토픽 모델링으로 해결할 수 있는 문제

Imagine thousands of reviews, emails, or tickets. Reading them all is impossible. Topic modeling finds the main themes for you, automatically. 📚

A topic is just a group of words that tend to appear together, like price, refund, and shipping. Together they hint at one theme.

The best part: topic modeling is unsupervised. You never tell it the categories. It discovers themes straight from the raw text.

Tagging documents by hand is slow, costly, and inconsistent. Manual labeling simply does not scale to millions of texts.

One article can be 70% sports and 30% business. Topic modeling treats each document as a mixture of several topics, not just one.

Feed in news stories and the model might surface politics, weather, and finance on its own. You just read the clusters it returns.

Customer feedback, research papers, support logs: anywhere you have lots of text, topic modeling reveals structure you would never spot by hand. 🔍

Search needs you to know the keyword first. Topic modeling instead discovers what the collection is about before you ask anything.

Classification sorts text into fixed boxes you defined. Topic modeling invents the boxes itself, so it adapts to whatever your corpus contains.

You receive a short list of topics, each as its top words, plus how strongly every document belongs to each topic.

The most popular method is LDA, short for Latent Dirichlet Allocation. The next lessons unpack exactly how it works.

What makes topic modeling so useful for large text collections?

Topic modeling scans many documents and surfaces their hidden themes with no labels. Each text is a mixture of topics, and LDA is the go-to method. 🎯