He’s puttin’ on a drum show
Even now, even now, even now He’ll take the longer way home (I’ve been this way) Even now, well even now, well even now He’ll never ever say so (I want to change) He drives fast just to feel it, feel it (Feel it) He drives slow if his song’s not over (I’ve been this way) (Feel it) Drown it out, drown it out (I want to change)
I’ve been this way (Feel it, feel it) I want to change (Feel it, feel it) I’ve been this way (Feel it, feel it) I want to”””,
Garbage = “"”I didn’t do it
I feel like garbage I feel like My systems are operating at twenty percent
Or less I’ll walk you through the numbers I’ll show you nothing’s left Oh-oh
( Yeah, yeah) I’m biting down on my shirtsleeve ( Yeah, yeah) I’ll lash out if you’re in arm’s reach (Yeah, yeah) I’m so lost, like, what is happening? Like, like I feel like garbage
Ooh, yeah
I can’t decide If I could feel more or I just knew less When I was younger Bright-eyed with wonder Do you know which name it’s under? (Oh-oh-oh)
Or less I’ll walk you through the numbers I’ll show you nothing’s left Oh-oh
(Yeah, yeah) I’m biting down on my shirt sleeve (Yeah, yeah) I’ll lash out if you’re in arm’s reach (Yeah, yeah) I’m so lost, like, what is happening? Like, what is happening?
Would you move closer if I grew quieter? Maybe this is you Maybe you don’t fix and you like it like this
I feel like garbage (Yeah) I feel like garbage I feel like (Yeah) Like
I’ll lash out, lash out, lash out (Hey) I’m biting down on my shirt sleeve Like, I’ll lash out, lash out, lash out Don’t give up on me Don’t give up on me No, no, no, please, please, please”””,
The_Contract = “"”I check the doors, check the windows and pull the blinds I check the clock, wondering what he’ll pull this time I have a feeling that necromancer’s outside And I’m just tryin’ to stay quiet
I don’t sleep much, that’s crazy, how’d you know that? (That) Keep myself up, that’s maybe how you know that (That, that) Ooh, promises and contracts I used to keep I don’t sleep much, that’s crazy, how’d you know that? (That) Keep myself up, that’s maybe how you know that (That, that) Ooh, my hallucination I used to see
Wake up and I find out, did a change up Wait, did it change up? Is it light out yet? Better find out, bet I’ma walk around the edge of the room and the bed Where I hide my face, pace around Better try to breathe, pace around, pace around
I check the doors, check the windows, and pull the blinds I check the clock, wondering what he’ll pull this time I have a feeling that necromancer’s outside And I’m just tryin’ to stay quiet
I don’t sleep much, that’s crazy, how’d you know that? (That) Keep myself up, that’s maybe how you know that (That, that) Ooh, promises and contracts I used to keep I don’t sleep much, that’s crazy, how’d you know that? (That) Keep myself up, that’s maybe how you know that (That, that) Ooh, my hallucination I used to see
I used to see Sleep I found
(Oh-ooh) I wanna get out there (Oh-ooh) But I don’t try (Oh-ooh) I wanna get out there (Oh-ooh) But I don’t try
I check the clock, wondering what he’ll pull this time I have a feeling that necromancer’s outside And I’m just tryin’ to stay quiet
I don’t sleep much, that’s crazy, how’d you know that? (That) I keep myself up, that’s maybe how you know that (That, that) Ooh, promises and contracts I used to keep I don’t sleep much, that’s crazy, how’d you know that? (That) Keep myself up, that’s maybe how you know that (That, that) Ooh, my hallucination I used to see
I used to see I used to see Ooh, my hallucination I used to see
I used to see, it felt so real But now I plead, just take the deal I promised you a contract I promised you a contract”””,
Downstairs = “"”You-ooh-ooh can have all I’ve made and all I’ve ever known You-ooh-ooh can have both my lungs if you ask me so Cause I-I-I want to be the one after your own heart And I-I-I might doubt the process like I doubted the start
So I’m begging oh, oh-oh, oh-oh, mercy I’m begging oh, oh-oh, oh-oh, mercy
Downstairs, I feel better in the cellar Down there, it’s some shelter from the weather I wear all of my heart on my sleeve I’ll take what I believe, and I’m hiding it Downstairs, I feel better in the cellar Down there, it’s some shelter from the weather I wear all of my heart on my sleeve I’ll take what I believe, and I’m hiding it
Feeling like I’m nothing Feeling like I’m run down Was afraid of nothing Now I’m just ashamed how I hide my face from you
So I’m begging oh, oh-oh, oh-oh, mercy I’m begging oh, oh-oh, oh-oh, mercy
Downstairs, I feel better in the cellar Down there, it’s some shelter from the weather I wear all of my heart on my sleeve I’ll take what I believe, and I’m hiding it Downstairs, I feel better in the cellar Down there, it’s some shelter from the weather I wear all of my heart on my sleeve I’ll take what I believe, and I’m hiding it
Oh, what have I become? Dirty and wretched one Am I unholy man? Have I forced your hand?
