We created this project for the class DIGIT 210: Large Scale Text Analysis at Penn
State Behrend during the Spring 2023 semester. The project was initiated with the goal
of analyzing Stardew Valley's characters and themes through their word choice, though this goal is not what we
ended up focusing on in the end.
Graydon Kupfer
Graydon Kupfer is a junior at Penn State Behrend. He is majoring in DIGIT and minoring in
Game Development. He is currently a Schreyer Honors student. He enjoys playing video
games and watching movies. Stardew Valley is his favorite game, so working on this
project was a fun opportunity.
Contributions:
Found the files within the game that contained dialogue and converted them into a
readable format.
Established most of the project's research questions and goals.
Made major contributions to the project website.
Created txt files for each character in the game and sorted each one's dialogue into
their respective files.
Helped clean up the XML files.
Autotagged the named entities in the XML files for each in-game location.
Created the Cytoscape network showing which characters appear in which
locations.
Yuying Jin
Yuying Jin is a senior at Penn State Behrend. She is majoring in DIGIT and Computer
Science.
Contributions:
Converted YAML files to CSV files and cleaned CSV files.
Generated wordcloud for main and major characters.
Output the sentiment analysis with normal distribution chart.
Implemented emotion detection and visualized the result.
Implemented part-of-speech tagging and listed the verbs and adjs.
Will Stiller
Will Stiller is a senior at Penn State Behrend. He is majoring in DIGIT with a minor in
Data Vis. He is a Lions Ambassador and a captain for an Esports team at Behrend. He has
played Stardew Valley before, but the project has given him a larger appreciation of the
game.
Contributions:
Initiated HTML and CSS code for site design.
Lead the Cleanup of Character, Location, Event, and Misc XML files.
Made the RNC Schema that would give structure to each XML file
Formed Python script to output topic modeling visualizations.
