nawerjunky.blogg.se - Welcome to trap street lyrics

The second part describes Armstrong writing to her after six months of living at his own place, now being happy to live on his own.Ī video was released for the song. The first verse is Armstrong talking to his mother after three weeks of leaving her place, telling her that he's scared about being on his own. The song is sung as Billie is talking or sending a message to his mother, after moving out of her house. The song is played with the guitar tuned a half-step down, as are many of their Dookie songs. It's no place you want to walk around at night, but it's a neat warehouse where you can play basketball and stuff. It's about West Oakland, living in a warehouse with a lot of people, a bunch of artists and musicians, punks and whatever just lived all up and down, bums and junkies and thugs and gang members and stuff that just lived in that area. The house was quite broken-down but to them it became home, and this feeling is described in the song.īillie Joe Armstrong said this of the song: It is based on the band's experience moving out of their parents' houses and into an abandoned house in Oakland, California where the band members, along with a number of others, lived without paying rent. The lyrics of the song were written by Billie Joe Armstrong, and the music by Armstrong with Mike Dirnt and Tré Cool.

Track 2 was recorded on Maat Jannus Landing, St.

The output of the t-SNE algorithm mapped rappers to a two-dimensional space based on the similarity of their lyrics. We then took the resulting matrix and fed it into t-SNE with a perplexity parameter set to 40. We took the tf-idf matrix and first reduced it to 50 dimensions using Truncated singular value decomposition (SVD). T-SNE: To create our map of rappers, we used a dimensionality reduction technique called t-SNE. Higher cosine values imply more similarity, with an upper bound of 1 when the vectors are perfectly similar. In our case, that means taking the tf-idf vector for an artist and comparing it to that of another. This rules out words that are repeated over and over by one or a few artists (think “controlla” for Drake).Ĭosine Similarity: Cosine similarity is a common way of calculating the similarity between two vectors by taking the cosine of the angle between them. That means, to be considered in our tf-idf computation, a term had to be used at least once by 10% of the artists in our dataset. 2) We also set a “cut-off” for document frequency of 0.1. You can read more about why you might want to do sublinear scaling here. 1) We used sublinear scaling on the term frequencies, giving us a little more variation across our lists. We made two slight modifications to the traditional formula. The words with the ten highest tf-idf scores for each artist were deemed the words “most unique” to him or her. For a given word, we count the number of times it occurs in one rapper’s catalogue (its term frequency) and divide by the number of artists that use it across the hip-hop corpus (its document frequency). Each rapper gets assigned a tf-idf score for every word in the hip-hop corpus.

TF-IDF: to determine the words that characterize each hip-hop artist, we used a technique called term frequency-inverse document frequency (tf-idf). other genres, but was only used 116 times in 26 million words. For example “lowrider” had a 255:1 ratio in hip hop vs.

These all had fewer than 1,000 occurances in the hip hop corpus. the general corpus, were still rather rare words. Some words were filtered from this list that, while indexing high in hip hop vs. We then compare that to the same math for the general corpus. For example, this is # of appearences in hip hop corpus divided by total words in hip hop corpus. Most Hip Hop: To find the words most “characteristic” of hip-hop, we computed the odds that a word appeared in the hip hop corpus vs. This included efforts to standardize spelling, remove capitalization, and apply light lemmatization. We filtered hip-hop artists by cross-referencing their primary genre on MusixMatch.įor consistency, The hip hop data was cleaned using the same script as the LyricFind corpus.

The general music corpus was formed using data from LyricFind.