Oh, woah-oh, woah-oh, mercy, yeah Oh, woah-oh, woah-oh, mercy, yeah
Down there, it’s some shelter from the weather I wear all of my heart on my sleeve I’ll take what I believe, and I’m hiding it Downstairs, I feel better in the cellar Down there, it’s some shelter from the weather I wear all of my heart on my sleeve I’ll take what I believe, and I’m hiding it Downstairs (You can have), I feel better in the cellar (All I’ve made) Down there, need some shelter from the weather (All I’ve ever known) I wear (You can have) all of my heart on my sleeve (Both my lungs) I’ll take what I believe, and I’m hiding it, yeah (If you ask me so, oh) And I’m hiding it, yeah (‘Cause I want to be the one) And I’m hiding it, yeah (After your own heart) And I’m hiding it, yeah, oh (And I might doubt the process like I doubted the start)
You can have all I’ve made and all I’ve ever known You can have both my lungs if you ask me so Cause I want to be the one after your own heart And I might doubt the process like I doubt the start”””,
Robot_Voices = “"”They keep me company The robots and machines in my room An understanding The beeps and tones they make The only advice I take, the inside jokes are landing Now I’m waitin’ for the changes, everyone sees Feeling so fancy
And I just wanna dance and style and stay a while The lines on the floor, how did you find me?
Woo I wasn’t lookin’ for love this year But my robot told me that I shouldn’t fear When I met you, I found you safe and warm Then the robot voices would reassure me
The half-drawn blinds casting lines on the floor as my metal friend flashes in time The daisy-chained power strips generating A newfound loneliness, I’m waiting for the changes, everyone sees Feeling so fancy
And I just wanna dance and style and stay a while The lines on the floor, how did you find me?
Woo I wasn’t lookin’ for love this year But my robot told me that I shouldn’t fear When I met you, I found you safe and warm Then the robot voices would reassure me I wasn’t lookin’ for love this year But my robot told me that I shouldn’t fear When I met you, I found you safe and warm Then the robot voices would reassure me”””,
Center_Mass = “”“(Right) I figured out how it’s on it (Right this way) It goes (Right) My death (Right this way) Girl, I really don’t think you should take that (Stop it)
A little softer than I used to be (Right) Used to mouth off often with artillery Now it is cough drops and soft spots in a middle seat (Right this way) Chivalry A tighter space than I used to know (Right) Spread out wide no more, pinch my shoulders close Hoping they misplace my reservation (Right this way), okay, okay
I thought you had it figured out I know, I know You said you made it to the top I lied, I lied So, tell us, how did you make out alright? Well
I pull on my shirtsleeve to cover half of my hands Bring my arms closer to my body, get as small as I can They call it center mass, that part they aim for Shrink it down to nothing and forgetting what the pain’s for I pull on my shirtsleeve (My shirtsleeve), to cover half of my face (Half of my face) Bring my arms closer to my body, get as small as they say (Small as they say) They call it center mass, that part they aim for (For-for-for-for-for-for)
Right this way if you’re frustrated (Right) Your first mate in the fifth grade tried to persuade Try it once, he was shown on the bus (Right this way) Chain’s off my huff, anxious enough as it is (Right) Gotta walk, never looking up once at the kids in the park What a day, getting dark, what a day (Right this way) Okay, okay
I thought you had it figured out(I thought you had it figured out) I know, I know You said you made it to the top (You said you made it to the top) I lied, I lied So, tell us, how did you make out alright? Well
I pull on my shirtsleeve (My shirtsleeve), to cover half of my hands (Half of my hands) Bring my arms closer to my body, get as small as I can (Small as I can) They call it center mass, that part they aim for Shrink it down to nothing and forgetting what the pain’s for I pull on my shirtsleeve (My shirtsleeve), to cover half of my face (Half of my face) Bring my arms closer to my body, get as small as they say (Small as they say) They call it center mass, that part they aim for
Shrink it down to nothing and forgetting what the pain’s for That part they aim for
Found out what I’m worth, I run to you I hope you understand this run-on sentence My tattoos only hurt when meaning fades I think my skin got worse with good intentions I don’t want to say what happened, I just want to let it go Nothing was the same right after, I went to the funeral I miss you so, so much, take what you want Take what you want from me Took you for granted They call it center mass, that part they aim for That part they aim for That part they aim for That part they aim for”””,
Cottonwood = “"”Can’t believe you’re gone for good Someone chopped you down Thought you’d just fall in the woods But she heard that sound No, I don’t, no, I don’t remember Last we spoke No, I don’t, no, I don’t remember
Remember Remember Remember
I look back in time through a telescope I’ve been catching my I’ve been catching my My reflection already looking
Most are blown down in a storm But the wind was tame You weren’t waiting anymore Were you sad or brave? No I don’t, no, I don’t condemn you Back and forth No I don’t, no, I don’t condemn you
Condemn you Condemn you Condemn you
I look back in time through a telescope I’ve been catching my I’ve been catching my My reflection already looking
