Christopher Flynn

From data to wisdom, let the power of data science guide you.

2 minute read

Check out my original blog post for my thoughts on building the Sentiment Analysis Model.

Check out the live demo

This project allows users to choose from a list of five subreddits, view the sentiment of the top 3 comments from the top 5 hottest posts for the day, and visualize the sentiment through both a bar chart and a trend line over time. It combines machine learning, Firebase, and Streamlit to create a real-time, interactive dashboard. Here’s how I went from a standalone model to a real-time database and an interactive deployment.

From Model to Real-World Data

Once I finished building my sentiment analysis model, I was eager to see how it performed on real-world data. Initially, I planned to use Twitter data, since the Sentiment140 dataset I trained on is Twitter-based. However, due to challenges acquiring Twitter API access, I pivoted to using Reddit data via PRAW: The Python Reddit API Wrapper. Reddit turned out to be a perfect alternative, even if it wasn’t the original training source.

Website Hosting Migration

I quickly realized that my existing setup with GitHub Pages wouldn’t support real-time data storage or updates. That’s when I decided to migrate my website to Firebase Hosting, which supports dynamic data and is widely trusted. The migration was straightforward: I initialized Firebase for my project, updated my DNS settings, and made minor adjustments to my package.json for deployment scripts.

Building the Python App

With Firebase and Reddit access in place, I built a Python application to:

  • Import the trained sentiment model and vectorizer
  • Connect to Reddit and Firebase using secure credentials
  • Clean and process Reddit comment data (with profanity filtering and NLP)
  • Organize and push data to Firebase Realtime Database

Initially, the system only tracked r/technology, but the foundation was there for expansion.

Model Debugging

During testing, I noticed that my model was predicting only negative sentiment, even for clearly positive inputs. After digging into the issue, I realized that my label mapping included a Neutral class that didn’t actually exist in the dataset. I corrected the mapping to 0 = Negative, 1 = Positive, retrained the model, and resolved the issue. The model began correctly predicting both sentiment classes.

Discovering Streamlit

I originally tried to embed real-time visuals directly into my Jekyll-based website. Unfortunately, mixing a markdown static site with real-time modular JavaScript proved frustrating. I reached my wit’s end and nearly gave up until I wondered, “Is there a tool that lets me easily host interactive visualizations that I can link to from my site?” That’s when I discovered Streamlit, which lets you turn Python scripts into shareable web apps in minutes. It was exactly what I needed.

With renewed passion, I built the app quickly using my knowledge of pandas and visualization tools. Within hours, I had a working, interactive app pulling data from Firebase and displaying it beautifully. It also let me demonstrate visualization skills in the absence of Tableau or Power BI experience.

Reflecting on the Journey

This project pushed me to:

  • Migrate my site from GitHub Pages to Firebase
  • Work with real-time databases (Firebase Realtime Database)
  • Build a data collection app in Python
  • Learn and deploy with Streamlit
  • Think creatively and problem-solve under pressure

There were moments I considered stopping, thinking maybe what I had was “enough.” But my passion for data science pushed me forward. I’m proud of what I’ve built and excited to tackle more full-stack projects that let me explore data and share insights with others.

Stay tuned for more projects!

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