Reddit Sentiment Analysis

Abstract

1. Introduction

pip install -r requirements.txt python3 reddit-sentiment-analysis.py

This program goes through reddit, finds the most mentioned tickers and uses Vader SentimentIntensityAnalyzer to calculate the ticker compound value.

subs = [] sub-reddit to search post_flairs = {} posts flairs to search || None flair is automatically considered goodAuth = {} authors whom comments are allowed more than once uniqueCmt = True allow one comment per author per symbol ignoreAuthP = {} authors to ignore for posts ignoreAuthC = {} authors to ignore for comment upvoteRatio = float upvote ratio for post to be considered, 0.70 = 70% ups = int define # of upvotes, post is considered if upvotes exceed this # limit = int define the limit, comments 'replace more' limit upvotes = int define # of upvotes, comment is considered if upvotes exceed this # picks = int define # of picks here, prints as "Top ## picks are:" picks_ayz = int define # of picks for sentiment analysis

2. Objective

This program goes thru reddit, finds the most mentioned tickers and uses Vader SentimentIntensityAnalyzer to calculate the ticker compound value.

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/asad70/reddit-sentiment-analysis.git
cd reddit-sentiment-analysis

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by asad70 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.