Jester-Head / Azeroth-Talks

This project involves scraping the World of Warcraft forums to gather data on player satisfaction with Classic World of Warcraft .

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Azeroth Talks

Project Overview

The project aims to uncover valuable insights into the attitudes, opinions, and behaviors of the WoW gaming community.

Related Project

This project is related to Epic-Parse. While Azeroth Talks focuses on player satisfaction in Classic World of Warcraft, the linked project concentrates on class balancing in the standard version of World of Warcraft.

How to Contribute

Help with Data Annotation

I am currently preparing to annotate a large dataset, and your assistance would be extremely valuable. At this stage, here's how you can start getting involved:

  1. Access the Dataset: The dataset is hosted on Kaggle. You can view and download it here.

  2. Upcoming Annotation Process: The official guidelines for annotating this dataset are being developed. I will be using Doccano. More details on this will be provided soon.

  3. Express Your Interest: If you're interested in contributing to the annotation process, please contact me directly. We will discuss the details of the setup and how you can be involved.

  4. Stay Updated: As the annotation guidelines and setup are finalized, I will update contributors directly. Your patience and interest are greatly appreciated during this setup phase.

  5. Questions and Collaboration: If you have any questions or wish to discuss potential collaboration, please don't hesitate to contact me.

Contact

For more information, expressing interest in contributing, or any questions, please reach out to me at jesterhead0114@gmail.com

This a significant learning journey for me in the field of data science and NLP. As I am still expanding my skills and knowledge, your feedback and suggestions on this project are highly valued.

About

This project involves scraping the World of Warcraft forums to gather data on player satisfaction with Classic World of Warcraft .


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Language:Jupyter Notebook 98.2%Language:Python 1.8%