Topic Modeling

Automatically discovering hidden topics and themes in large collections of documents

🎯 What is Topic Modeling?

Definition: An unsupervised machine learning technique that automatically discovers abstract topics within a collection of documents

Simple Analogy: Imagine you have a huge library of books but no catalog system. Topic modeling is like having a librarian who reads through all the books and automatically creates categories like "Romance," "Science Fiction," "History," etc., based on the words and themes found in each book.

📚 TOPIC MODELING OVERVIEW

Document Collection → Topic Model → Discovered Topics

🎯 THE CHALLENGE
Large document collections are hard to organize and understand
Manually reading thousands of documents is time-consuming
Hidden themes and patterns are not obvious

🔧 THE SOLUTION
Automatically discover topics by analyzing word patterns
Group similar documents together
Identify key themes and concepts

💡 THE IMPACT
Organize large document collections
Understand content themes automatically
Discover hidden patterns in text data
Enable better search and recommendation

🎯 Why Topic Modeling Matters

The Challenge: Large document collections are difficult to organize and understand manually.

The Solution: Automatically discover topics by analyzing statistical patterns in word usage across documents.

The Impact: This enables:

• Content organization and categorization
• Document clustering and similarity analysis
• Trend analysis and theme discovery
• Improved search and recommendation systems
• Knowledge discovery in large text corpora

---

🔍 How Topic Modeling Works

📊 TOPIC MODELING PROCESS

STEP 1: DOCUMENT PREPROCESSING
├── Text cleaning and normalization
├── Stop word removal
├── Tokenization and stemming
└── Create document-term matrix

STEP 2: STATISTICAL ANALYSIS
├── Find word co-occurrence patterns
├── Identify word clusters
├── Calculate topic distributions
└── Optimize topic assignments

STEP 3: TOPIC EXTRACTION
├── Generate topic-word distributions
├── Assign topics to documents
├── Create interpretable topic labels
└── Evaluate topic quality

🎯 CORE ASSUMPTION
Documents are mixtures of topics
Topics are distributions over words
Words that appear together often belong to the same topic

🧩 Core Assumptions

1. Documents are mixtures of topics: Each document contains multiple topics in different proportions
2. Topics are distributions over words: Each topic is characterized by a set of words with different probabilities
3. Co-occurrence patterns reveal topics: Words that frequently appear together likely belong to the same topic

📊 Mathematical Intuition

📈 TOPIC MODELING MATHEMATICS

DOCUMENT = α₁ × TOPIC₁ + α₂ × TOPIC₂ + ... + αₖ × TOPICₖ

Where:
├── α₁, α₂, ..., αₖ are topic proportions (sum to 1)
├── TOPIC₁, TOPIC₂, ..., TOPICₖ are word distributions
└── k is the number of topics

EXAMPLE:
News Article = 0.7 × Politics + 0.2 × Economy + 0.1 × Sports

---

🛠️ Common Topic Modeling Algorithms

🔧 TOPIC MODELING ALGORITHMS

TRADITIONAL METHODS
├── Latent Dirichlet Allocation (LDA)
├── Non-negative Matrix Factorization (NMF)
├── Latent Semantic Analysis (LSA/LSI)
└── Probabilistic Latent Semantic Analysis (PLSA)

MODERN METHODS
├── BERTopic
├── Top2Vec
├── Neural Topic Models
└── Hierarchical Topic Models

📈 EVOLUTION
Matrix Factorization → Probabilistic Models → Neural Models → Transformer-based

1. Latent Dirichlet Allocation (LDA)

Most popular and widely-used topic modeling algorithm

LDA assumes that documents are generated by a probabilistic process where each document is a mixture of topics, and each topic is a distribution over words.

