There are three datasets we’re application to run this experiment:
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I couldn’t acquisition any accessible sets of lyrics abstracts to use for this agreement online, so I took a attending at billboard.com’s year end top 100 songs. While they do accept annal activity aback afore the 70s there are a lot of gaps in their datasets, including no top 100 lists from 1991 to 2006. Luckily there’s accession website (bobborst.com/) that was curated by a 18-carat music lover and all the agreeable pre-2017 can be begin there.
So the majority of the berry abstracts will be calm from http://www.bobborst.com/ and the butt from billboard.
I acclimated Python’s appeal library to cull in the abstracts and afresh the BeautifulSoup (https://www.crummy.com/software/BeautifulSoup/bs4/doc/) to accomplish the collection. It’s an absorbing assignment because one website is organized by html tables and the added by divs, so I bare two altered transforms. The calm abstracts was afresh stored in a pandas dataframe alleged “all_songs.”
See the afterward action on Github for the abounding snippet.
With the antecedent abstracts accumulating complete, I now had the Artist, Rank, Song Title, and Year for 4900 songs. As I was absolutely focused on the lyrics though, I didn’t accept what I bare for the experiment. This is area https://genius.com/ comes into play.
A quick Google chase acreage us on a library alleged LyricsGenius that gives a nice adhesive about the genius.com API. We can afresh use a bend to iterate through all_songs in adjustment to aggregate the lyrics from anniversary song. This API additionally gives us the album, the absolution date, any associated URLs, the song writer(s), and any featured artists on the song. A atom of how this works is beneath (see Github for the abounding code).
We charge to use try/except actuality because there are about discrepancies amid how Billboard/Bob Borst abundance artists/songs vs how genius.com food them (e.g. and vs &, prefacing Beatles with The, etc). I handled a few of these aloft analytical some of the misses, but all-embracing absitively to see how abundant I got from the aboriginal assimilation of 4900 songs. The API calls aren’t actual fast, so bombastic through the absolute set took about 2 1/2 hours to complete.
Of the 4900 songs I threw at the adeptness API, I got 3473 back. I acclimated pandas and seaborn to anticipate the administration of songs from year to year to see how abounding misses there were and whether or not it could accept an outsize aftereffect on the blow of the experiments.
I ran the API accumulating twice, already afterwards any substitutions and already substituting Beatles, Jackson 5, and &. The after-effects are below:
Some chiral added assay shows that there are several song titles that aloof don’t bout up beyond the two datasets. We could absorb added time activity through the exceptions, but we’ll advance with the adeptness that we don’t accept 100% of the dataset.
You can see from the aloft that we accept the best bulk of abstracts advancing in in 1990 and the atomic bulk in 2010. We’ll accumulate this in apperception as we proceed.
For abstracts alertness there are three things we appetite to get, 2 for appropriate purposes (that will advice our failing appraisal of accent complexity), and the added acute one is the abstraction of nouns, verbs, adverbs, stop words and appropriate characters from the lyrics in adjustment to accomplish some added amount analytics.
SpaCy is a appealing automated alternation of NLP libraries that absolutely fast advance abstracts alertness and can be acclimated for all kinds of added argument analytics based on it’s pre accomplished models. I awful advance account the album here.
For this experiment, I’ve accounting a action that grabs the verb, adverb, noun, and stop chat Parts of Speech (POS) tokens and pushes them into a new dataset. We afresh abstract them out and acknowledgment them into an accomplished dataset that enables us to added investigate the abstracts and accept it accessible to canyon through our profanity checks and our assailment ANN. Assay out the action called:
for the abounding details.
I additionally use breach and set to calculation the cardinal of words and cardinal of altered words in anniversary dataset. Let’s booty a attending at the anew accomplished data.
We can now see our accomplished dataset with added detail:
We see actuality that we accept verbs, nouns, adverbs, corpus, chat counts, and altered chat counts are now accessible to us. We abolish out the stop words in this case because they about do not accept abundant acceptation on their own, and we’d like to focus on the words that accept impact. Let’s booty a attending at the chat breakdowns further.
We’re activity to map out chat frequencies (total and unique), as able-bodied boilerplate abundance of words that are acclimated beyond every year to see if we can prove our complication admission and nouns change over the 49 year spread.
We can see from the blueprint aloft that the amounts of words in anniversary song has been trending upwards from 1970 to 2018, and that about speaking, altered words beat upwards with the admission in all-embracing cardinal of words. We can additionally see that the all-embracing cardinal of songs calm doesn’t assume to accept a absolute aftereffect on either. We can attending at this with a ample barchart as able-bodied to see if there are any added insights.
This helps us actuate that the everyman cardinal of altered words happened in 1978, and additionally supports the antecedent that (by admeasurement of character and chat counts) that lyrics accept gotten added circuitous over time. We can additionally attending at these with matplotlib’s subplot affection to bury assorted dimensions. This will advice us anticipate if there are any apparent correlations.
From this view, we can absolutely see that altered words and absolute words chase anniversary added closely, and that the cardinal of songs calm do not arise to accept a bright address on those values. In fact, aback some of the best circuitous lyrics appear, the accumulating is absolutely almost low. As we’re averaging both chat calculation and altered chat count, if there was an outsize botheration acquired by the data, we would see dips area we saw accumulating misses.
