Introducing your companion for mental well-being! Gain a deeper understanding of your mood with a two-dimensional model of emotional state assessment. Plus, automatically track your music listening habits for additional insights into your emotional well-being.
Hey everyone 👋
I'm excited to be launching Moodset today! 🚀 Here's how it all started.
I've always been interested in exploring myself but never tracked my mood. At a certain point, I became curious about what had been happening with my mental state over the past years and how it relates to the present. Since I didn't write down the mood, I began searching for some indicators that would reflect this. After reading a bunch of studies, I realized that music reflects our mood well. Unfortunately, I couldn't find any product that could show the mood of the music I've been listening to for many years. So, I decided to create my own.
During development, I had more and more ideas about what should be in this app. And today, I present to you:
- automatic tracking of the mood of the music you listen to;
- the ability to manually record your mood using a two-dimensional mood assessment model and optionally complement it with tags and notes;
- setting reminders to record your mood;
- synchronization of data between all your devices;
- creating playlists with desired moods and genres.
And this is just the beginning. I have big plans for this product. In the near future, I would like to add more data sources (not only mood) and a lot of analytics. I won't talk about long-term plans yet😁
Since it all started as a hobby, and so far, I'm doing the whole product myself, there are limitations that will prevent some of you from trying Moodset out:
- the app is only available for Android;
- Spotify is the only music source option.
But I also have plans to make it available to more users.
This is my first launch on Product Hunt, and I appreciate your support, questions, and feedback!
Hi. Don't you think it's subjective if I share my mood? It's not like it's 100% reflective of my emotional state right now and the person likes to embellish.
@ititov_agency Thanks for your feedback.
I wrote about this in one of the answers and will repeat it here again.
From what I've seen in various studies and my subjective opinion, I would say the accuracy is around 80% - 90%. I also want to note that there are genres that have lower accuracy, for example, classical music.
It's also worth noting that it depends on your listening behavior as a listener. For example, whether you listen to the same playlist every day or approach music more selectively.
It's important to understand that this is not telepathy. The app cannot directly read your mood (at least not yet 😁).
The idea is based on the fact that people choose music based on their mood, and as a result, the music reflects their mood. Conversely, music can also influence your mood. This has all been confirmed by research.
As a result, you can see the average mood of your preferred music on a selected day or month. You can also see the trend when it goes up or down, thus providing you with additional information about your mood.
@ititov_agency Before starting development, I researched the market and didn't find any product that allowed viewing the mood history of listened music beyond the last 50 tracks. And I'm not even talking about combining it with mood notes.
Spotify uses mood data for more accurate recommendations. However, they do not provide users with the ability to view this data.
As for whether such data is used on YouTube, I can't say. But they also don't provide users with the capabilities that Moodset offers.
@ititov_agency Rock falls within the 80% - 90% accuracy range I mentioned. Generally, the arousal aspect is always accurately reflected. However, the valence is not as precise. And I have a plan to improve this metric.
I would appreciate your feedback once you have a slightly longer experience using it!
@shushant_lakhyani thank you for your question!
I would say it helps you be more productive but in a non-obvious way.
I created tags for myself indicating different levels of productivity, as well as other tags indicating mood influences.
As a result, thanks to the graphs, I can quickly identify days when my mood was not very good, and most often on such days, my productivity is low. This indicates a correlation between productivity and mood. And thanks to the tags and notes, I can understand which factors worsened my mood, thus eliminating them in the future.
By the way, I want to automate the last part with identifying mood-influencing factors and add it as analytics in the future.
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