Analysis of the Major characters in Warcraft, by examining their character pages on wowpedia and analysing the user comments on their wowhead threads.
The end products are an initial YouTube Video for Part A, and the final Website for Part B.
For a detailed explanation of the project, which tools are used etc., refer to the Explainer.ipynb notebook.
Setup environment
python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt
Start by downloading raw and clean wowpedia pages from the api with the following two scripts
python download_character_pages.py
python download_character_pages_clean.py
Create the networkx graph along with a pandas dataframe. The resulting graph and dataframe should already be store in G_wow.gexf and Gcc_wow.gexf, and df_chars.csv in the folder /store/.
python create_wow_graph.py
Download necessary stuff in relation to comments. This takes a long time, if repo is not too old, just use the comments already stored in /data/char_comments/.
python download_character_comments.py
Clean the user comments from wowhead and convert the cleaned character pages from wowhead and wowpedia to list of words.
python comments_clean.py
python pages_to_words.py -f -s wowhead
python pages_to_words.py -f -s wowpedia
Extract quotes from the raw wowpedia pages, used for sentiment analysis.
python extract_character_quotes.py
Perform text analysis computations by referencing the Text Analysis.ipynb notebook. These computation are (and should already be) stored in the json files under /store/wowhead/ and /store/wowpedia/.
Then we can create wordclouds for different graph partitions for both wowpedia and wowhead by
python create_wordclouds.py -s wowpedia
python create_wordclouds.py -s wowhead
We can also compute sentiments for character quotes and wowhead user comments, and perform time-series analysis, which is done in Sentiment Analysis.ipynb.
Every part of the network analysis is done in the Graph Analysis.ipynb notebook.