🎵 Analysis of Popular Songs on Spotify (2023)

This project aims to analyze the Most Streamed Spotify Songs 2023 dataset to understand the relationship between musical characteristics and the popularity of songs on Spotify. The analysis explores patterns, correlations, and trends over time.

📊 Project Details

• Objective: Investigate the factors influencing song popularity on Spotify by analyzing characteristics such as energy, valence, playlist presence, sentiment, and artist popularity.
• Context: Public datasets containing information about the most popular Spotify songs in 2023 were used, along with historical data from 2017-2021. Song lyrics were also extracted for sentiment analysis and linguistic pattern identification.
• Main Techniques Used:
  • Exploratory Data Analysis (EDA)
  • Visualizations (Scatter plots, histograms, word clouds, etc.)
  • Sentiment Analysis with VADER
  • Spearman Correlation
  • Linear Regression (Ordinary Least Squares - OLS)
  • Principal Component Analysis (PCA)
  • Conditional Probability

🛠️ Repository Structure

├── analise_dados_spotify      # Jupyter Notebook with analyses
├── relatorio.pdf        # Report containing all conducted analyses
├── README.md       # Project documentation

🚀 Results & Insights

• Popularity and musical characteristics: No strong correlations were found between popularity and characteristics such as BPM, valence, or energy.
• Playlist influence: Songs featured in popular playlists on Spotify, Deezer, and Apple Music showed significant correlation with the number of streams.
• Recent releases dominate: Songs released in 2022 were the most popular in 2023, with a decline in popularity for previous years.
• Lyrics analysis: Words like "love," "know," and "like" were the most common in the most played songs.
• Song sentiment: Most popular songs had neutral or slightly positive sentiments.
• Release patterns: Many songs were released on the 1st of each month and in January, suggesting marketing strategies.

📁 Data

• Data Sources:

  • Most Streamed Spotify Songs 2023
  • Spotify Daily Top 200 (2017-2021)
  • Lyrics extraction and translation using Lyrics.ovh API and Gemini - Pro AI.

• Data Format:

  • CSV files containing information such as song name, artist, release date, number of streams, audio features (danceability, energy, valence, etc.), and playlist presence.

🤷‍♂️ How to Use

1. Clone this repository:

   git clone https://github.com/GabrielIamato/spotify_musics_analysis.git

2. Install the required libraries:

   pip install pandas numpy matplotlib seaborn wordcloud scipy statsmodels scikit-learn nltk vaderSentiment

3. Run the main script:

   python main.py

📈 Technologies Used

• Language: Python
• Libraries:
  • pandas, numpy – Data manipulation
  • matplotlib, seaborn, wordcloud – Visualization
  • scipy, statsmodels – Statistical analysis
  • sklearn – PCA and modeling
  • nltk, vaderSentiment – Natural language processing

🤝 Contributions

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a branch for your feature:

   git checkout -b my-feature

3. Submit a Pull Request.

📄 License

This project is licensed under the GNU General Public License v3.0, published by the Free Software Foundation.