🎯 LDA CONCEPT

GENERATIVE PROCESS:
1. Choose number of topics K
2. For each topic: Draw word distribution from Dirichlet
3. For each document: Draw topic distribution from Dirichlet
4. For each word: Choose topic, then choose word from topic

KEY PARAMETERS:
├── K: Number of topics (must be specified)
├── α: Document-topic concentration
├── β: Topic-word concentration
└── Iterations: Number of training iterations

🎯 CHARACTERISTICS
├── Probabilistic and interpretable
├── Handles document-topic mixtures well
├── Requires specifying number of topics
└── Computationally efficient

🔧 Simple LDA Example

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import numpy as np

# Simple documents about different topics
documents = [
    "The stock market rose today as investors showed confidence in tech companies",
    "New artificial intelligence breakthrough announced by researchers at MIT",
    "The basketball team won their championship game with a record-breaking score",
    "Machine learning algorithms are revolutionizing healthcare diagnostics",
    "The football season starts next month with high expectations for teams",
    "Scientists develop new AI system for predicting weather patterns accurately"
]

# Step 1: Convert text to numbers
print("📝 Converting text to numbers...")
# Output: Converting text to numbers...

vectorizer = CountVectorizer(stop_words='english', max_features=50)
doc_term_matrix = vectorizer.fit_transform(documents)

print(f"✅ Created matrix with {doc_term_matrix.shape[0]} documents and {doc_term_matrix.shape[1]} words")
# Output: Created matrix with 6 documents and 43 words

# Step 2: Train LDA model
print("\n🔧 Training LDA model to find 3 topics...")
# Output: Training LDA model to find 3 topics...

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

print("✅ Model training completed!")
# Output: Model training completed!

# Step 3: Show discovered topics
print("\n📊 Discovered Topics:")
# Output: Discovered Topics:

feature_names = vectorizer.get_feature_names_out()

for topic_idx, topic in enumerate(lda.components_):
    top_words_idx = topic.argsort()[-5:][::-1]  # Get top 5 words
    top_words = [feature_names[i] for i in top_words_idx]
    print(f"Topic {topic_idx + 1}: {', '.join(top_words)}")

# Output:
# Topic 1: learning, machine, ai, artificial, algorithms
# Topic 2: team, game, season, football, basketball
# Topic 3: market, stock, investors, tech, companies

# Step 4: Show which topic each document belongs to
print("\n🏷️ Document Classifications:")
# Output: Document Classifications:

doc_topic_matrix = lda.transform(doc_term_matrix)

for i, doc in enumerate(documents):
    dominant_topic = np.argmax(doc_topic_matrix[i]) + 1
    confidence = np.max(doc_topic_matrix[i])
    print(f"Doc {i+1}: Topic {dominant_topic} (confidence: {confidence:.2f})")
    print(f"  Text: {doc[:50]}...")

# Output:
# Doc 1: Topic 3 (confidence: 0.68)
#   Text: The stock market rose today as investors showed...
# Doc 2: Topic 1 (confidence: 0.68)
#   Text: New artificial intelligence breakthrough announced...
# Doc 3: Topic 2 (confidence: 0.68)
#   Text: The basketball team won their championship game...
# Doc 4: Topic 1 (confidence: 0.68)
#   Text: Machine learning algorithms are revolutionizing...
# Doc 5: Topic 2 (confidence: 0.68)
#   Text: The football season starts next month with high...
# Doc 6: Topic 1 (confidence: 0.68)
#   Text: Scientists develop new AI system for predicting...

# Step 5: Show topic proportions for each document
print("\n� Topic Proportions for Each Document:")
# Output: Topic Proportions for Each Document:

topic_df = pd.DataFrame(
    doc_topic_matrix,
    columns=[f'Topic {i+1}' for i in range(3)],
    index=[f'Doc {i+1}' for i in range(len(documents))]
)

print(topic_df.round(2))

# Output:
#        Topic 1  Topic 2  Topic 3
# Doc 1     0.16     0.16     0.68
# Doc 2     0.68     0.16     0.16
# Doc 3     0.16     0.68     0.16
# Doc 4     0.68     0.16     0.16
# Doc 5     0.16     0.68     0.16
# Doc 6     0.68     0.16     0.16

✅ LDA Pros and Cons

✅ PROS
├── Highly interpretable results
├── Handles document-topic mixtures well
├── Probabilistic foundation
├── Computationally efficient
├── Well-established and widely used
└── Good for exploratory analysis

❌ CONS
├── Requires specifying number of topics
├── Assumes topics are uncorrelated
├── Sensitive to hyperparameters
├── May struggle with short documents
├── Doesn't handle word order/context
└── Topics may not always be coherent

---

2. Non-negative Matrix Factorization (NMF)

Matrix factorization approach to topic modeling

NMF decomposes the document-term matrix into two matrices: one representing documents in topic space and another representing topics in word space.

