Topic modeling is a statistical way of finding out which clusters of subjects an unlabelled pile of text is made of. Nobody hands the method a list of topics up front. It looks at which words tend to appear together in the same documents and derives candidate topics from those co-occurrences.
The classic approach is LDA. It treats each document as a mixture of a few topics and each topic as a probability distribution over words. What comes out is a numbered cluster, say topic 3, plus the words weighted most heavily inside it. Seeing "shipping, delivery, delayed, courier" and calling that logistics complaints is still a human step. Newer approaches embed the documents and cluster them in vector space, which makes it easier to pull synonyms into the same topic.
It earns its place on text piles too large to read. Support tickets, app store reviews, open-ended survey answers, social posts and big content archives all qualify.
A concrete case: running topic modeling over 60,000 support tickets shows a quarter of them landing under billing and payment. That points at missing help centre content and at a product step worth fixing.
Results are not deterministic. The number of topics is chosen by hand, so the same data under different settings can produce different clusters. Output gets checked against sample documents before it reaches a report.