You tore me up more than you know You tore me up more than you know You tore me up more than you know You tore me up
I look back in time through a telescope I’ve been catching my I’ve been catching my My reflection already looking”””,
One_Way = “"”I wanted to fly But they say a system’s coming in The chances are high That this is a One way, one way, one way, one way
Am I dreaming? If it’s so Can I take back who I was before? If it’s no Then all my dreams have Not been interesting in a while This has not been interesting in a while, while, while
I wanted to fly But they say a system’s coming in The chances are high That this is a One way, one way, one way, one way, one way (ooh)
Me and my scene just ain’t gelling Someone next to me keeps yelling Why am I still walking? Something must be wrong here I just don’t belong here
I wanted to fly But they say a system’s coming in Chances are high That this is a One way, one way, one way, one way, one
I wanted to fly But they say a system’s coming in The chances are high That this is a One way, one way, one That this is a One way, one way, one That this is a One way, one way (This is a)”””,
Days_Lie_Dormant = “"”Hey it’s Josh, call me back I’m not opposed to “”"”One Way”””” going to “”"”Days Lie Dormant”””” But it does feel like quite a vibe change And uhh”” ““Ah
You say, “”"”I should chase it”””” I’ll never say “”"”No”””” Too narrow and too tight Somewhere you can’t follow
You say with your face Something else altogether
My days lie dormant ‘Cause I just can’t afford it To waste time in a city Where no one knows your name My days lie dormant ‘Cause I just can’t afford it To waste my time inside a place you’re not Don’t make me waste my love It’s everything I’ve got
You say, Don’t worry ‘bout me though It looks so much warmer there And I should try Topo Chico And get some more sun
But you say with your face Something else altogether
My days lie dormant ‘Cause I just can’t afford it To waste time in a city Where no one knows your name My days lie dormant ‘Cause I just can’t afford it To waste my time inside a place you’re not Don’t make me waste my love It’s everything I’ve got
My days lie dormant Frozen and distorted Robbed of my momentum And suspended in the air My days lie dormant It’s feeling less important To waste my time inside a place you’re not Don’t make me waste my love It’s everything I’ve got Yeah-yeah-yeah-yeah Yeah-yeah-yeah-yeah Yeah-yeah-yeah-yeah Yeah-yeah-yeah-yeah
My days lie dormant ‘Cause I just can’t afford it To waste time in a city Where no one knows your name My days lie dormant ‘Cause I just can’t afford it To waste my time inside a place you’re not Don’t make me waste my love It’s everything I’ve got”””,
Tally = “"”I lost my rank and title I turned my back on it Sold it all at a discount (Yeah, yeah) I wanted to progress things I wanted my soul set free Lost it all at a discount (Yeah, yeah)
You and I both know that’s how the story goes We’ve seen it several times (Yeah, yeah) It’s kinda hard to know, is there’s a point at all? Or should we cut the line?
You want a tally, I lost the count You want to love me, I’ll let you down Still now, you believe in me somehow When I replay it in my mind I see your heartbreak every time Still now, you believe in me somehow
You believe in me somehow You believe in me somehow
I liken our fight to two lightning strikes strike on cue Like right when a storm hits, sights set (Yeah, yeah) Right set, circumstantial proof You should go your way, I should try mine Even though we both know We should just cut the line (Yeah, yeah)
You want a tally, I lost the count You want to love me, I’ll let you down Still now, you believe in me somehow When I replay it in my mind I see your heartbreak every time Still now, you believe in me somehow
Somehow, somehow (Ooh, ooh) You believe in me somehow Ooh
Ooh, somehow I figured you were keeping track of every time Just figured you were keeping track of every time I let you down, I breached your trust, I let you down Just figured you were keeping track of every time You were keeping track
You want a tally, I lost the count (You want a tally) You want to love me (You want to love me), I’ll let you down Still now (Still now), you believe in me somehow (You believe in me somehow) When I replay it in my mind (When I replay it) I see your heartbreak every time (I see your heartbreak) Still now (Still now), you believe in me somehow
Yeah, yeah”””,
Intentions = “"”I am starting it all over once again Did I learn a thing? (hoo-hoo-hoo, hoo-hoo-hoo, hoo-hoo-hoO)
Intentions are everything (hoo-hoo-hoo, hoo-hoo-hoo, hoo-hoo-hoO) Intentions will set you free
You will fail most every day and every way Did you learn a thing? (hoo-hoo-hoo, hoo-hoo-hoo, hoo-hoo-hoO)
Intentions lack memory (hoo-hoo-hoo, hoo-hoo-hoo, hoo-hoo-hoO) Intentions will set you free
Just try to be”””,
Drag_Path = “"”When I see the devil’s eyes A current travels down my spine He found me Seems as though I’ve lost again A story told at nauseam He found me Maybe once or twice or three He’s tried his hand at drowning me But I’m still on fire At least I’m pretty sure
When I see the devil’s eyes I’ll look away and smile wide You found me ‘Cause then I’ll know you’re also there ‘Cause proof is in the opposite You found me
A drag path etched in the surface as evidence I left there on purpose a sad, sad Laying on the surface (Yeah) Can you find me? I dug my heels into the gravel as evidence For you to unravel a drag path etched in the surface (Yeah) Can you find me?