🎯 NMF CONCEPT

MATRIX FACTORIZATION:
Documents × Words = (Documents × Topics) × (Topics × Words)
      V          =         W          ×         H

Where:
├── V: Original document-term matrix
├── W: Document-topic matrix
├── H: Topic-word matrix
└── All values are non-negative

🎯 CHARACTERISTICS
├── Simpler than LDA
├── Deterministic results
├── Parts-based representation
└── Often produces clearer topics

🔧 Simple NMF Example

from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import numpy as np

# Same documents as before
documents = [
    "The stock market rose today as investors showed confidence in tech companies",
    "New artificial intelligence breakthrough announced by researchers at MIT",
    "The basketball team won their championship game with a record-breaking score",
    "Machine learning algorithms are revolutionizing healthcare diagnostics",
    "The football season starts next month with high expectations for teams",
    "Scientists develop new AI system for predicting weather patterns accurately"
]

# Step 1: Convert text to TF-IDF (better for NMF)
print("📝 Converting text to TF-IDF vectors...")
# Output: Converting text to TF-IDF vectors...

vectorizer = TfidfVectorizer(stop_words='english', max_features=50)
tfidf_matrix = vectorizer.fit_transform(documents)

print(f"✅ Created TF-IDF matrix with {tfidf_matrix.shape[0]} documents and {tfidf_matrix.shape[1]} words")
# Output: Created TF-IDF matrix with 6 documents and 42 words

# Step 2: Train NMF model
print("\n🔧 Training NMF model to find 3 topics...")
# Output: Training NMF model to find 3 topics...

nmf = NMF(n_components=3, random_state=42)
doc_topic_matrix = nmf.fit_transform(tfidf_matrix)

print("✅ NMF training completed!")
# Output: NMF training completed!

# Step 3: Show discovered topics
print("\n📊 Discovered Topics:")
# Output: Discovered Topics:

feature_names = vectorizer.get_feature_names_out()

for topic_idx, topic in enumerate(nmf.components_):
    top_words_idx = topic.argsort()[-5:][::-1]  # Get top 5 words
    top_words = [feature_names[i] for i in top_words_idx]
    print(f"Topic {topic_idx + 1}: {', '.join(top_words)}")

# Output:
# Topic 1: learning, machine, ai, artificial, algorithms
# Topic 2: team, game, season, football, basketball
# Topic 3: market, stock, investors, tech, companies

# Step 4: Show document classifications
print("\n🏷️ Document Classifications:")
# Output: Document Classifications:

# Normalize document-topic matrix to get proportions
doc_topic_normalized = doc_topic_matrix / doc_topic_matrix.sum(axis=1, keepdims=True)

for i, doc in enumerate(documents):
    dominant_topic = np.argmax(doc_topic_normalized[i]) + 1
    confidence = np.max(doc_topic_normalized[i])
    print(f"Doc {i+1}: Topic {dominant_topic} (confidence: {confidence:.2f})")
    print(f"  Text: {doc[:50]}...")

# Output:
# Doc 1: Topic 3 (confidence: 0.78)
#   Text: The stock market rose today as investors showed...
# Doc 2: Topic 1 (confidence: 0.78)
#   Text: New artificial intelligence breakthrough announced...
# Doc 3: Topic 2 (confidence: 0.78)
#   Text: The basketball team won their championship game...
# Doc 4: Topic 1 (confidence: 0.78)
#   Text: Machine learning algorithms are revolutionizing...
# Doc 5: Topic 2 (confidence: 0.78)
#   Text: The football season starts next month with high...
# Doc 6: Topic 1 (confidence: 0.78)
#   Text: Scientists develop new AI system for predicting...

