# Algorithm Listens

## Overview

Algorithm Listens is an ongoing computational text analysis series that explores artists through their complete bodies of work. Each essay combines lyrical analysis, sentiment mapping, and thematic clustering to provide deep insights into an artist's creative evolution and thematic patterns.

## Project Goals

- Analyze **100 artists** across **10 genres**
- Provide data-driven insights into lyrical themes and patterns
- Map sentiment and emotional trajectories across discographies
- Identify thematic clusters and recurring motifs
- Understand artistic evolution over time

## Methodology

![Algorithm Listens screenshot 1](/projects/images/algolistens-1.webp)

![Algorithm Listens screenshot 2](/projects/images/algolistens-2.webp)

### Data Collection

- Complete lyrical datasets for each artist
- Metadata including release dates, album information, and collaborations

### Analysis Techniques

- **Natural Language Processing (NLP)** for text analysis
- **Sentiment Analysis** to track emotional tone
- **Topic Modeling** to identify recurring themes
- **Cluster Analysis** to group similar songs and albums
- **Time Series Analysis** to track evolution

### Visualization

- Interactive charts and graphs
- Sentiment heatmaps
- Thematic networks
- Word clouds and frequency distributions

## Technology Stack

- **Python** for data processing and analysis
- **Pandas** for data manipulation
- **NLTK/spaCy** for natural language processing
- **Scikit-learn** for machine learning and clustering
- **Matplotlib/Plotly** for visualizations

## Artists Covered

This project aims to cover 10 artists from each of the following genres:

- Pop
- Rock
- Hip-Hop/Rap
- R&B/Soul
- Electronic
- Country
- Jazz
- Metal
- Indie/Alternative
- Folk

![Algorithm Listens sentiment heatmap](/projects/images/algolistens-3.webp)

![Thematic clusters network visualization](/projects/images/algolistens-4.webp)

## Key Insights

Each artist analysis provides:

- **Lyrical vocabulary complexity** over time
- **Dominant themes** and how they evolve
- **Sentiment trends** across albums
- **Collaboration patterns** and their influence
- **Genre-specific characteristics** and innovations

## Future Development

- Expand to more artists and genres
- Add audio feature analysis
- Integrate streaming data for popularity correlation
- Create interactive web visualizations
- Build recommendation systems based on thematic similarity

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_This project represents a deep dive into the intersection of data science, music, and literary analysis._