What will you do? Please, please hurry What will you do? Please, please hurry
Can you, can you, can you, can you? I’m trapped now, etched in the surface as evidence I’m left there on purpose, a sad sign, laying on the surface Can you, can you find, can you find me? I dug my heels into a gravel as evidence For you to unravel the drag path Etched in the surface
Can you find me? (Can you find me?) Can you find me? (Can you find me?) Can you find me? (Can you find me?) Can you find me? (Can you find me?)
Then the sun begins to rise We made it through the darkest night You found me”””
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stop_words = [ “i”, “me”, “my”, “myself”, “we”, “our”, “ours”, “ourselves”, “you”, “your”, “yours”, “he”, “him”, “his”, “she”, “her”, “it”, “its”, “they”, “them”, “their”, “what”, “which”, “who”, “whom”, “this”, “that”, “these”, “those”, “am”, “is”, “are”, “was”, “were”, “be”, “been”, “being”, “have”, “has”, “had”, “having”, “do”, “does”, “did”, “doing”, “a”, “an”, “the”, “and”, “but”, “if”, “or”, “because”, “as”, “until”, “while”, “of”, “at”, “by”, “for”, “with”, “about”, “against”, “between”, “into”, “through”, “during”, “before”, “after”, “above”, “below”, “to”, “from”, “up”, “down”, “in”, “out”, “on”, “off”, “over”, “under”, “again”, “further”, “then”, “once”, “not”, “no”, “yes”, “so”, “can”, “just”, “wanted”, “where”, “dont”, “yeah”, “let”, “im”, “ah”, “want”, “like”, “ive”, “all”, “ooh”, “ill”, “even”, “forforforforforfor”, “every”, “hoohoohoo”, “hell”, “yeahyeahyeahyeah”, “daisychained”, “thats”, “there”, “how”, “somehow”, “when”, “used”, “cant” ]
Function to remove stop words from a string
def remove_stopwords(text): words = text.split() filtered_words = [word for word in words if word.lower() not in stop_words] return “ “.join(filtered_words)
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2. Use your existing list of lyric variables
all_lyrics = [ City_Walls, RAWFEAR, Drum_Show, Garbage, The_Contract, Downstairs, Robot_Voices, Center_Mass, Cottonwood, One_Way, Days_Lie_Dormant, Tally, Intentions, Drag_Path ]
3. Build the DataFrame
df_breach = pd.DataFrame({ “Song_Name”: song_names, “Lyrics”: all_lyrics })
Display to verify
print(df_breach.head())
%%
df_breach.head() df_breach.to_csv(‘BreachLyrics.csv’, index=False)
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df.shape
%% [markdown]
### Create all_processed_words list
%%
import re from collections import Counter
1. Group all your song variables into one list
Replace these placeholder names with your actual 14 variables
songs_to_process = [City_Walls, RAWFEAR, Drum_Show, Garbage, The_Contract, Downstairs, Robot_Voices, Center_Mass, Cottonwood, One_Way, Days_Lie_Dormant, Tally, Intentions, Drag_Path] # Add all 14 song variables here
This list will store the ‘list of words’ for each song
all_processed_words = []
2. Loop through the list to process each song automatically
for song in songs_to_process: # Handle the tuple check for each individual song to avoid AttributeError text_to_process = song[0] if isinstance(song, tuple) else song
# Normalize: lowercase and remove punctuation
clean_lyrics = re.sub(r'[^\w\s]', '', text_to_process.lower())
# Filter: Use your remove_stopwords function
filtered_lyrics = remove_stopwords(clean_lyrics)
# Split: Break the string into a list of words
words = filtered_lyrics.split()
# Add this song's word list to our collection
all_processed_words.append(words)
3. Tally: Count occurrences across ALL songs combined
flattened_list = [word for song_list in all_processed_words for word in song_list] counts = Counter(flattened_list)
4. Output: View the most frequent meaningful words in the discography
print(“Top 10 words across all songs:”) print(counts.most_common(10))
%% [markdown]
### LDA training code
%%
import gensim from gensim import corpora
1. Create the Dictionary
This maps every unique word to an ID
dictionary = corpora.Dictionary(all_processed_words)
2. Create the Corpus
