Since December 2022, I have made a playlist each month containing my favorites of the month. I liked the idea of being able to look back and relive certain memories. Coming up on year three, I wanted to know if there were more patterns of be found.
Collating and analyzing the data, this is what 38 months of saved Spotify tracks say about how my taste and mood.
At a glance:
Since Spotify's API doesn't provide song information like tempo, energy, valence, or "dancability" anymore, I pulled genre tags from Last.fm's API. I pulled each song's lyrics from Genius and ran sentiment analysis on the prose with VADER.
What I didn't expect was that the data would map kinda cleanly onto my life. The month I graduated had the darkest lyrics I'd ever saved. And each trip home to Bangkok shows up as a spike in positive sentiment. And when I moved, I unconsciously replaced a lot of my artist roster.
My monthly listening volume with lyrical sentiment overlaid:
I graduated in May 2024, and it's the single quietest and saddest month in 38 months of data. Only 9 tracks saved. A sentiment score of -0.132, the only time it ever went negative.
Something about endings?
What genres I listen to
My favorites from the last three years are (very roughly) 40% indie, 32% pop, and then a scattering of everything else. These proportions have varied over time.
A few things stand out. Indie (green) was dominant in late 2022 (73% of what I saved that December) and remains the backbone throughout. Pop surges during album cycles: November '23 with Taylor Swift's 1989 TV, June '24 with brat, and August '25 with Sabrina Carpenter. Hip-hop was most present early on and has almost completely faded from my favorites.
Who I dropped
In August 2024, I moved from Bangkok to Vancouver. That month, I favorited only 7 tracks, but 93% were from artists I had never saved before.
Indie (39%)
Taylor Swift, Charli xcx, The 1975
Indie (43%)
Sabrina Carpenter, beabadoobee, wave to earth
Indie (41%)
Sabrina Carpenter, beabadoobee, Charli xcx
Interestingly, moving month had the highest sentiment of any phase — 0.602. I think maybe it was the optimism of moving somewhere new.
Taylor Swift, who'd been in my top 3 for a year and a half, did NOT make the trip... she vanished entirely after April 2024. Sabrina Carpenter and beabadoobee, who'd been hovering in the background, became my new headliners.
Who stuck around
This beautiful chart maps when each of my top artists appeared in my favorites. Bigger dots = more tracks that month. Some artists defined a single era, and others persisted across the whole timeline.
Sabrina Carpenter appeared in 15 different months across 3 years. She's definitely stuck around, and so has beabadoobee.
What I was singing about
The NLP analysis I did on the lyrics of every track tagged five recurring themes. Love dominates (it's pop), but the smaller themes reveal the most.
Every Febuary seem to be pretty loaded (haha): in February 2024, I favorited 32 love-tagged tracks and 15 heartbreak tracks. Nostalgia seems to peak during transition periods: it spiked in Feb '24 (last semester), Aug '25 (leaving home again), and the Dec '22 when I first started tracking.
Going home
Maybe the cleanest pattern in the entire dataset: every time I go back to Bangkok, the music I listen to is happier.
For context, my overall average is 0.46. December 2025 — two weeks at home for New Year's — hit 0.800, the highest of the entire timeline. Every trip back pushes the needle higher than the surrounding months.
Was I surprised?
It was cool that the data made legible something I'd only felt — that moving (really, my environment) changed how I listen. The artists I brought with me, the ones I left behind, what I felt in that graduation month, the brightness of coming home. You can really see it in the numbers.
Something something music is memory compressed?
For next time, I'd really like to learn more about effective interactive visualizations. You have to check out pudding.cool/.