Similar Project 1: “The Emotions of London”
Summary: This project analyzed thousands of historical texts to map emotional language across different neighborhoods in London. It used text-mining tools to identify patterns in how people described places, feelings, and social conditions.
Similarities/ differences
How it is similar:
- Uses text mining
- Analyzes large collections of public text
- Looks for patterns in language and emotion
- Connects digital tools to cultural understanding
How it is different from my project:
- Focuses on historical documents, not modern online conversations
- Studies geography and emotion, not student experiences
- Uses large archival datasets instead of social media text
- Not centered on student life or campus culture
Similar Project 2: “What We Tweet”
Summary: This project analyzed Twitter posts to understand how people discuss political events, social issues, and public reactions. It used word frequencies, sentiment analysis, and topic modeling to interpret online discourse
Similarities/ differences
How it is similar:
- Uses social media text as data
- Analyzes public online conversations
- Uses computational tools to identify themes
- Focuses on digital communication as cultural data
How it is different from my project:
- Focuses on political and national conversations, not student life
- Uses Twitter, not Reddit/Discord/Instagram/Yik Yak
- Aims to understand public opinion, not campus experiences
- Not designed for a university-specific audience