Topic Modeling Guide | NLP | Nikhil Learn Hub

Finding "The Gist"

Topic Modeling is an unsupervised learning technique to discover the "hidden topics" (clusters of words) that occur in a large collection of documents without needing manual labels.

Topic 1: Tech

"software, computer, AI, data, server"

Topic 2: Gov

"election, policy, senate, law, voter"

Topic 3: Sports

"team, player, goal, stadium, match"

Level 1 — Late Dirichlet Allocation (LDA)

LDA assumes each document is a "bag of words" and represents a mixture of multiple topics. It is the gold standard for traditional topic modeling.

Python: Scikit-Learn LDA

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

documents = [
    "Machine learning is transforming data science.",
    "Politics and elections are happening this Tuesday.",
    "The football team won the championship match.",
    "Deep learning models require massive GPU data.",
    "The senate voted on the new environmental law."
]

vectorizer = CountVectorizer(stop_words='english')
X = vectorizer.fit_transform(documents)

lda = LatentDirichletAllocation(n_components=3, random_state=42)
lda.fit(X)

words = vectorizer.get_feature_names_out()
for topic_idx, topic in enumerate(lda.components_):
    print(f"Topic #{topic_idx+1}:", [words[i] for i in topic.argsort()[:-6:-1]])

Level 2 — Non-Negative Matrix Factorization (NMF)

NMF is similar to LDA but often produces more coherent and human-readable topics by decomposing the term-document matrix into two non-negative matrices.

When to use NMF?

NMF is excellent when you have smaller datasets or when topics need to be very distinct and non-overlapping compared to the fuzzy mixtures in LDA.

Level 3 — BERTopic (The Neural Standard)

BERTopic uses BERT embeddings and UMAP/HDBSCAN clustering to create topics that understand context and nuance that bag-of-words models (LDA/NMF) miss.

Python: BERTopic Workflow

from bertopic import BERTopic

# BERTopic makes the process incredibly simple!
model = BERTopic()
topics, probs = model.fit_transform(documents)

# Get details about the topics
model.get_topic_info()

# Visualize the cluster
model.visualize_topics()