In this blog, we'll dive 🌊🤿 into building a Streamlit-based dashboard for analyzing Spotify User Sentiment using Airbyte for data extraction, Motherduck (DuckDB) for storage and querying, and Daytona for streamlined development environments. This project explores how these technologies integrate with Streamlit to create an interactive and insightful data analysis application.

📁 Folder Structure Overview

SPOTIFY-REVIEWS-ANALYSIS

├── .devcontainer

│   ├── devcontainer.json

├── .streamlit

│   ├── config.toml

├── assets

│   ├── main.png

├── src

│   ├── config

│   │   ├── __init__.py

│   │   ├── config.py

│   ├── utils

│   │   ├── __init__.py

│   │   ├── database.py

│   ├── app.py

├── .env

├── venv

├── .gitignore

├── LICENSE.md

├── README.md

├── requirements.txt

• 
  .devcontainer/devcontainer.json: Configures development environment.


• 
  .streamlit/config.toml: Streamlit's UI style and configuration.


• 
  assets: Stores static assets like images.


• 
  src/config/config.py: Handles environment variables.


• 
  src/utils/database.py: Queries data from Motherduck.


• 
  src/app.py: Streamlit dashboard and logic.


• 
  .env: Stores environment variables securely.

👉 Tips On Folder Structure

• 
  SPOTIFY-REVIEWS-ANALYSIS: This is the outer folder of the repository.


• 
  src: This folder contains config and utils for project logic.

☀️ Daytona Integration

Daytona is an open-source Development Environment Manager (DEM) designed to simplify and streamline the process of setting up development environments.

🛠️ Why Daytona?

• 
  Consistency: Ensures uniform development environments across all team members.


• 
  Scalability: Manages multiple environments seamlessly.


• 
  Security: Isolates and secures environments.


• 
  Efficiency: Reduces overhead during setup and switching between environments.

📚 Daytona Setup

1. 
   Installation: Follow the .
   
   
   
   
   
   

   
   
   
   📊 Core Logic of Sentiment Analysis
   

   
   
   

   Sentiment analysis is powered by TextBlob to determine the polarity (positive/negative sentiment) and subjectivity (factual/opinionated content) of reviews.

   
   
   

   
   
   
   🧠 Sentiment Analysis Function
   

   
   
   
   
   
   
   

   CODE Kopieren

   from textblob import TextBlob
   
   
   
   def get_sentiment(text):
   
       blob = TextBlob(str(text))
   
       return blob.sentiment.polarity if sentiment_type == "Polarity" else blob.sentiment.subjectivity
   
   

   
   
   
   
   
   
   
   

   
   
   
   📈 Visualization Example
   

   
   
   
   
   
   
   

   CODE Kopieren

   fig = px.histogram(reviews_df, x='sentiment', title='Sentiment Distribution')
   
   st.plotly_chart(fig)
   
   

   
   
   
   
   
   
   
   
   
   
   

   
   
   
   🦆 Database Integration with Motherduck
   

   
   
   

   
   
   
   🔗 database.py
   

   
   
   
   
   
   
   

   CODE Kopieren

   import duckdb
   
   from config.config import MOTHERDUCK_TOKEN
   
   
   
   def get_connection():
   
       return duckdb.connect(f"md:?token={MOTHERDUCK_TOKEN}")
   
   
   
   def get_reviews_for_sentiment():
   
       conn = get_connection()
   
       query = """
   
       SELECT content, score FROM spotify_reviews WHERE content IS NOT NULL
   
       """
   
       return conn.execute(query).fetch_df()
   
   

   
   
   
   
   
   
   
   

   This code fetches Spotify review data securely using MOTHERDUCK_TOKEN stored in .env through config.py file.

   
   
   

   
   
   
   🗂️ config.py
   

   
   
   
   
   
   
   

   CODE Kopieren

   import os
   
   from dotenv import load_dotenv
   
   
   
   load_dotenv()
   
   MOTHERDUCK_TOKEN = os.getenv("MOTHERDUCK_TOKEN")
   
   

   
   
   
   
   
   
   
   

   
   
   
   🔄 Connection Between app.py and database.py
   

   
   
   

   The app.py imports get_reviews_for_sentiment from database.py, creating a seamless flow of data into the dashboard.

   
   
   
   
   
   

   
   
   
   🎯 Conclusion
   

   
   
   

   We successfully built a Spotify Reviews Sentiment Analysis Dashboard using Airbyte, Motherduck, Streamlit, and Daytona. This project demonstrates the power of consistent development environments, robust data storage, and insightful visualization.

   
   
   

   .
   
   📺 Live PROJECT https://spotify-sentiment-analysis.streamlit.app

   
   
   

   
   
   
   Sentiment Analysis #Happy Coding! #Daytona 🚀🦆
   

   Vollständiger Original-Bericht

   Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf dev.to.

   ↗ Original-Artikel auf dev.to lesen