Latent Dirichlet Allocation (LDA)

In the previous lesson, you learned what Topic Modeling is and why it is important for discovering hidden themes in large text datasets.

Now we will study the most popular and widely used topic modeling algorithm: Latent Dirichlet Allocation (LDA).

LDA is the backbone of many real-world NLP systems, from research analysis to news categorization and customer feedback mining.

What Is Latent Dirichlet Allocation?

Latent Dirichlet Allocation (LDA) is a probabilistic topic modeling algorithm.

It assumes that:

• Each document is a mixture of topics
• Each topic is a mixture of words

The word latent means hidden — topics are not directly visible but inferred from data.

The Core Intuition (Very Important)

LDA tries to reverse how documents are written.

Imagine this hidden process:

1. Choose topics for a document
2. For each word:
   • Select a topic
   • Select a word from that topic

LDA works backward to discover:

• Which topics exist
• Which words belong to each topic
• Which topics appear in each document

Why LDA Works Well

LDA is powerful because:

• Documents are not forced into a single topic
• Topics are represented probabilistically
• It scales well for large datasets

This makes it realistic and flexible.

Important Terminology in LDA

To fully understand LDA, you must know these terms:

• Document: A single text item
• Corpus: Collection of documents
• Topic: Probability distribution over words
• Document-topic distribution: Topic mixture per document
• Topic-word distribution: Word mixture per topic

The Role of Dirichlet Distribution

LDA uses a mathematical distribution called Dirichlet Distribution.

It controls how:

• Topics are distributed within documents
• Words are distributed within topics

You do NOT need deep math now, but conceptually remember:

• Dirichlet controls diversity vs concentration

Key Hyperparameters in LDA

LDA has two important parameters:

• Alpha (α): Topic distribution per document
• Beta (β): Word distribution per topic

Interpretation:

• Low α → documents focus on fewer topics
• High α → documents mix many topics
• Low β → topics use fewer words
• High β → topics use many words

Typical LDA Workflow

1. Text cleaning
2. Tokenization
3. Stopword removal
4. Vectorization (Bag of Words)
5. Apply LDA
6. Interpret topics

You already know steps 1–4.

Practical Example: LDA Topic Modeling

Where to run this code:

• Google Colab (recommended)
• Jupyter Notebook (Anaconda)

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

documents = [
    "I love watching movies and films",
    "This film was boring and slow",
    "The movie had excellent acting",
    "Football matches are exciting",
    "I enjoy watching football games"
]

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

lda = LatentDirichletAllocation(
    n_components=2,
    random_state=42
)
lda.fit(X)

words = vectorizer.get_feature_names_out()

for idx, topic in enumerate(lda.components_):
    print(f"Topic {idx}:")
    print([words[i] for i in topic.argsort()[-5:]])

Understanding the Output

The output shows two discovered topics.

• Each topic is represented by top words
• Words indicate the theme of the topic

You will typically see:

• One topic related to movies
• Another topic related to sports

This happens without giving labels to the model.

How LDA Assigns Topics to Documents

LDA does not say:

• This document belongs to only Topic A

Instead it says:

• Document = 70% Topic A, 30% Topic B

This makes LDA more realistic for real-world text.

Choosing the Number of Topics

There is no perfect number of topics.

Common strategies:

• Domain knowledge
• Trial and error
• Coherence score

Too few topics → vague themes Too many topics → noisy results

Applications of LDA

• Document clustering
• Research paper categorization
• Customer feedback analysis
• News topic discovery
• Content recommendation

Common Mistakes to Avoid

• Skipping preprocessing
• Choosing too many topics
• Expecting perfect topic names

Topic interpretation always requires human judgment.

Assignment / Homework

Theory:

• Explain LDA in your own words
• Difference between LDA and LSA

Practical:

• Collect 30 articles from news websites
• Apply LDA with 3–5 topics
• Interpret each topic manually

Practice Environment:

• Google Colab
• Jupyter Notebook

Practice Questions

Quick Quiz

Quick Recap

• LDA is the most popular topic modeling algorithm
• Documents are mixtures of topics
• Topics are mixtures of words
• Probabilistic and unsupervised

In the next lesson, we will explore Text Similarity and understand how machines compare documents mathematically.