This converts each song into a “Bag of Words” format (word_id, frequency)
corpus = [dictionary.doc2bow(text) for text in all_processed_words]
3. Train the LDA Model
We use 3 topics because 14 songs is a smaller sample size
lda_model = gensim.models.LdaModel( corpus=corpus, id2word=dictionary, num_topics=3, random_state=100, update_every=1, chunksize=10, # Adjusted for your 14 songs passes=20, # Increased passes to help the model find patterns in a small dataset alpha=’auto’ )
4. Show the discovered Topics
print(“— Discovered Lyrical Themes —”) for idx, topic in lda_model.print_topics(-1): print(f”Topic {idx}: {topic}\n”)
%% [markdown]
### Topic Model
%%
import gensim from gensim import corpora
1. Create the Dictionary
This maps every unique word to a unique ID
dictionary = corpora.Dictionary(all_processed_words)
2. Create the Corpus
This converts each song into a list of (word_id, word_frequency)
corpus = [dictionary.doc2bow(text) for text in all_processed_words]
3. Train the LDA Model
We use 5 topics as a starting point for 14 songs
lda_model = gensim.models.LdaModel( corpus=corpus, id2word=dictionary, num_topics=5, random_state=100, passes=20, # More passes help with small datasets alpha=’auto’, # Let the model learn the topic distribution per_word_topics=True )
4. Display the Topics and their Top Words
print(“— Discovered Lyrical Themes —”) for idx, topic in lda_model.print_topics(-1): print(f”Topic {idx}: {topic}\n”)
%%
topic_labels = { 0: “Faith & Resilience”, 1: “Emotional Exposure”, 2: “Search for Truth”, 3: “Wasted Time”, 4: “Path & Purpose” }
%%
for i, topic in enumerate(lda_model.print_topics()): print(f”Topic {i} ({topic_labels[i]}): {topic}”)
%% [markdown]
### Quantify the vibe of each song
%%
import pandas as pd
def get_dominant_topic(ldamodel, corpus): # Initialize a list to hold our data topic_data = []
# Iterate through each song in the corpus
for i, row in enumerate(ldamodel[corpus]):
# The row contains a list of (topic_id, probability) tuples
# Sort it so the highest probability is first
row = sorted(row[0], key=lambda x: (x[1]), reverse=True)
# Get the top topic ID and its percentage
topic_num, prop_topic = row[0]
# Add to our list
topic_data.append([i, int(topic_num), round(prop_topic, 4)])
return pd.DataFrame(topic_data, columns=['Song_ID', 'Dominant_Topic', 'Topic_Perc_Contribution'])
2. Generate the Dataframe
df_topics = get_dominant_topic(lda_model, corpus)
topic_labels = { 0: “Faith & Resilience”, 1: “Emotional Exposure”, 2: “Search for Truth”, 3: “Wasted Time”, 4: “Path & Purpose” } df_topics[‘Topic_Label’] = df_topics[‘Dominant_Topic’].map(topic_labels)
3. Combine with your song names (if you have them in a list)
song_names = [“City_Walls”, “RAWFEAR”, “Drum_Show”, “Garbage”, “The_Contract”, “Downstairs”, “Robot Voices”, “Center Mass”, “Cottonwood”, “One_Way”, “Days_Lie_Dormant”, “Tally”, “Intentions”, “Drag_Path”] # Add all 14 names df_topics[‘Song_Name’] = song_names[:len(df_topics)]
Display the final table
print(df_topics[[‘Song_Name’, ‘Topic_Label’, ‘Dominant_Topic’, ‘Topic_Perc_Contribution’]])
%% [markdown]
Discovered Lyrical Themes
Topic 0: Persistent Reminiscing Focuses on the passage of time, keeping score of past hurts (“tally,” “track,” “replay”), and the struggle to “believe” while feeling “lost” in heartbreak.
#
Topic 1: Unfiltered Vulnerability Centers on raw emotions and internal conflict, specifically the refusal to slow down despite “fear” and the “raw” feeling of “hiding” one’s heart.
#
Topic 2: Searching for Proof Reflects a quest for clarity or “evidence,” using imagery of things being “etched” on the “surface” to “find” or “know” the truth.
#
Topic 3: Regret and Stagnation Deals with the anxiety of “wasting” time and “days” lying “dormant,” highlighting the realization that one cannot “afford” to let more time slip away.
#
Topic 4: Direction and Intent Explores the path forward (“way”) and the internal “intentions” or “aim” behind one’s actions, balancing “right” and “will.”
