{"id":8915,"date":"2024-05-09T11:33:23","date_gmt":"2024-05-09T15:33:23","guid":{"rendered":"https:\/\/sites.temple.edu\/tudsc\/?p=8915"},"modified":"2024-05-09T11:33:23","modified_gmt":"2024-05-09T15:33:23","slug":"text-mining-pitchfork-music-reviews-for-descriptors-of-vocal-sounds","status":"publish","type":"post","link":"https:\/\/sites.temple.edu\/tudsc\/2024\/05\/09\/text-mining-pitchfork-music-reviews-for-descriptors-of-vocal-sounds\/","title":{"rendered":"Text Mining Pitchfork Music Reviews for Descriptors of Vocal Sounds"},"content":{"rendered":"\n

By R.J. McGhee<\/p>\n\n\n\n\n\n\n\n

Introduction<\/h2>\n\n\n\n

Have you ever wondered how music reviewers write about vocalists in their reviews? How do music reviewers depict the way vocalists sing? Which adjectives and verbs help create a depiction of vocal styles? <\/p>\n\n\n\n

As a student in a master’s program in Information Science and Technology and a musician, my goal for the Cultural Analytics Certificate Practicum project is to use my background knowledge to explore how reviewers of music albums describe sound, specifically the description of the musical properties of vocal sounds. I selected a dataset of music reviews from the prominent music journalism platform, Pitchfork, for this analysis.<\/p>\n\n\n\n

To analyze the words used to describe vocal sounds in Pitchfork music reviews, I employ natural language processing techniques, utilizing Python libraries like pandas<\/code>, and SpaCy<\/code> to tally a list of Tagg’s “Sound Descriptor Words” within the Pitchfork Reviews. To see my full code, follow this link<\/a>.<\/p>\n\n\n\n

Background:<\/h2>\n\n\n\n

In his book, \u201cMusic Meanings,\u201d Musicologist Phillip Tagg<\/a> investigates the ways we discuss music and its significance to the world (Tagg, 2013). Tagg identifies the phenomenon of individuals talking about vocals using the “Aesthetic Perspective.” Tagg defines the “Aesthetic Perspective” in terms of how vocal sounds are perceived, interpreted, and reacted to by listeners (Tagg, 2013).<\/p>\n\n\n\n

Tagg created a list of ‘Directly Sound Descriptive Adjectives and Verbs’ to catalog the language commonly used when discussing the qualities of vocal sound. See his list below:<\/p>\n\n\n\n

<\/div>\n\n\n\n

Sound Descriptor Word List:<\/p>\n\n\n\n

hum, high-pitched, whiny, squeaky, booming, low-pitched, deep, full-throated, gruff, breathy, husky, guttural, distinct, harsh, indistinct, muffled, plaintive, rasping, roaring, shrill, stammering, loud, declamatory, soft, quiet, monotone, lispy, bird-like, hoarse, throaty, babble, bark, bawl, belch, bellow, bleat, blubber, boom, buzz, cackle, caterwaul, chant, chatter, chuckle, chirp, cluck, complain, cough, croak, croon, cry, declaim, denounce, drone, exclaim, gargle, gasp, giggle, growl, grumble, gurgle, hiccup, hiss, hoot, howl, lament, laugh, lilt, moan, mumble, mutter, praise, preach, proclaim, pronounce, quack, quip, rant, recite, roar, scream, screech, shout, shriek, sigh, snap [at], snarl, snigger, snore, snort, sob, spit, splutter, squawk, squeak, stammer, stutter, twitter, ululate, wail, warble, weep, wheeze, whimper, whine, whinge, whisper, whistle, whoop, yammer, yap, yawn, yell, yelp, yow<\/p>\n\n\n\n

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By examining the frequency of these Sound Descriptors and their association with genre categorization in Pitchfork reviews, I hope to find insights into whether and how reviewers are using sound descriptors in their writing. This involves tallying the occurrences of words from Phillip Tagg’s list in sentences discussing vocal sounds.<\/p>\n\n\n\n

The Dataset<\/h2>\n\n\n\n

For my dataset, I downloaded a set of 24,169 Pitchfork reviews spanning from January 5, 1999, to December 12, 2021 that Nolan Conaway web-scraped from the Pitchfork website<\/a>.<\/p>\n\n\n\n

I chose this dataset because of its accessibility and its cultural importance to music journalism. Pitchfork is a cornerstone of the online music review culture. Pitchfork<\/a> started in 1996 as a small music review website and has now grown to be one of the most well-known music review websites internationally (\u201cThe History of Pitchfork\u2019s Reviews Section in 38 Reviews,\u201d 2021).<\/p>\n\n\n\n

The dataset can be downloaded for free as a SQLite3 file. The tables contain columns which include the name of the artists, the title of the album, the score, the author of the review, the primary genres, the release year of the album, the publication year, and the review itself. <\/p>\n\n\n\n

Text Cleaning Process<\/h2>\n\n\n\n

My first step in this research project was to consolidate these tables into one functional table, which I can further filter to answer my research questions. My main avenue of organizing this data was to use a library in Python called pandas<\/a><\/code>. I first converted each table into pandas dataframes using SQL calls, which then I was able to connect each table together using their respective table keys.\u00a0I then created a new pandas<\/code> dataframe by genre. The genres are assigned to every article in the dataset.<\/p>\n\n\n\n

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Pitchfork Genres<\/h3>\n\n\n\n
Experimental<\/td>Rock<\/td>Electronic<\/td>Folk\/Country<\/td>Rap<\/td>Pop\/R&B<\/td>Jazz<\/td>Global<\/td>Metal<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

I also needed to prepare the data for analysis, so I applied SpaCy’s<\/a> lemmatization feature to each sentence, facilitating the extraction of root forms of words. This step ensures the identification of terms from the sound descriptor and vocal term list, accommodating for the variations in word forms due to prefixes or suffixes (“Linguistic Features \u00b7 SpaCy Usage Documentation,” n.d.).<\/p>\n<\/div>\n\n\n\n

Text Mining Process<\/h2>\n\n\n\n

To begin mining these texts, I needed to verify that I counted only instances where the sound descriptive words described vocal sounds. I created a basic list of words that would normally describe vocals and appear in reviews discussing vocals. See the list below:<\/p>\n\n\n\n

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