Topic modelling learning journal (part 3)

Discovering underlying topics: LDA Results

After exploring the global structure with TF-IDF and PCA, I moved on to Bag-of-Words + LDA to extract more explicit, interpretable topics.

I experimented with different numbers of topics and eventually settled on six, balancing granularity and interpretability. To visualise the results, I used a Termite-like topic–term matrix, a design originally proposed by researchers at Stanford.

The resulting matrix is genuinely fascinating. Some themes are immediately recognisable. For example, artificial intelligence clearly emerges as a dominant topic, reflecting its growing prominence across many funding areas rather than being confined to a single discipline.

That said, interpreting LDA outputs is still something I’m actively learning. Unlike PCA plots, which offer a relatively intuitive geometric interpretation, topic–term matrices require subjective judgement and domain knowledge. Deciding where one topic ends and another begins is not always clear-cut.

At this stage, I see this ambiguity not as a weakness, but as part of the learning process. Topic modelling doesn’t hand you answers—it invites you to reason about patterns in text.

Closing Thoughts

This project has fundamentally changed how I think about NLP. I no longer see it as “just text generation,” but as a powerful toolkit for making sense of unstructured text at scale.

Topic modelling, in particular, feels like a natural extension of the unsupervised learning techniques I was already familiar with—just applied to a different and increasingly important kind of data. While I’m still very much a beginner, this journey has already reshaped how I approach text, research trends, and even my own career exploration.

In future posts, I plan to dive deeper into topic interpretation, compare classical and embedding-based approaches, and reflect on where NLP fits into my broader research toolkit.