%%
Create a new column for the descriptive label
df_topics[‘Topic_Label’] = df_topics[‘Dominant_Topic’].map(topic_labels)
Displaying the final organized table
print(df_topics[[‘Song_Name’, ‘Topic_Label’, ‘Topic_Perc_Contribution’]])
%%
import matplotlib.pyplot as plt
1. Ensure the mapping is applied (if not already done)
topic_labels = { 0: “Temporal Persistence”, 1: “Vulnerable Rawness”, 2: “Tangible Evidence”, 3: “Stagnation & Regret”, 4: “Calculated Intent” } df_topics[‘Topic_Label’] = df_topics[‘Dominant_Topic’].map(topic_labels)
2. Count the occurrences of each label
We use the list of values from our dictionary to keep the 0-4 order
ordered_labels = [topic_labels[i] for i in range(5)] topic_counts = df_topics[‘Topic_Label’].value_counts().reindex(ordered_labels).fillna(0)
3. Create the bar chart
plt.figure(figsize=(12, 6)) topic_counts.plot(kind=’bar’, color=’maroon’, edgecolor=’black’)
plt.title(‘Frequency of Lyrical Themes in “Breach”’, fontsize=14) plt.xlabel(‘Lyrical Theme’, fontsize=12) plt.ylabel(‘Number of Songs’, fontsize=12)
Set the x-ticks to your specific labels
plt.xticks(rotation=45, ha=’right’) plt.tight_layout() plt.show()
%%
import matplotlib.pyplot as plt
Count how many songs fall into each topic
topic_counts = df_topics[‘Dominant_Topic’].value_counts().sort_index()
Simple bar chart
plt.figure(figsize=(10,6)) topic_counts.plot(kind=’bar’, color=’maroon’) plt.title(‘Number of Songs per Lyrical Theme’) plt.xlabel(‘Topic ID’) plt.ylabel(‘Number of Songs’) plt.xticks(rotation=0) plt.show()
%% [markdown]
### Breach Bar Chart
%%
Convert your Counter object to a sorted DataFrame
df_counts = pd.DataFrame(counts.most_common(15), columns=[‘Word’, ‘Count’]) df_counts = df_counts.sort_values(by=’Count’, ascending=True)
Define Red/Orange/Yellow palette
You can add as many hex codes as you like to get the exact look
warm_colors = [ ‘#4A0E0E’, # Dark Garnet ‘#7B0828’, # Deep Ruby ‘#9B1B30’, # Chili Pepper ‘#B22222’, # Firebrick ‘#C0392B’, # Pomegranate ‘#D72638’, # Crimson ‘#E74C3C’, # Alizarin ‘#F0544F’, # Soft Coral ‘#FF4500’, # Orange Red ‘#FF5733’, # Persimmon ‘#FF7F50’, # Coral ‘#FF8C00’, # Dark Orange ‘#FFA500’, # Orange ‘#FFC300’, # Sunflower ‘#F1C40F’ # Maize ]
Create the plot
plt.barh(df_counts[‘Word’], df_counts[‘Count’], color=warm_colors) plt.xlabel(‘Frequency’) plt.title(‘Breach Words’) plt.tight_layout()
Save or display
plt.savefig(‘breach.png’)
![breach_png]/(breach_lab/assets/breach.png)
%% [markdown]
### Breach Lollipop
%%
1. Prepare your data (from your existing code)
df_counts = pd.DataFrame(counts.most_common(15), columns=[‘Word’, ‘Count’])
Sorting ensures the “longest” lollipops are at the top
df_counts = df_counts.sort_values(by=’Count’, ascending=True)
2. Create the plot
plt.figure(figsize=(10, 8))
Draw the “stems” (horizontal lines)
plt.hlines(y=df_counts[‘Word’], xmin=0, xmax=df_counts[‘Count’], color=’yellow’, alpha=0.5, linewidth=2)
Draw the “candies” (the dots)
plt.plot(df_counts[‘Count’], df_counts[‘Word’], “o”, markersize=10, color=’orangered’)
3. Styling
plt.xlabel(‘Frequency’, fontsize=12) plt.title(‘Breach Lollipop’, fontsize=14, fontweight=’bold’) plt.grid(axis=’x’, linestyle=’–’, alpha=0.3) # Horizontal grids help track the values
Add labels to the end of the dots (optional)
for i, count in enumerate(df_counts[‘Count’]): plt.text(count + 0.2, i, str(count), va=’center’, fontsize=11)
plt.tight_layout()
4. Save or display
plt.savefig(‘breach_lollipop.png’) plt.show()
![breach_lollipop]/breach_lab/assets/breach_lollipop.png
%% [markdown]
### Breach Bubble Chart
%%
1. Prepare your data (from your existing code)
df_counts = pd.DataFrame(counts.most_common(15), columns=[‘Word’, ‘Count’])
2. Setup positions for the bubbles (random or ordered)
We use random X and Y coordinates to spread the bubbles out
np.random.seed(42) # Keeps the layout the same every time you run it df_counts[‘x’] = np.random.rand(15) df_counts[‘y’] = np.random.rand(15)
3. Create the plot
plt.figure(figsize=(12, 8))
Squaring the count allows for more dramatic size differences
df_counts[‘size’] = df_counts[‘Count’] ** 2
s= size of bubbles (multiplied by a factor to make them visible)