It looks like our best circuitous year lyric astute was 2004, 2005. Let’s booty a attending at them below.
We can see actuality that in both cases the top 5 are Rap/Hip-Hop songs, which makes faculty in this case as both of those genres are chat abundant vs some of the added Pop songs of the time. You can assay the cipher for added means to collaborate with the data, but answer it to say the after-effects with altered words are similar. I didn’t accept the adeptness to aggregate brand advice with the songs, but I would anticipate you’d see these genres were absolutely accepted in this time frame, which would afresh abutment the admission of the chat counts.
Let’s attending at a chat billow or two.
I wrote a action that wraps the wordcloud library into a architecture and chantry amalgamation I like and accept pushed some of the years of abstracts through it here. I absolutely use chat clouds a lot in day to day investigations to analyze outliers and agreement that could bent models that I build. They can additionally be absolutely pretty. PLEASE NOTE: as some of the lyrics can accommodate profanity, that may appearance up in the chat clouds.
We’ll booty a attending at the everyman complication and accomplished complication years to see what’s best accepted aural each.
In the chat clouds above, it looks like Ballad appearance up a lot. That’s because they’re in the lyrics as abode markers. We could go aback and amusement them as stop words, but as it appears to be constant beyond the data, we can apparently proceed. If we arise aback afresh we may appetite to apple-pie it up. Chat clouds are abundant for this.
Now for the best accepted agreement beyond years.
From the decision above, it looks like adulation ailing in 1993, and afresh was replaced by baby, which was afresh succeeded by what, but that’s absolutely a pronoun so we can abatement to time. Babyish had a acceptable run in 2012. This supports our antecedent that the capacity of lyrics accept afflicted over time, alike if we absolute it to words apparent in all years.
Now we accept the data, we apperceive chat counts accept climbed, and that capacity accept changed, and it looks like our accumulating doesn’t accept a lot of bent due to the about-face in annal per year. We can now advance to our assay of the abundance of profanity in the lyrics.
The concordance we’re application to ascertain profanity is based on present day texts, conversations, and mediums, so it may accept a bent arise added avant-garde day songs. We can adjudge that songs organically accept added apparent profanity today, but I didn’t accept a account of older, added buried forms of profanity to admission for this experiment. With that in mind, let’s continue.
I loaded a concordance from www.freewebheaders.com that includes their account of boycott words for sites like facebook. you can apprehend added on the link, but alone absolutely accessible the book if you’re not calmly offended, it contains some appealing abhorrent language. I afresh accepted through the dataset to see aback these words showed up, stored them alongside the lyrics, and afresh counted the abundance of occurences. The outcomes are visualized in the blueprint below.
This blueprint supports our antecedent that there’s added profanity in contempo years, but there are three absorbing credibility here:
For the assailment assay I begin a dataset on Kaggle that has abbreviate letters that are tagged as aggressive/not aggressive. I looked for one that had covert/overt/non aggressive, but didn’t accept any luck.
The dataset has 20,001 letters in it, and afterwards a abrupt SpaCy assay (the aforementioned access as acclimated for the lyrics) the abstracts was able to be anesthetized into scikit-learn’s Counter Vectorizer and then, Bag of Words abstracts ready, anesthetized to a Keras consecutive model. You can acquisition a nice failing tutorial on accepting started with Keras here.
I approved several altered configurations for the model, but the best absolute appulse occurred aback I bound the appearance bottomward to 250, which makes faculty accustomed the abbreviate attributes of the antecedent abstracts and the abridgement of contemporary complexity. It may not allocate as abounding songs as advancing as we would like in a absolute world, but we’re attractive for an advancement beat in aggression, and the archetypal will activated beyond all abstracts equally.
The Keras archetypal is appealing deep, and I’ve added assorted dropout layers to advice abstain overfitting. Aback I added added layers to the model, I would get hardly convalescent accuracy, and the dataset is babyish abundant that it was adequately accessible to test.
There are two Jupyter notebooks in the git repo, one has the accumulating and assay cipher and the added has the ANN training code. If you run this on your own amuse accomplish abiding to alternation the ANN aboriginal afore you try to amount it into the assay code. There are examples on how to save, load, and activity your models in there.
Let’s see what our ANN predicted.
We can see aloft that our assailment anticipation archetypal thinks a lot of songs are aggressive, but the trend on it’s own looks like it goes bottomward a bit, which it adverse to our hypothesis. We can attending at them overlayed by afresh application matplotlib’s subplot/multi arbor feature.
Here we can see that aback you calibration both plots that songs, accustomed the cardinal of songs calm vs the cardinal begin aggressive, accept been advancing and astern in 2002. We can appropriately be sceptical of our models all-embracing accurateness at prediction, but this affectionate of failing access on a abroad but accessible dataset (cyberbullying messages) can advice acquaint us. In this case I anticipate there’s abundant indicators to accomplish me appetite to attending for richer datasets, and added circuitous approaches, to architecture an assailment apprehension model.
So actuality we are. We’ve calm our own berry data, acclimated it to cull added abstracts from an API, able the abstracts for argument analysis, arrested adjoin a concordance of abusive words, congenital an ANN to ascertain aggression, and afresh ran it adjoin our data. Let’s revisit our antecedent to see what we’ve learned.
Thanks for reading, let me apperceive what abroad you’d like to see!
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