DOI: 10.7910/dvn/nf5teo
title: Replication Data for: Rhythm of Markets

description: This is the data used in my paper the Rhythm of Markets (https://www.researchgate.net/publication/323560860_The_Rhythm_of_Markets). 



The data is shared here only for the purposes of replication to verify existing findings. Please contact me before use in publication at Hisam.sabouni@cgu.edu



Dataset 1: Emolex_Billboard_Official_Charts_Monthly.csv

Using Mohammad and Turney's (2013) Word-Emotion Association lexicon (EmoLex):

In order to estimate the emotions of the top 100 songs, I compile the lyrics of each song from the Genius developer API, where Genius is a company specializing in collecting song lyrics and gathering musical knowledge. I collect each songs lyrics on the Billboard Hot 100 list and the Official Charts Company’s lists by searching across the Genius developer API, I find matches for 70 songs on average per month of the top 100, where older songs are more difficult to track down. The song lyrics is processed by removal of punctuation and common English stop-words (terms such as the, is, at, which, on, etc.). I iterate through each song’s lyrics and count the total number of words that co-occur within each emotion’s dictionary for each month, across the Billboard Hot 100 lists and Official Charts Company top 100 lists, and normalize the data by the total number of songs we were able to gather lyrics for each month. 
(See page 11 in the paper for more clarification).

This data is in level form and is non-stationary, which is why the paper used a vector error correction model to estimate effects with financial indices and the University of Michigan's Consumer Sentiment Index. 

Dataset 2: Spotify_Billboard_Official_Charts_Metrics_Monthly.csv

From the Spotify developer API, I collect the following metrics: danceability, energy, key, loudness, and valence of songs to provide insights into cultural and perceptual cues of the top 100 songs. Danceability, as you may guess, measures “how suitable a track is for dancing” by using a “combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity.” Energy “represents a perceptual measure of intensity and activity,” with music such as death metal having high energy and music such as Bach having low energy. Key represents the key that a track is played in pitch class notation. Loudness represents the average track loudness in terms of decibels (dB) and lastly, valence describes the musical positiveness conveyed by a given track, where “tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad,
depressed, angry).” I searched through the Spotify API for each song with use of the RSpotify Github repository, where I first search through the API by artist, then through each of the albums I search for a match with the songs on our top 100 lists using regular expressions. Repeating this process for each of the weekly Billboard Hot 100 tracks and monthly for the Official Charts Company top 100 tracks, I am able to extract on average 70 of the top 100 songs for each month, where some of the historical top 100 songs are not readily available on Spotify. With the metrics for each song in hand, I create monthly time-series of the top 100 songs by simply averaging across all songs that were on the Billboard Hot 100 within each month as well as averaging across the Official Charts Company top 100 list. I then average across the Billboard Hot 100 metrics and the Official Charts Company top 100 lists to get our final sentiment indices that represent the sentiment of all of the most popular songs in the United States and United Kingdom. (See pages 7 and 8 in the paper for more clarification).