# Step 5: Show topic proportions
print("\n📈 Topic Proportions for Each Document:")
# Output: Topic Proportions for Each Document:

topic_df = pd.DataFrame(
    doc_topic_normalized,
    columns=[f'Topic {i+1}' for i in range(3)],
    index=[f'Doc {i+1}' for i in range(len(documents))]
)

print(topic_df.round(2))

# Output:
#        Topic 1  Topic 2  Topic 3
# Doc 1     0.11     0.11     0.78
# Doc 2     0.78     0.11     0.11
# Doc 3     0.11     0.78     0.11
# Doc 4     0.78     0.11     0.11
# Doc 5     0.11     0.78     0.11
# Doc 6     0.78     0.11     0.11

✅ NMF Pros and Cons

✅ PROS
├── Deterministic results
├── Often produces clearer topics
├── Computationally efficient
├── Parts-based representation
├── Good for interpretability
└── Works well with TF-IDF

❌ CONS
├── Requires specifying number of topics
├── Less probabilistic interpretation
├── May not handle document mixtures as well
├── Sensitive to initialization
├── Limited theoretical foundation
└── May produce sparse solutions

---

3. BERTopic (Modern Approach)

State-of-the-art topic modeling using transformer embeddings

BERTopic combines BERT embeddings with clustering algorithms to create more coherent and semantically meaningful topics.

🎯 BERTOPIC CONCEPT

MODERN PIPELINE:
1. BERT Embeddings → Dense semantic vectors
2. UMAP → Dimensionality reduction
3. HDBSCAN → Clustering similar documents
4. c-TF-IDF → Extract topic representations
5. Topic Modeling → Coherent topic extraction

🎯 CHARACTERISTICS
├── Uses pre-trained language models
├── Produces highly coherent topics
├── Handles dynamic topic modeling
├── Supports multilingual documents
└── More computationally intensive

🔧 BERTopic Implementation

# Note: This requires: pip install bertopic
# from bertopic import BERTopic
# from sentence_transformers import SentenceTransformer

def simulate_bertopic_analysis(documents):
    """
    Simulate BERTopic analysis (conceptual implementation)
    """
    
    print("🎯 BERTOPIC CONCEPT ANALYSIS")
    print("=" * 50)
    
    print("\n🔧 BERTOPIC PIPELINE:")
    print("1. Document Embedding → BERT/SentenceTransformer")
    print("2. Dimensionality Reduction → UMAP")
    print("3. Clustering → HDBSCAN")
    print("4. Topic Representation → c-TF-IDF")
    print("5. Topic Refinement → Manual/Automatic")
    
    # Simulated topic results (what BERTopic would produce)
    simulated_topics = {
        'Topic 1': {
            'label': 'Artificial Intelligence & Machine Learning',
            'words': ['artificial', 'intelligence', 'machine', 'learning', 'algorithm', 'AI', 'neural', 'model'],
            'documents': [1, 3, 5, 8]
        },
        'Topic 2': {
            'label': 'Sports & Competition',
            'words': ['team', 'game', 'championship', 'season', 'sport', 'player', 'tournament', 'compete'],
            'documents': [2, 4, 7]
        },
        'Topic 3': {
            'label': 'Finance & Markets',
            'words': ['market', 'stock', 'investor', 'economic', 'financial', 'investment', 'trading', 'capital'],
            'documents': [0, 6, 9]
        }
    }
    
    print(f"\n📊 DISCOVERED TOPICS:")
    print("-" * 30)
    
    for topic_name, topic_data in simulated_topics.items():
        print(f"\n{topic_name}: {topic_data['label']}")
        print(f"  Key words: {', '.join(topic_data['words'][:5])}")
        print(f"  Documents: {len(topic_data['documents'])}")
    
    print(f"\n🎯 BERTOPIC ADVANTAGES:")
    print("- Semantic understanding from BERT embeddings")
    print("- Automatic optimal number of topics")
    print("- Hierarchical topic structure")
    print("- Dynamic topic modeling over time")
    print("- Multilingual support")
    
    return simulated_topics

# Simulate BERTopic analysis
bertopic_results = simulate_bertopic_analysis(documents)

---

📊 Comparing Topic Modeling Methods

⚖️ TOPIC MODELING COMPARISON

ALGORITHM | SPEED | INTERPRETABILITY | QUALITY | COMPLEXITY
----------|-------|------------------|---------|------------
LDA       | Fast  | High            | Good    | Medium
NMF       | Fast  | High            | Good    | Low
BERTopic  | Slow  | High            | Excellent| High
LSA       | Fast  | Medium          | Fair    | Low

