Sentishelter

HTML, CSS, JavaScript, Python, SpaCy, Matplotlib, Seaborn, GitHub Pages, HuggingFace Spaces, Kaggle

I collaborated with 3 people for this project. As a group, we combined our skills in data science, NLP, web development, and visualization to explore how climate change discussions affect housing sentiment.

Live Website

Chatbot

Github Repo

Inspiration

We created SentiShelter to help people understand how climate change conversations influence the housing market. From rising insurance rates to homes becoming less sustainable, we wanted to provide a tool that visualizes sentiment trends and highlights key topics and locations.

What it does

SentiShelter analyzes Reddit comments from 2010-2022, tracking sentiment over time related to climate change and housing. Key features include:

• Sentiment Analysis: Identify positive, negative, or neutral sentiment in discussions about climate and housing.
• Topic Clusters: Extract major topics and trends from discussions.
• Interactive Website: Users can explore data visualizations and access a chatbot to learn more about the dataset.
• Data Visualization: Bar charts, line graphs, and scatter plots show sentiment trends over time.

Final Product

How we built it

• Data Source: Kaggle Reddit Climate Change Dataset (2010-2022)
• Processing: Python, SpaCy NLP for cleaning and extracting entities like people and locations
• Visualization: Matplotlib and Seaborn for sentiment trends, topic clusters, and frequency charts
• Website: Responsive HTML/CSS/JS site with integrated Hugging Face chatbot

Sample Kaggle Data Science Work

• Cleaning the Dataset

• Average Sentiment Analaysis per month
  

  Key Features

• Explore Reddit sentiment trends (2010-2022)
• Identify top entities (persons, locations) and their associated sentiment
• Interactive visualizations and data exploration
• Hugging Face chatbot integration for learning about climate and housing

Challenges we ran into

• Large Dataset: Required sampling to maintain performance without losing insights
• Entity Analysis: Identifying meaningful relationships between people, locations, and sentiment was complex
• Visualization: Presenting data clearly while maintaining an interactive user experience

Accomplishments that we are proud of

• Successfully combined NLP, data visualization, and web development
• Developed a user-friendly website with clear insights
• Integrated a chatbot interface for exploring climate change and housing discussions

What we learned

• How to extract insights from large datasets using NLP
• How to visualize and present complex sentiment data interactively
• How to combine Python analysis with a responsive website

Winner of Technica 2023

• My team and I won first place in the Fannie Mae “Climate Change Sentiment Analysis and Impacts on Housing” competition, and second place in the Bloomberg Industry Group “Best AI-Powered Solution” competition.

What is next for SentiShelter

• Add more real-time sentiment tracking as new data becomes available
• Expand chatbot capabilities to provide more detailed explanations of trends and insights
• Enhance topic clustering and sentiment analysis using advanced NLP models

• 2025 3
• 2024 4
• 2023 2
• 2022 1