Natural Language Processing
NLP Text Analysis and Topic Modeling
A coursework-style NLP notebook project combining GloVe embeddings, word similarity analysis, representation bias discussion, spaCy preprocessing, and LDA topic modeling. I completed this notebook work myself as formal ML/NLP practice.
Period
May 2026
Tools / Tech
PythonJupyter NotebookScikit-learnspaCyGensimGloVeLDA
Why I built it
I wanted to connect classic NLP ideas with hands-on notebooks: vector representations, preprocessing choices, topic-word distributions, and document-level interpretation.
Links
What it includes
- Explores GloVe embeddings and word similarity.
- Uses spaCy preprocessing before topic modeling.
- Builds and interprets LDA topic models through word-topic and document-topic views.
What I worked on
- Prepared notebook workflows for embeddings, similarity checks, preprocessing, and topic modeling myself.
- Discussed representation bias and limitations of embedding-based analysis.
- Interpreted topic model outputs rather than only reporting model artifacts.
What I Learned
- Learned how preprocessing decisions affect downstream NLP interpretation.
- Practiced connecting mathematical representations to readable explanations.
- Built a more cautious view of embedding similarity and topic labels.