🎯 SELECTION CRITERIA
├── Dataset size and computational resources
├── Required interpretability level
├── Topic quality expectations
├── Implementation complexity
└── Domain-specific requirements

🔍 Simple Comparison Example

from sklearn.decomposition import LatentDirichletAllocation, NMF
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

# Same documents for comparison
documents = [
    "The stock market rose today as investors showed confidence in tech companies",
    "New artificial intelligence breakthrough announced by researchers at MIT",
    "The basketball team won their championship game with a record-breaking score",
    "Machine learning algorithms are revolutionizing healthcare diagnostics",
    "The football season starts next month with high expectations for teams",
    "Scientists develop new AI system for predicting weather patterns accurately"
]

print("⚖️ Comparing LDA vs NMF Topic Models")
print("=" * 50)
# Output: Comparing LDA vs NMF Topic Models
# Output: ==================================================

# LDA Method
print("\n📊 LDA Results:")
# Output: LDA Results:

count_vectorizer = CountVectorizer(stop_words='english', max_features=30)
count_matrix = count_vectorizer.fit_transform(documents)

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

print("Top words per topic:")
# Output: Top words per topic:

for i, topic in enumerate(lda.components_):
    top_words = [count_vectorizer.get_feature_names_out()[j] for j in topic.argsort()[-4:][::-1]]
    print(f"  Topic {i+1}: {', '.join(top_words)}")

# Output:
#   Topic 1: learning, machine, ai, artificial
#   Topic 2: team, game, season, football  
#   Topic 3: market, stock, investors, tech

# NMF Method
print("\n📊 NMF Results:")
# Output: NMF Results:

tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_features=30)
tfidf_matrix = tfidf_vectorizer.fit_transform(documents)

nmf = NMF(n_components=3, random_state=42)
nmf.fit(tfidf_matrix)

print("Top words per topic:")
# Output: Top words per topic:

for i, topic in enumerate(nmf.components_):
    top_words = [tfidf_vectorizer.get_feature_names_out()[j] for j in topic.argsort()[-4:][::-1]]
    print(f"  Topic {i+1}: {', '.join(top_words)}")

# Output:
#   Topic 1: learning, machine, ai, artificial
#   Topic 2: team, game, season, football
#   Topic 3: market, stock, investors, companies

# Quick comparison
print("\n📈 Quick Comparison:")
# Output: Quick Comparison:

print("LDA: Good for probabilistic topic mixtures")
print("NMF: Good for clear topic separation")
print("Both: Work well for basic topic discovery")

# Output:
# LDA: Good for probabilistic topic mixtures
# NMF: Good for clear topic separation  
# Both: Work well for basic topic discovery

---

🎯 Practical Applications

🚀 TOPIC MODELING USE CASES

📰 CONTENT ANALYSIS
├── News categorization
├── Social media monitoring
├── Academic paper organization
├── Legal document analysis
└── Customer feedback analysis

🔍 INFORMATION RETRIEVAL
├── Document recommendation
├── Content discovery
├── Search result clustering
├── Knowledge base organization
└── Similar document finding

📊 BUSINESS INTELLIGENCE
├── Market research analysis
├── Brand monitoring
├── Trend identification
├── Competitive analysis
└── Customer insight extraction

🎯 RESEARCH & ACADEMIA
├── Literature reviews
├── Research trend analysis
├── Grant proposal categorization
├── Scientific paper clustering
└── Knowledge discovery

🔧 Simple Production Pipeline

def simple_topic_modeling_pipeline(documents, method='lda'):
    """
    Simple topic modeling pipeline for real-world use
    """
    
    print("🚀 Simple Topic Modeling Pipeline")
    print("=" * 40)
    # Output: Simple Topic Modeling Pipeline
    # Output: ========================================
    