c= color (can be mapped to the count)
scatter = plt.scatter( df_counts[‘x’], df_counts[‘y’], s=df_counts[‘Count’] * 1200, # Adjust the multiplier (100) based on your data scale c=df_counts[‘Count’], cmap=’YlOrRd’, alpha=0.6, )
4. Add the word labels inside or near the bubbles
for i, row in df_counts.iterrows(): plt.annotate( f”{row[‘Word’]}\n({row[‘Count’]})”, (row[‘x’], row[‘y’]), ha=’center’, va=’center’, fontsize=10, fontweight=’bold’ )
5. Styling
plt.title(‘Breach Bubble Frequency Chart’, fontsize=16) plt.axis(‘off’) # Hide axes for a cleaner “cloud” look plt.colorbar(scatter, label=’Frequency’) # Optional: adds a color scale legend
plt.tight_layout() plt.savefig(‘breach_bubble.png’) plt.show()
%% [markdown]
### Breach Tree Map
%%
1. Prepare your data (from your existing Counter object)
df_counts = pd.DataFrame(counts.most_common(15), columns=[‘Word’, ‘Count’])
2. Create the plot
plt.figure(figsize=(12, 8))
Define colors (using a nice Seaborn palette)
colors = sns.color_palette(‘viridis’, len(df_counts))
Create labels that include the word and the count
labels = [f”{row[‘Word’]}\n({row[‘Count’]})” for i, row in df_counts.iterrows()]
3. Draw the Treemap
squarify.plot( sizes=df_counts[‘Count’], label=labels, color=warm_colors, alpha=0.8, text_kwargs={‘fontsize’: 10, ‘fontweight’: ‘bold’} )
4. Styling
plt.title(‘Breach Treemap’, fontsize=16) plt.axis(‘off’) # Treemaps don’t need axes
plt.tight_layout() plt.savefig(‘breach_treemap.png’) plt.show()
%% [markdown]
### Breach Word Cloud
%%
Join all words into one string weighted by their count
word_freq = dict(counts.most_common(20))
wordcloud = WordCloud( width=800, height=400, colormap=’YlOrRd’ ).generate_from_frequencies(word_freq)
plt.figure(figsize=(15, 7)) plt.imshow(wordcloud, interpolation=’bilinear’) plt.axis(‘off’) wordcloud.to_file(“breach_wordcloud.png”) plt.show()
%% [markdown]
### Breach Radial Bar Chart
%%
Prepare data
df_counts = pd.DataFrame(counts.most_common(15), columns=[‘Word’, ‘Count’]) n = len(df_counts) angles = np.linspace(0, 2 * np.pi, n, endpoint=False) widths = [2 * np.pi / n] * n
plt.figure(figsize=(10, 10)) ax = plt.subplot(111, polar=True)
Create the bars
bars = ax.bar(angles, df_counts[‘Count’], width=widths, bottom=10, color=plt.cm.YlOrRd(df_counts[‘Count’] / df_counts[‘Count’].max()), edgecolor=’white’)
Add labels
ax.set_xticks(angles) ax.set_xticklabels(df_counts[‘Word’], fontsize=10) ax.set_yticklabels([]) # Hide radial grid numbers plt.title(“Breach Radial”, va=’bottom’)
dpi=300 ensures a high-resolution, professional-looking image
plt.savefig(“breach_radial.png”, format=”png”, dpi=300, bbox_inches=’tight’) plt.show()
%% [markdown]
### Breach Funnel Chart
%%
import plotly.express as px import pandas as pd
df_counts = pd.DataFrame(counts.most_common(15), columns=[‘Word’, ‘Count’])
fig = px.funnel(df_counts, x=’Count’, y=’Word’, title=’Top 15 Lyrics Word Frequency’, color=’Word’, color_discrete_sequence=warm_colors)
Save the figure as a PNG
fig.write_image(“breach_funnel.png”, scale=2) # scale=2 increases resolution
fig.show()
<
%% [markdown]
### Breach Words by Length
%%
1. Properly flatten and split your list of lyrics
word_list = []
if isinstance(filtered_lyrics, str): # If it’s just one string, split it word_list = filtered_lyrics.split() else: # If it’s a list (lyrics_list), iterate through each song and split for song in filtered_lyrics: if isinstance(song, str): word_list.extend(song.split())
Now word_list contains every individual word from every song
print(f”Total words processed: {len(word_list)}”)
%%
1.5 Initialize the dictionary
word_lengths = {}
2. Categorize
for word in word_list: # Skip empty strings if any if not word: continue
length = len(word)
if length not in word_lengths:
word_lengths[length] = []
word_lengths[length].append(word)
3. Display the results correctly
print(f”Total words processed: {len(word_list)}”) print(“-“ * 30)
for length in sorted(word_lengths.keys()): # Get unique examples so we don’t see the same word 5 times unique_words = list(set(word_lengths[length])) count = len(word_lengths[length]) examples = unique_words[:5]
