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Welcome to the WoW Sentiment Analysis repository! I am Isaac, I have been a long time addict of WoW and I am just getting into developing my own machine learning models with data from API's. I have long been wanting to get into sentiment analysis and I figured it would be a good idea to focus on something I am oh too knowledgeable about, Thanks!

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customer-satisfaction-analysis data-science gaming machine-learning predictive-analytics predictive-modeling sentiment-analysis wow

wow_customer_satisfaction's Introduction

World of Warcraft Sentiment Analysis and Customer Satisfaction

Overview

This project aims to analyze the sentiment of the World of Warcraft community regarding various aspects of the game, such as updates, expansions, gameplay features, and more. Utilizing data from Twitter, Reddit, and Blizzard's API, we will employ Machine Learning techniques to gauge community sentiment, identify trends, and uncover insights that could inform both players and developers alike.

Objectives

  • To collect and analyze social media data related to World of Warcraft.
  • To implement sentiment analysis using Machine Learning models to understand community sentiment.
  • To identify key factors that contribute to positive or negative sentiment within the WoW community.
  • To visualize sentiment trends over time in relation to game updates and community events.
  • to wrap finalized code and models into an interactive environment using front-end development

Data Sources

  • Twitter: Tweets mentioning World of Warcraft, using specific hashtags and keywords.
  • Reddit: Posts and comments from the WoW subreddit and other relevant forums.
  • Blizzard API: In-game data that might correlate with community sentiment.

Tools and Technologies

  • Python: For data collection, processing, and Machine Learning.
  • Jupyter Notebook: For interactive data exploration and analysis.
  • Spyder For writing large modules and repeatable functions and code.
  • Pandas & NumPy: For data manipulation and numerical computation.
  • Matplotlib & Seaborn: For data visualization.
  • Scikit-learn & TensorFlow: For implementing Machine Learning models.
  • PRAW (Python Reddit API Wrapper): To access Reddit data.
  • Tweepy: To access Twitter data.
  • JavaScript for UI design

Project Structure

  • data/: Directory for storing raw and processed data.
  • notebooks/: Jupyter notebooks for exploration and analysis.
  • scripts/: Python scripts for data collection and processing.
  • models/: Trained Machine Learning models and scripts.
  • reports/: Analysis reports and insights.

How to Contribute

Contributions are welcome! If you're interested in contributing, please contact me on discord isaacdechant and read the CONTRIBUTING.md for more information on how to get started.

License

This project is not currently licensed.

Acknowledgments

  • Thanks to the World of Warcraft community for the wealth of data and discussion.
  • This project is not affiliated with Blizzard Entertainment but seeks to contribute positively to the community's understanding and enjoyment of the game. We use only publicly accesible data.

Contact

For any queries or discussions, please open an issue on this repository or contact me at [email protected].


Note: This project is in the initial stages of development. The above details are subject to change as the project progresses.

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