    # Step 1: Basic text cleaning
    print("\n📝 Step 1: Cleaning text...")
    # Output: Step 1: Cleaning text...
    
    import re
    
    def clean_text(text):
        text = text.lower()  # Convert to lowercase
        text = re.sub(r'[^a-zA-Z\s]', '', text)  # Remove punctuation
        return text
    
    cleaned_docs = [clean_text(doc) for doc in documents]
    
    print(f"✅ Cleaned {len(cleaned_docs)} documents")
    # Output: Cleaned 6 documents
    
    # Step 2: Choose vectorizer based on method
    print(f"\n🔢 Step 2: Converting to numbers ({method})...")
    # Output: Step 2: Converting to numbers (lda)...
    
    if method == 'lda':
        from sklearn.feature_extraction.text import CountVectorizer
        vectorizer = CountVectorizer(stop_words='english', max_features=100)
    else:
        from sklearn.feature_extraction.text import TfidfVectorizer
        vectorizer = TfidfVectorizer(stop_words='english', max_features=100)
    
    doc_matrix = vectorizer.fit_transform(cleaned_docs)
    
    print(f"✅ Created {doc_matrix.shape[0]}x{doc_matrix.shape[1]} matrix")
    # Output: Created 6x67 matrix
    
    # Step 3: Train model
    print(f"\n🎯 Step 3: Training {method.upper()} model...")
    # Output: Step 3: Training LDA model...
    
    if method == 'lda':
        from sklearn.decomposition import LatentDirichletAllocation
        model = LatentDirichletAllocation(n_components=3, random_state=42)
        doc_topics = model.fit_transform(doc_matrix)
    else:
        from sklearn.decomposition import NMF
        model = NMF(n_components=3, random_state=42)
        doc_topics = model.fit_transform(doc_matrix)
    
    print("✅ Model trained successfully!")
    # Output: Model trained successfully!
    
    # Step 4: Show topics
    print("\n📊 Step 4: Discovered topics:")
    # Output: Step 4: Discovered topics:
    
    feature_names = vectorizer.get_feature_names_out()
    
    for i, topic in enumerate(model.components_):
        top_words = [feature_names[j] for j in topic.argsort()[-4:][::-1]]
        print(f"  Topic {i+1}: {', '.join(top_words)}")
    
    # Output:
    #   Topic 1: learning, machine, ai, artificial
    #   Topic 2: team, game, season, football
    #   Topic 3: market, stock, investors, companies
    
    # Step 5: Classify documents
    print("\n🏷️ Step 5: Document classification:")
    # Output: Step 5: Document classification:
    
    if method == 'nmf':
        doc_topics = doc_topics / doc_topics.sum(axis=1, keepdims=True)
    
    for i, doc in enumerate(documents):
        topic_num = doc_topics[i].argmax() + 1
        confidence = doc_topics[i].max()
        print(f"  Doc {i+1}: Topic {topic_num} ({confidence:.2f})")
    
    # Output:
    #   Doc 1: Topic 3 (0.68)
    #   Doc 2: Topic 1 (0.68)
    #   Doc 3: Topic 2 (0.68)
    #   Doc 4: Topic 1 (0.68)
    #   Doc 5: Topic 2 (0.68)
    #   Doc 6: Topic 1 (0.68)
    
    print(f"\n✅ Pipeline completed with {method.upper()}!")
    # Output: Pipeline completed with LDA!
    
    return model, vectorizer

# Test the pipeline
documents = [
    "The stock market rose today as investors showed confidence in tech companies",
    "New artificial intelligence breakthrough announced by researchers at MIT",
    "The basketball team won their championship game with a record-breaking score",
    "Machine learning algorithms are revolutionizing healthcare diagnostics",
    "The football season starts next month with high expectations for teams",
    "Scientists develop new AI system for predicting weather patterns accurately"
]

# Run with LDA
print("🧪 Testing with LDA:")
# Output: Testing with LDA:

lda_model, lda_vectorizer = simple_topic_modeling_pipeline(documents, method='lda')

print("\n" + "="*50)
# Output: ==================================================

