https://github.com/luizabash/reddit-datahoarder

NLP project focused on the subreddit r/datahoarder
https://github.com/luizabash/reddit-datahoarder

eda nlp

Last synced: about 11 hours ago
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NLP project focused on the subreddit r/datahoarder

• Host: GitHub
• URL: https://github.com/luizabash/reddit-datahoarder
• Owner: luizabash
• Created: 2024-09-10T09:10:31.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-12T16:41:44.000Z (4 months ago)
• Last Synced: 2024-11-11T19:03:51.812Z (2 months ago)
• Topics: eda, nlp
• Language: Python
• Homepage:
• Size: 678 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Reddit-Datahoarder

this repository contains a data analysis project focused on the subreddit r/datahoarder

# Reddit Data Analysis - r/datahoarder

This project explores the community discussions and trends in the subreddit [r/datahoarder](https://www.reddit.com/r/datahoarder/). The analysis focuses on understanding what topics and themes are popular in the subreddit by examining word frequencies and visualizing key terms.

## Project Overview

- **Objective**: To analyze Reddit posts from `r/datahoarder` and identify the most common words, trends, and sentiment in the community's discussions.

- **Data Source**: Reddit posts were collected using the PRAW library (Python Reddit API Wrapper).

- **Tools and Libraries Used**: Python, Pandas, Matplotlib, NLTK, Gensim, and Streamlit.

## Project Structure

- **`data/`**: Contains the cleaned dataset used for analysis.

- **`notebooks/`**: Jupyter Notebook with Exploratory Data Analysis (EDA) and visualizations.

- **`images/`**: Visualizations generated during EDA.

- **`streamlit_app.py`**: A Streamlit app to create an interactive dashboard.

## Key Steps

1. **Data Collection**: Collected Reddit posts from `r/datahoarder` using the PRAW API.

2. **Data Preprocessing**: Cleaned the text data by removing URLs, punctuation, stop words, and lemmatizing.

3. **Exploratory Data Analysis (EDA)**: Conducted word frequency analysis and created visualizations like word clouds and bar plots.

4. **Sentiment Analysis** (Optional): Attempted sentiment analysis using VADER, but faced compatibility issues with the current setup.

5. **Results and Visualization**: Visualized the most common words in titles and post bodies, and identified key trends.