Topic Modeling (LDA – Latent Dirichlet Allocation)

Introduction

Topic modeling is an unsupervised machine learning technique that identifies the underlying themes (topics) in a large collection of text documents. One of the most popular techniques for topic modeling is Latent Dirichlet Allocation (LDA), a probabilistic model that assumes documents are composed of multiple topics, each represented as a distribution of words.

LDA helps in:

Discovering hidden topics in a large corpus.
Summarizing massive text datasets.
Improving text classification by extracting key themes.
Enhancing information retrieval by clustering similar documents.

Understanding Latent Dirichlet Allocation (LDA)

LDA is based on the idea that each document in a dataset is a mixture of different topics, and each topic is a mixture of words. It assigns words to topics in a probabilistic manner.

Mathematical Foundation

LDA is built on a generative probabilistic model:

Each document is represented as a distribution over a set of topics.
Each topic is a distribution over a vocabulary of words.
Dirichlet distribution is used to ensure topic distribution remains sparse.

LDA works under the Bag of Words (BoW) assumption, meaning it does not consider word order—just word frequency.

Step-by-Step Working of LDA

LDA follows a generative approach, meaning it assumes the existence of topics and assigns words to topics in a probabilistic manner.

Step 1: Preprocessing the Text Data

Before applying LDA, text needs to be cleaned and transformed:

Tokenization – Splitting text into words.
Lowercasing – Converting all words to lowercase.
Stopword Removal – Removing common words like “is”, “the”, “and”.
Lemmatization – Converting words to their root forms (e.g., “running” → “run”).
TF-IDF or Count Vectorization – Converting text into numerical format.

Step 2: Define Hyperparameters

LDA requires setting two key hyperparameters:

α (Alpha) – Controls document-topic distribution.
β (Beta) – Controls topic-word distribution.

A higher α means documents have more topics, while a higher β means topics contain more diverse words.

Step 3: Random Initialization

LDA starts by randomly assigning words in each document to different topics. Initially, these assignments are arbitrary.

Step 4: Iterative Topic Assignment (Gibbs Sampling)

LDA refines topic assignments by iterating through each word in the document and reassigning it based on:

How frequently the word appears in the document’s current topic.
How frequently the word appears in the topic across all documents.

This is done using Gibbs Sampling, a Markov Chain Monte Carlo (MCMC) algorithm.

Step 5: Convergence and Topic Extraction

After multiple iterations, the model stabilizes, and the final topic-word and document-topic distributions are determined.

Evaluating LDA Model

Once the LDA model is trained, its performance can be assessed using:

Perplexity Score – Lower perplexity indicates a better model.
Coherence Score – Measures how semantically similar the words in a topic are.
Manual Inspection – Checking if the extracted topics make sense.

Practical Implementation of LDA in Python

Here’s how you can implement LDA using Python and Gensim:

import gensim
from gensim import corpora
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords

# Sample documents
documents = [
    "Artificial Intelligence is the future of technology.",
    "Machine Learning is a subset of AI.",
    "Deep Learning techniques power modern AI applications.",
    "Data Science and AI are closely related fields."
]

# Preprocessing
stop_words = set(stopwords.words('english'))
processed_docs = [[word.lower() for word in word_tokenize(doc) if word.isalpha() and word.lower() not in stop_words] for doc in documents]

# Create dictionary and corpus
dictionary = corpora.Dictionary(processed_docs)
corpus = [dictionary.doc2bow(doc) for doc in processed_docs]

# Train LDA model
lda_model = gensim.models.LdaModel(corpus, num_topics=2, id2word=dictionary, passes=15)

# Display topics
for idx, topic in lda_model.print_topics():
    print(f"Topic {idx}: {topic}")

Advantages of LDA

✔️ Unsupervised Learning – No labeled data is required.
✔️ Scalable – Works well with large datasets.
✔️ Interpretable Topics – Helps in document clustering and summarization.

Limitations of LDA

❌ Bag of Words Assumption – Ignores word order.
❌ Fixed Number of Topics – The number of topics must be predefined.
❌ Sensitive to Hyperparameters – Poor tuning can lead to meaningless topics.

Applications of LDA

News Categorization – Grouping articles by topics.
Recommender Systems – Suggesting content based on topics.
Social Media Analysis – Identifying trends and user interests.
Healthcare & Research – Analyzing medical literature for disease classification.

Topic Modeling (LDA)