Data • python • plotly • pandas • LDA • nltk •TEXTBLOB• html • css
This master’s capstone explores how people express quality of life across different geographic regions using tweets pulled from Twitter’s API. The keyword “why” was chosen to guide the query, referencing the World Health Organization’s definition of quality of life as a foundation.
The project blends Python, Plotly, and various NLP tools to perform text analysis, topic modeling (via Latent Dirichlet Allocation), and sentiment analysis (using NLTK and TextBlob). Topic modeling identified recurring themes and key terms, while sentiment analysis helped gauge the emotional tone of the tweets.
The final result is a visual and textual snapshot of how quality of life is perceived—through questions, emotions, and patterns—across digital conversation.
Explore the project:
