And many artists operate with different bands-Neil Young solo, Neil Young & Crazy Horse, and Neil Young & the Shocking Pinks, for instance. Rank'em has neglected to import data from some singles and soundtrack albums, which means several of my favorite songs-"Hey Jude," "Hey Bulldog," and "It's All Too Much" by The Beatles, "Positively Fourth Street" by Dylan-are completely missing from the site. There are other data-related problems as well: albums often have the wrong years associated with them and some albums are listed twice with two different years.
This can really mess up the rankings, as votes are split among multiple versions of the same song. For instance, the Who-notorious for repackaging songs and albums-has some songs show up five times. This completeness is a drawback when you're just trying to find a song and rank it. The site draws song data from the user-maintained MusicBrainz database, but MusicBrainz is obsessively complete, listing multiple releases for the same album. Unfortunately, Rank'em is only as good as the song data it offers, and that data's flawed right now.
#Song rating website code#
You can try it yourself: enter the code "CNET" when you register (you'll also need a valid e-mail address) and you can start ranking songs and reading aggregate rankings in a few minutes. Although the site is still in beta, so far it looks pretty promising: for example, the results for Jimi Hendrix place well-deserved fan favorites like "Spanish Castle Magic" and "Voodoo Child (Slight Return)" above overplayed radio hits like "Purple Haze" and "Foxy Lady." When other users search for that artist's name, they'll see aggregate rankings of the songs, weighted by each voter's fanstanding, along with links to buy them on iTunes, Amazon, and eMusic (assuming they're available through these sources). Rank'em urges me to pick the original version of any song I select as a favorite, but how am I supposed to know which version of "Boris the Spider" is the original? They're all exactly the same, but released at different times on different albums. If you pick 20 songs, they'll let you give yourself up to 10 points. If you only know a handful of Modest Mouse songs, Rank'em will assume you can't be a very big fan, and will only let you rate yourself on a scale of one to five. These self-ratings are limited by the number of songs picked. Users must also rate their level of enthusiasm-or "fanstanding" as the site calls it-for each artist. So Rank'em has users select particular artists, then asks them to choose between 5 and 20 songs and rank them in order. This obviously wouldn't work without some structure-asking users to pick their 10 favorite songs at random, for instance, would be too scattershot and yield useless results.
Rank'em (located at ), now in beta testing, uses this human urge as the basis for a crowdsourced song recommendation engine. It gained popularity in December 2020 for its snarky roasts of users' listening habits.Everybody likes contributing an opinion, especially in subjects on which we consider ourselves experts. How Bad is Your Spotify is an AI that judges your music taste.
It also shows how your top tracks, artists, and genres have changed since the last time you used Stats for Spotify.
It shows you your top tracks, artists, and genres organized by the previous four weeks, last six months, and all time. Stats for Spotify is a classic Spotify data analyzer.
#Song rating website how to#
So if you're the type of person who spends your days counting down to Spotify Wrapped or who regularly analyzes your listening habits to understand yourself better, you'll love these clever tools.Ī heads up that each of these websites requires you to log in to your Spotify and grant the website access to your Spotify data, and we've included directions at the bottom for how to remove each site's access once you've tried them out. We've scoured the corners of the internet and collected our 12 favorite websites that analyze your Spotify data. What genre do you listen to the most? How obscure are your favorite artists? And, wait, you listened to "Alone" by Heart how many times!? Mining your own Spotify data is like accessing a musical window into your own soul.
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