MozMusic Mood Analytics
Inspiration
Music has a profound effect on human emotions, influencing mood, energy levels, and mental well-being. We wanted to explore how different musical attributes—such as tempo, energy, and genre—correlate with mood patterns. Inspired by data-driven music analysis, we leveraged the MusicMoz dataset and HPCC Systems Cloud Cluster to process and analyze music data at scale.
What It Does
MozMusic Mood Analytics analyzes and visualizes the relationship between music and mood. Using HPCC Cloud Cluster, we classify songs into mood categories (Energetic, Calm, Excited, Melancholic, and Neutral) based on their genre. Applied a custom query in HPCC's ECL language to classify songs according to their genre, which was then used to derive mood insights. The processed data is stored in MongoDB for future retrieval.
How It's Built It
HPCC Cloud Cluster: We used HPCC Systems' Cloud Cluster to process and classify the MusicMoz dataset based on its genres using custom ECL queries. Mood Classification: A custom ECL query was created to classify the songs based on their genre into mood categories like Energetic, Calm, Excited, Melancholic, and Neutral. This classification allowed for deeper insight into mood trends. Data Conversion: The processed data was converted into JSON format and stored in MongoDB for easy retrieval and querying. Backend: A Node.js backend was built to serve API endpoints that allow for data retrieval from MongoDB. FUTURE -> Frontend: The data is visualized on the frontend using React.js, Chart.js, and D3.js, allowing users to interact with mood-music correlations.
Challenges Ran Into
ata Processing Complexity: Handling a large dataset in HPCC Systems required optimizing ECL queries for efficient classification. Custom functions were created to assign moods based on genre, which helped improve processing time and accuracy. Data Format Issues: Converting data from CSV to JSON for use with MongoDB while ensuring that the schema aligned properly was tricky, requiring proper parsing and structuring. Integration of HPCC with MongoDB: Ensuring that the data processed in HPCC could be smoothly imported and queried in MongoDB posed integration challenges.
Accomplishments
Successfully processed and analyzed a large-scale music dataset using HPCC Cloud Cluster. Designed and implemented a query that allows users to explore the relationships between mood and music. Built an efficient data pipeline from HPCC → MongoDB, ensuring fast data processing and visualization. Created a custom ECL query for mood classification based on music genre, improving our ability to identify how different genres influence mood. Integrated MongoDB seamlessly with the backend, providing real-time data access and querying capabilities.
What Was Learned
Advanced HPCC Systems & ECL queries for large-scale data processing. Optimizing MongoDB for music analysis and ensuring efficient data storage. Best practices in integrating Node.js with MongoDB for real-time data access. Gained experience in the end-to-end process of extracting, processing, and analyzing music data.
What's Next for MozMusic Mood Analytics?
✅ Enhance Mood Classification Algorithm using musical features. ✅ User-Driven Analysis: Allow users to base mood predictions on their playlist and view visualization. ✅ Compare Mood Trends Over Time to understand how music emotions have evolved over decades. ✅ Genre-Specific Mood Analytics: Analyze how different genres contribute to mood variations. ✅ Expand Dataset by incorporating data from Spotify API for real-world music analysis.
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