📊 Dropbox User Sentiment Analysis Dashboard

This project demonstrates how to set up a Dropbox User Sentiment Analysis Dashboard using Airbyte for data extraction, Motherduck (DuckDB) for storage and querying, and Streamlit for visualization. 🚀

🌟 Overview of the Project

The goal is to analyze user reviews of the Dropbox app using sentiment analysis techniques. Here's the workflow breakdown:

1. Dataset Source: A CSV dataset of Dropbox app user reviews (from Kaggle).
2. Preprocessing: Data uploaded to Google Sheets for formatting.
3. Airbyte Integration: Google Sheets (source) connected to Motherduck (destination) via Airbyte.
4. Destination Setup: Motherduck stores data in DuckDB.
5. Sentiment Analysis: Python and Streamlit dashboard for data visualization.

🔗 Live Demo: Streamlit Dashboard

🔗 Blog Post: Detailed Guide on Sentiment Analysis

---

🛠️ Tech Stack

• Airbyte: Data Integration
• Motherduck (DuckDB): Database Management
• Streamlit: Data Visualization
• Python: Backend and Sentiment Analysis
• TextBlob: Sentiment Analysis Library
• Plotly: Data Visualization Library

---

📁 Folder Structure Overview

DROPBOX-REVIEWS-ANALYSIS
├── .devcontainer
│   ├── devcontainer.json
├── .streamlit
│   ├── config.toml
├── assets
│   ├── main.png
├── dropbox-reviews-analytics
│   ├── src
│   │   ├── config
│   │   │   ├── __init__.py
│   │   │   ├── config.py
│   │   ├── utils
│   │   │   ├── __init__.py
│   │   │   ├── database.py
│   │   ├── app.py
├── .env
├── venv
├── .gitignore
├── LICENSE.md
├── README.md
├── requirements.txt

👉 Key Directories Explained:

• .streamlit/config.toml: UI customization for Streamlit.
• src/config/config.py: Handles environment variables.
• src/utils/database.py: Database queries with Motherduck.
• src/app.py: Core Streamlit app logic.
• .env: Stores secure environment variables.

---

🚀 Setup and Installation

1️⃣ Clone the Repository

git clone https://github.com/abhirajadhikary06/Dropbox-Sentiment-Analysis.git
cd Dropbox-Sentiment-Analysis

2️⃣ Create and Activate Virtual Environment

python -m venv venv
source venv/bin/activate  # On macOS/Linux
venv\Scripts\activate    # On Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Setup Environment Variables

Create a .env file in the root directory and add:

MOTHERDUCK_TOKEN=your_motherduck_api_key

5️⃣ Run the Streamlit App

streamlit run src/app.py

The app will be available at http://localhost:8501.

---

🧠 Core Components

📊 Sentiment Analysis Logic

Using TextBlob, we calculate sentiment polarity and subjectivity.

from textblob import TextBlob

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

🦆 Motherduck Database Integration

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 dropbox_reviews WHERE content IS NOT NULL
    """
    return conn.execute(query).fetch_df()

📈 Visualization Example

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

---

⚠️ Deployment Notes

• Avoid specifying exact library versions in requirements.txt.
• Ensure .env is correctly configured.
• Validate database connection tokens during runtime.

Deployment Steps:

1. Load .env variables.
2. Connect securely to Motherduck.
3. Serve the dashboard via Streamlit.

---

🎯 What’s Next?

• Improve dashboard interactivity.
• Add real-time review updates.
• Expand to analyze multiple datasets.

🔗 Complete Project on GitHub: GitHub Repository

🔗 Live Demo: Streamlit Dashboard

🔗 Motherduck Instance:

-- Run this snippet to attach database
ATTACH 'md:_share/abhiraj_db/275eb3cc-2d8b-4705-a787-39c8010e8b2f';

---

📜 License

This project is licensed under the Creative Commons Zero v1.0 Universal.