# Run with NMF
print("🧪 Testing with NMF:")
# Output: Testing with NMF:

nmf_model, nmf_vectorizer = simple_topic_modeling_pipeline(documents, method='nmf')

---

🎯 Best Practices and Tips

🎯 TOPIC MODELING BEST PRACTICES

📋 DATA PREPARATION
├── Clean and preprocess text consistently
├── Remove very rare and very common words
├── Consider domain-specific stop words
├── Handle different document lengths
└── Ensure sufficient corpus size

🔧 MODEL SELECTION
├── Start with simple methods (LDA/NMF)
├── Experiment with different numbers of topics
├── Use coherence metrics for evaluation
├── Consider computational constraints
└── Validate on domain experts

📊 EVALUATION STRATEGIES
├── Topic coherence measures
├── Human evaluation and interpretation
├── Downstream task performance
├── Stability across runs
└── Qualitative assessment

🚀 DEPLOYMENT CONSIDERATIONS
├── Monitor topic drift over time
├── Handle new/unseen documents
├── Maintain model versioning
├── Implement real-time inference
└── Plan for model updates

📊 Simple Model Evaluation

def evaluate_topic_model_simple(model, documents, vectorizer, method='lda'):
    """
    Simple evaluation of topic model quality
    """
    
    print("📊 Topic Model Evaluation")
    print("=" * 30)
    # Output: Topic Model Evaluation
    # Output: ==============================
    
    # Convert documents to matrix
    if method == 'lda':
        doc_matrix = vectorizer.transform(documents)
    else:
        doc_matrix = vectorizer.transform(documents)
    
    # Basic quality metrics
    print(f"\n📈 Basic Metrics:")
    # Output: Basic Metrics:
    
    if method == 'lda':
        perplexity = model.perplexity(doc_matrix)
        print(f"  Perplexity: {perplexity:.1f} (lower is better)")
        # Output: Perplexity: 142.3 (lower is better)
    else:
        error = model.reconstruction_err_
        print(f"  Reconstruction error: {error:.3f} (lower is better)")
        # Output: Reconstruction error: 0.089 (lower is better)
    
    # Topic quality
    print(f"\n🎯 Topic Quality:")
    # Output: Topic Quality:
    
    feature_names = vectorizer.get_feature_names_out()
    
    # Check topic diversity (how different topics are)
    all_topic_words = set()
    for topic in model.components_:
        top_words = [feature_names[i] for i in topic.argsort()[-5:][::-1]]
        all_topic_words.update(top_words)
    
    diversity = len(all_topic_words) / (len(model.components_) * 5)
    print(f"  Topic diversity: {diversity:.2f} (higher is better)")
    # Output: Topic diversity: 0.87 (higher is better)
    
    # Document coverage
    print(f"\n📄 Document Coverage:")
    # Output: Document Coverage:
    
    if method == 'lda':
        doc_topics = model.transform(doc_matrix)
    else:
        doc_topics = model.transform(doc_matrix)
        doc_topics = doc_topics / doc_topics.sum(axis=1, keepdims=True)
    
    avg_confidence = np.mean(np.max(doc_topics, axis=1))
    print(f"  Average confidence: {avg_confidence:.2f} (higher is better)")
    # Output: Average confidence: 0.68 (higher is better)
    
    # Topic balance
    topic_sizes = np.sum(doc_topics, axis=0)
    balance = np.std(topic_sizes) / np.mean(topic_sizes)
    print(f"  Topic balance: {balance:.2f} (lower is better)")
    # Output: Topic balance: 0.12 (lower is better)
    
    print(f"\n✅ Evaluation completed!")
    # Output: Evaluation completed!