print(f"Length {length:2}: {count:4} words found. Examples: {examples}") # %% from collections import Counter
print(“— TOP 5 WORDS BY LENGTH (1-14) —”)
Ensure word_lengths exists from your previous flattening step
for length in range(1, 15): if length in word_lengths and word_lengths[length]: # Count occurrences of each word of this specific length counts = Counter(word_lengths[length])
# Get the top 5 most frequent words
top_words = counts.most_common(5)
# Format and print the words and their frequencies
word_list_str = ", ".join([f"'{word}' ({count})" for word, count in top_words])
print(f"Length {length:2}: {word_list_str}")
else:
print(f"Length {length:2}: No words found.") # %% from collections import Counter
print(“— TOP 5 WORDS BY LENGTH (1-14) —”)
Loop through lengths 1 to 14
for length in range(1, 15): # Check if we actually have words of this length to avoid errors if length in word_lengths and word_lengths[length]: counts = Counter(word_lengths[length]) top_words = counts.most_common(5)
# Formatting the output for readability
word_list_str = ", ".join([f"'{word}' ({count})" for word, count in top_words])
print(f"Length {length:2}: {word_list_str}")
else:
print(f"Length {length:2}: No words found.") # %% import numpy as np
Prepare data
lengths = sorted(word_lengths.keys()) counts = [len(word_lengths[l]) for l in lengths]
Set up the polar projection
plt.figure(figsize=(8, 8)) ax = plt.subplot(111, projection=’polar’)
Create the angles for the bars
angles = np.linspace(0, 2 * np.pi, len(lengths), endpoint=False) width = (2 * np.pi) / len(lengths)
Use your expanded reds for the colors
colors = plt.cm.YlOrRd(np.linspace(0.4, 1, len(lengths)))
bars = ax.bar(angles, counts, width=width, color=colors, edgecolor=’white’)
Add labels
ax.set_xticks(angles) ax.set_xticklabels([f”{l} letters” for l in lengths]) ax.set_yticklabels([]) # Hide the radial grids for a cleaner look plt.title(“Lyric Density Spiral”, va=’bottom’, fontsize=15) plt.show()
%% [markdown]
### Breach Statistical Distribution of Word Lengths
%%
import seaborn as sns
Create a list of the lengths of every word in your dataset
lengths_data = [len(w) for w in word_list]
plt.figure(figsize=(10, 4)) sns.boxplot(x=lengths_data, color=”#D72638”) plt.title(‘Statistical Distribution of Breach Lyric Word Lengths’) plt.xlabel(‘Number of Letters’) plt.show()
%% [markdown]
### Breach Word Cloud Color-Coded by Length
%%
from wordcloud import WordCloud
Create a single string where words are repeated based on frequency
text_for_cloud = “ “.join(word_list)
Custom color function: Redder for longer words, Yellower for shorter
def color_by_length(word, font_size, position, orientation, random_state=None, **kwargs): length = len(word) if length <= 3: return “rgb(255, 215, 0)” # Yellow elif length <= 6: return “rgb(255, 140, 0)” # Orange else: return “rgb(215, 38, 56)” # Red
wc = WordCloud(background_color=”black”, width=800, height=400, color_func=color_by_length).generate(text_for_cloud)
plt.figure(figsize=(15, 10)) plt.imshow(wc, interpolation=’bilinear’) plt.axis(“off”) plt.savefig(‘breach_length_distribution.png’, format=’png’, bbox_inches=’tight’)
%%
import matplotlib.pyplot as plt
1. Prepare the data
We sort the lengths (keys) so the X-axis goes 1, 2, 3…
lengths = sorted(word_lengths.keys()) counts = [len(word_lengths[l]) for l in lengths]
2. Create the Bar Chart
plt.figure(figsize=(10, 6)) bars = plt.bar(lengths, counts, color=’red’, edgecolor=’navy’)
3. Add labels and title
plt.xlabel(‘Word Length (Number of Letters)’, fontsize=12) plt.ylabel(‘Frequency (How many words)’, fontsize=12) plt.title(‘Distribution of Word Lengths Across All 12 Notebooks’, fontsize=14)
4. Make it look professional
plt.xticks(lengths) # Ensures every length (1, 2, 3…) is labeled on the bottom plt.grid(axis=’y’, linestyle=’–’, alpha=0.7)
Optional: Add the exact count on top of each bar
for bar in bars: yval = bar.get_height() plt.text(bar.get_x() + bar.get_width() / 2, yval + 5, yval, ha=’center’, va=’bottom’)
plt.savefig(‘word_length_distribution.png’)
![breach_wc2]/breach_lab/assets/breach_length_distribution.png
tags: