Topic Modeling
Discover hidden themes and patterns in document collections
What is Topic Modeling?
Topic modeling is an unsupervised machine learning technique for discovering abstract "topics" that occur in a collection of documents. It automatically identifies patterns of word co-occurrences and groups them into topics, helping us understand the main themes in large text corpora without manual labeling.
Key Insight: Topic models assume documents are mixtures of topics, and topics are probability distributions over words. This allows documents to belong to multiple topics simultaneously.
Interactive Topic Visualization
Explore how topics are composed of words and how documents relate to topics:
Traditional Topic Modeling Methods
Latent Dirichlet Allocation (LDA)
The most popular traditional topic modeling algorithm, using Bayesian inference:
How it works:
- 1. Assumes each document is a mixture of topics
- 2. Each topic is a distribution over words
- 3. Uses Dirichlet priors for distributions
- 4. Inference via variational Bayes or Gibbs sampling
Key parameters:
- • α (alpha): Document-topic density
- • β (beta): Topic-word density
- • K: Number of topics
- • Iterations: Sampling iterations
Strengths: Interpretable, well-studied, handles sparse data well
Limitations: Assumes bag-of-words, ignores word order, requires tuning K
Latent Semantic Analysis (LSA)
Uses SVD to reduce dimensionality and find latent semantic structure:
# LSA process 1. Create term-document matrix (TF-IDF) 2. Apply SVD: A = UΣV^T 3. Truncate to k dimensions 4. Topics = rows of V (document-topic) 5. Terms = rows of U (term-topic)
✓ Fast and deterministic
✗ Topics can have negative values
Non-negative Matrix Factorization (NMF)
Decomposes document-term matrix into non-negative factors:
V ≈ W × HV: documents×terms, W: documents×topics, H: topics×terms
✓ Interpretable parts-based representation
✗ Sensitive to initialization
Modern Neural Topic Models
BERTopic
State-of-the-art topic modeling using transformer embeddings:
Pipeline:
1. Embed
BERT/Sentence-BERT
2. Reduce
UMAP
3. Cluster
HDBSCAN
4. Tokenize
c-TF-IDF
Advantages:
- • Captures semantic meaning
- • Handles short texts well
- • Dynamic topic modeling
- • No need to specify K
Limitations:
- • Computationally expensive
- • Requires pre-trained models
- • Less interpretable parameters
- • Can create many small topics
Top2Vec
Jointly learns word, document, and topic vectors:
- • Uses Doc2Vec for embeddings
- • Finds dense areas in embedding space as topics
- • Automatically determines number of topics
- • Topics represented as centroids in vector space
Neural Variational Topic Models
Combines neural networks with traditional topic modeling:
- • NVDM: Neural Variational Document Model
- • ProdLDA: Product-of-Experts LDA
- • ETM: Embedded Topic Model (uses word embeddings)
Multimodal Topic Modeling
Beyond Text: Multimodal Approaches
Modern topic modeling extends beyond text to handle multiple modalities:
Text + Image Topics
Discovers topics across textual and visual content:
- • mmLDA: Multi-modal LDA
- • CLIP-based: Uses CLIP embeddings
- • Applications: Social media analysis, news
Hierarchical Multimodal
Builds topic hierarchies across modalities:
- • Nested topic structures
- • Cross-modal topic alignment
- • Applications: Scientific literature
Example: Social Media Topic Modeling
Text
"Amazing sunset at the beach"
Image
[Sunset photo]
Topic
Travel/Nature
Comparing Topic Modeling Approaches
| Method | Type | Strengths | Limitations | Best For |
|---|---|---|---|---|
| LDA | Probabilistic | Interpretable, proven | Bag-of-words, fixed K | Long documents |
| BERTopic | Neural | Semantic, flexible K | Computational cost | Short texts, tweets |
| NMF | Matrix | Fast, deterministic | Requires good preprocessing | Well-structured text |
| Top2Vec | Neural | Automatic K, semantic | Black box | Exploratory analysis |
Practical Considerations
Preprocessing Steps
- 1. Text Cleaning:
- • Remove stopwords
- • Lemmatization/stemming
- • Handle special characters
- 2. Feature Engineering:
- • TF-IDF weighting
- • N-gram extraction
- • Minimum document frequency
Evaluation Metrics
- • Coherence Score: Measures topic quality based on word co-occurrence
- • Perplexity: Model's ability to predict held-out documents
- • Topic Diversity: Uniqueness of words across topics
- • Human Evaluation: Manual assessment of topic interpretability
Choosing the Right Method
If you have long, well-structured documents: Start with LDA
If you have short texts (tweets, reviews): Use BERTopic or Top2Vec
If you need deterministic results: Choose NMF or LSA
If you have multimodal data: Consider CLIP-based approaches
If interpretability is crucial: Stick with traditional methods (LDA, NMF)
Real-World Applications
Content Recommendation
Discover user interests and recommend similar content based on topic preferences
Trend Analysis
Track emerging topics in social media, news, or scientific literature over time
Document Organization
Automatically categorize and organize large document collections
Customer Feedback
Analyze reviews and support tickets to identify common themes and issues
Research Discovery
Find related research papers and identify research trends in academic literature
Market Intelligence
Analyze competitor content and industry discussions to identify market trends
Implementation Example
Quick Start with Python
# Traditional LDA with Gensim
from gensim import corpora, models
# Create dictionary and corpus
dictionary = corpora.Dictionary(tokenized_docs)
corpus = [dictionary.doc2bow(doc) for doc in tokenized_docs]
# Train LDA model
lda_model = models.LdaModel(
corpus=corpus,
id2word=dictionary,
num_topics=5,
random_state=42,
passes=10,
alpha='auto',
per_word_topics=True
)
# Modern BERTopic
from bertopic import BERTopic
model = BERTopic(
language="english",
calculate_probabilities=True,
verbose=True
)
topics, probs = model.fit_transform(documents)
# Visualize topics
model.visualize_topics()Both approaches have their place - choose based on your specific requirements for interpretability, performance, and document characteristics.
Next Steps
Explore related NLP concepts: