Ejecución de LDA con Gensim

Gensim is a Python library built for topic modeling. It makes running LDA on real text fast and approachable. 🐍

Gensim expects each document as a list of clean tokens. You bring already lowercased, stopword-free word lists.

A Gensim Dictionary maps every unique word to an integer id. LDA works on those ids, not the raw strings.

Convert each document to a bag-of-words: pairs of word id and count. This list is your corpus. 📦

Now fit LdaModel, passing the corpus, the dictionary, and how many topics you want it to find.

num_topics is your most important choice. Too few blurs themes together, too many splits them into noise.

The passes argument sets how many times LDA reads the corpus. A few extra passes usually sharpens the topics.

Call print_topics to see each topic as its top weighted words. This is your first look at what LDA discovered.

Pass a new bag-of-words to the model to get its topic distribution, showing how much each topic applies.

Use filter_extremes to drop ultra-rare and ultra-common words. A cleaner vocabulary gives clearer topics. 🧹

LDA has randomness. Pass random_state so your topics come out the same every run, which makes results reproducible.

Recall the steps before training an LDA model in Gensim.

Tokenize, build a Dictionary, make a bag-of-words corpus, then fit LdaModel with your chosen num_topics. Gensim handles the rest. ✅