Running LDA With Gensim
Fit topics on a real corpus.
Running LDA With Gensim is a free NLP Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Meet Gensim
Gensim is a Python library built for topic modeling. It makes running LDA on real text fast and approachable. 🐍
from gensim import corpora, modelsStart With Tokens
Gensim expects each document as a list of clean tokens. You bring already lowercased, stopword-free word lists.
docs = [["price", "refund"], ["battery", "screen"]]Build a Dictionary
A Gensim Dictionary maps every unique word to an integer id. LDA works on those ids, not the raw strings.
dictionary = corpora.Dictionary(docs)Create the Corpus
Convert each document to a bag-of-words: pairs of word id and count. This list is your corpus. 📦
corpus = [dictionary.doc2bow(d) for d in docs]Train the Model
Now fit LdaModel, passing the corpus, the dictionary, and how many topics you want it to find.
lda = models.LdaModel(corpus, num_topics=2, id2word=dictionary)Pick num_topics Wisely
num_topics is your most important choice. Too few blurs themes together, too many splits them into noise.
More Passes, Better Fit
The passes argument sets how many times LDA reads the corpus. A few extra passes usually sharpens the topics.
lda = models.LdaModel(corpus, num_topics=2, passes=10)Peek at the Topics
Call print_topics to see each topic as its top weighted words. This is your first look at what LDA discovered.
for t in lda.print_topics():
print(t)Score a New Document
Pass a new bag-of-words to the model to get its topic distribution, showing how much each topic applies.
lda[dictionary.doc2bow(["refund", "price"])]Trim the Dictionary
Use filter_extremes to drop ultra-rare and ultra-common words. A cleaner vocabulary gives clearer topics. 🧹
dictionary.filter_extremes(no_below=2, no_above=0.5)Set a Seed
LDA has randomness. Pass random_state so your topics come out the same every run, which makes results reproducible.
lda = models.LdaModel(corpus, num_topics=2, random_state=42)Quick Check
Recall the steps before training an LDA model in Gensim.
Recap
Tokenize, build a Dictionary, make a bag-of-words corpus, then fit LdaModel with your chosen num_topics. Gensim handles the rest. ✅
Frequently asked questions
Is the “Running LDA With Gensim” lesson free?
Yes — the full text of “Running LDA With Gensim” is free to read here on the web, and the NLP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “Running LDA With Gensim”?
Fit topics on a real corpus. You practise NLP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Running LDA With Gensim” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this NLP Academy lesson?
Yes. Every NLP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- What Topic Modeling Solves
- How LDA Groups Words Into Topics
- Running LDA With Gensim
- Interpreting and Labeling Topics