Overview

PillowStruck uses spotify APIs and musixmatch web crawling to implement a API wrapper for analysis of specific artists, albums and tracks with pyhton.

• Group name: PillowStruck
• Members of the group: Nyx Zhang, Tia Wang

Motivation: We listen to music either for a rest to the soul or primal passion of human nature. Some of us enjoy more on the melodies or rhythms, some focus more on the lyrics, and some who can't be ignored are there to support their favorite artists. In this API, we will present both the features of the musical part and lyrical part for users to dive deep into the songs or the artisit they are interested.

Name Meaning: You just worked overnight because of so much pressure recently. Exhausted, you frantically struck your pillow to vent. Your pillow cried and felt not fair so struck, then you couldnot sleep anymore. You are now totally struck down.

Quick Start

User Interface

To be more impressing on user interaction, we deployed a website on AWS with free tier.

Step 1: Search any keyword, including the "track", "artist" Object.	Step 2: Get the results contain tracks and artists list.

Step 3.1: Explore Tracks Click Tracks' "explore" to read track's detail, or click "home" to back to the search page.

lyrics word cloud	sentiment analysis	lyrics txt detail

Step 3.2: Explore Artists Click Artists' "explore" to read artist detail, or click "home" to back to the search page.

artist activity analysis	Top tracks list	latest album list

Note: However, lyrics acquirement and sentiment analysis can only run on private server instead of the above web server, due to financial and time restrictions. Because the free web server is so under-configured that it can't run deep learning that it cannot run Sentiment Analysis. In addition the main contributors, who still have tons of assaignments and upcoming quizzes, do not have time to write anti-block script for web crawling of musixmatch, by whom our AWS public IPs were blocked.

Installation guides

to be completed...

Usage

PillowStruck
|
│   README.md
│   cofig.yaml
│   main.py
|   requirements.txt
|   documents
└───apps
    │
    │   spotify_stare.py
    │   lyrics_rub.py
    │   lyrics_struck.py
    │   artist_struck.py
    └───[model apps]
    |
    |   app.py
    |   templates
    |   statics
    └───[web apps]

Users can input a keyword, PillowStruck will call Spotify APIs to get search results, results will contain "Tracks" and "Artists" pandas.DataFrames. The "Tracks" df contains features like track name, release_date, popularity, artist and album, and "Artists" df contains features like artist name, followers and genres. PillowStruck also give a choice for users to explore specific track or artist details.

In the "Track" detail, it shows wordcloud and Sentiment Analysis of lyrics. The wordcloud uses package wordcloud to compute and visualize, while Sentiment Analysis uses package tweetnlp to compute and uses package altair to visualize. Sentiment analysis shows the sentiment label and its score.
In the "Artist" detail, it shows the activity degree along years of this artist by the count of their released tracks. Also it shows airtist's top 10 tracks and latest 10 albums.

Run the website

$ cd apps
$ python app.py

Search keyword

from spotify_stare import SpotifyStare
q='David%20Bowie' #replace to your keyword
search_results = SpotifyStare(q)
artists, albums, tracks = search_results.search() # the results DataFrame of artists, albums, tracks individually.

Get track lyrics

q='David%20Bowie' #replace to your keyword
lyrics_file = get_lyrics(q) # write the lyrics in a file apps/lyrics/{Artist}_{Track}.txt

Generate track wordcloud picture and sentiment analysis

from lyrics_struck import LyricsStruck
lyrics_analysis = LyricsStruck(lyrics_file)
lyrics_analysis.wordcloud() # wordcloud picture in apps/static/wordcloud_{Artist}_{Track}.png
lyrics_analysis.visualize_sentiment() # sentiment analysis picture in apps/static/lyrics_{Artist}_{Track}.png

Generate artist activity analysis and more

from artist_struck import ArtistStruck
ats = ArtistStruck()
at_name = ats.get_artist_track_numbers_for_years(q) # artist activity analysis  picture in apps/static/{artist}.png
pop_tracks = ats.get_artist_top_tracks_by_name(q) # get top 10 popular tracks
pop_albums = ats.get_artist_albums_by_name(q) # get latest 10 albums

References

Spotify APIs: The Spotify APIs provides developers with a wealth of data, such as information about musicians and song albums, users' search history, song lists, and more. We can use this vast amount of data to build our own systems.
Musicmatch APIs and Scraping: Musicmatch APIs mainly provide the lyrics of songs.