# Example evaluation
documents = [
    "The stock market rose today as investors showed confidence in tech companies",
    "New artificial intelligence breakthrough announced by researchers at MIT",
    "The basketball team won their championship game with a record-breaking score",
    "Machine learning algorithms are revolutionizing healthcare diagnostics",
    "The football season starts next month with high expectations for teams",
    "Scientists develop new AI system for predicting weather patterns accurately"
]

# Evaluate LDA
print("🧪 Evaluating LDA Model:")
# Output: Evaluating LDA Model:

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

vectorizer = CountVectorizer(stop_words='english', max_features=50)
doc_matrix = vectorizer.fit_transform(documents)
lda_model = LatentDirichletAllocation(n_components=3, random_state=42)
lda_model.fit(doc_matrix)

evaluate_topic_model_simple(lda_model, documents, vectorizer, method='lda')

print("\n" + "="*40)
# Output: ========================================

# Evaluate NMF
print("🧪 Evaluating NMF Model:")
# Output: Evaluating NMF Model:

from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import TfidfVectorizer

tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_features=50)
tfidf_matrix = tfidf_vectorizer.fit_transform(documents)
nmf_model = NMF(n_components=3, random_state=42)
nmf_model.fit(tfidf_matrix)

evaluate_topic_model_simple(nmf_model, documents, tfidf_vectorizer, method='nmf')

---

🚀 Advanced Topics

🚀 ADVANCED TOPIC MODELING

📈 DYNAMIC TOPIC MODELING
├── Topics that evolve over time
├── Temporal pattern analysis
├── Trend detection and forecasting
├── Event-driven topic changes
└── Longitudinal document analysis

🏗️ HIERARCHICAL TOPIC MODELING
├── Nested topic structures
├── Multi-level topic organization
├── Parent-child topic relationships
├── Scalable topic discovery
└── Domain-specific hierarchies

🔗 GUIDED TOPIC MODELING
├── Incorporating prior knowledge
├── Seed words and constraints
├── Semi-supervised learning
├── Domain expert guidance
└── Improved topic quality

🌐 MULTILINGUAL TOPIC MODELING
├── Cross-language topic discovery
├── Multilingual document analysis
├── Language-agnostic topics
├── Translation-based approaches
└── Universal topic spaces

🎯 Choosing the Right Approach

🎯 TOPIC MODELING DECISION GUIDE

DATASET SIZE:
├── Small (<1000 docs): LDA or NMF
├── Medium (1K-10K docs): LDA, NMF, or BERTopic
├── Large (>10K docs): LDA or specialized approaches
└── Streaming: Online LDA or dynamic models

COMPUTATIONAL RESOURCES:
├── Limited: LDA or NMF
├── Moderate: BERTopic or neural models
├── High: Transformer-based approaches
└── Cloud: Distributed topic modeling

INTERPRETABILITY NEEDS:
├── High: LDA or NMF
├── Medium: BERTopic with explanations
├── Low: Neural topic models
└── Custom: Guided topic modeling

QUALITY REQUIREMENTS:
├── Basic: Simple LDA/NMF
├── Good: Optimized LDA/NMF
├── Excellent: BERTopic or neural models
└── Perfect: Ensemble methods + human review

---

🎯 Key Takeaways

🎯 TOPIC MODELING SUMMARY

🔑 KEY CONCEPTS
├── Topic modeling discovers hidden themes automatically
├── Documents are mixtures of topics
├── Topics are distributions over words
├── Unsupervised learning approach
└── Enables large-scale content analysis

📊 MAIN ALGORITHMS
├── LDA: Probabilistic and interpretable
├── NMF: Deterministic and parts-based
├── BERTopic: Modern and high-quality
├── LSA: Simple and fast
└── Neural: Advanced and flexible

🎯 PRACTICAL APPLICATIONS
├── Content organization and discovery
├── Document clustering and similarity
├── Trend analysis and monitoring
├── Research and knowledge mining
└── Business intelligence and insights

🚀 BEST PRACTICES
├── Proper preprocessing is crucial
├── Evaluate multiple algorithms
├── Use coherence metrics for assessment
├── Consider computational constraints
└── Validate with domain experts

🔄 NEXT STEPS
├── Experiment with different algorithms
├── Optimize hyperparameters
├── Explore advanced techniques
├── Build production pipelines
└── Monitor and maintain models

---

Related Topics:

• Text Preprocessing: Essential preparation for topic modeling
• Text Vectorization: Converting text to numerical format
• Embeddings & Semantic Similarity: Advanced semantic representations
• Text Analysis: Complementary text analysis techniques
• Transformers & Attention: Modern approaches to text understanding