---

name: ds-topic-modeling compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Implements topic modeling using Latent Dirichlet Allocation (LDA) Non-negative Matrix Factorization (NMF), and other topic extraction methods"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-association-rules, ds-clustering, ds-dimensionality-reduction role: implementation scope: implementation triggers: topic modeling, LDA, NMF, topic extraction, latent dirichlet allocation text analysis archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

---

Topic Modeling

Comprehensive guide to topic modeling in machine learning and data science workflows.

When to Use This Skill

• Solving real-world unsupervised learning problems
• Building machine learning pipelines with topic modeling
• Implementing best practices for topic modeling
• Optimizing model performance using topic modeling techniques
• Learning industry-standard approaches to topic modeling

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require topic modeling rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Topic Modeling is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Topic Modeling

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
from typing import List, Dict, Any

def basic_topic_modeling(texts: List[str], n_topics: int = 5) -> Dict[str, Any]:
    """
    Perform basic topic modeling using Latent Dirichlet Allocation (LDA).
    
    Args:
        texts: List of raw text documents
        n_topics: Number of topics to extract
        
    Returns:
        Dictionary containing fitted model, vectorizer, and topic-word distributions
    """
    if not texts:
        raise ValueError("Input texts list cannot be empty")
        
    # Vectorize text documents
    vectorizer = CountVectorizer(max_df=0.95, min_df=2, stop_words='english')
    doc_term_matrix = vectorizer.fit_transform(texts)
    
    # Initialize and fit LDA model
    lda_model = LatentDirichletAllocation(
        n_components=n_topics
        max_iter=10
        learning_method='online'
        random_state=42
    )
    lda_model.fit(doc_term_matrix)
    
    # Extract top words per topic
    feature_names = vectorizer.get_feature_names_out()
    topics = {}
    for idx, topic in enumerate(lda_model.components_):
        top_words_idx = topic.argsort()[:-10:-1]
        topics[f'topic_{idx}'] = [feature_names[i] for i in top_words_idx]
        
    return {
        'model': lda_model
        'vectorizer': vectorizer
        'topics': topics
        'doc_term_matrix': doc_term_matrix
    }

Pattern 2: Production-Ready Topic Modeling

import logging
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import NMF
from sklearn.pipeline import Pipeline
from typing import Any, Dict, List, Optional
import warnings
warnings.filterwarnings('ignore')

logger = logging.getLogger(__name__)

class TopicModeling:
    """Production-grade implementation of Topic Modeling using NMF"""
    
    def __init__(self, n_topics: int = 5, max_features: int = 1000, random_state: int = 42):
        self.n_topics = n_topics
        self.max_features = max_features
        self.random_state = random_state
        self.pipeline: Optional[Pipeline] = None
        self.feature_names: List[str] = []
        
    def _validate_input(self, data: pd.DataFrame, text_column: str) -> None:
        if text_column not in data.columns:
            raise ValueError(f"Column '{text_column}' not found in DataFrame")
        if data[text_column].isnull().any():
            logger.warning("Dropping rows with missing text data")
            data = data.dropna(subset=[text_column])
        if len(data) < self.n_topics:
            raise ValueError("Dataset size must be greater than n_topics")
            
    def _build_pipeline(self) -> Pipeline:
        vectorizer = TfidfVectorizer(
            max_features=self.max_features
            stop_words='english'
            ngram_range=(1, 2)
        )
        nmf_model = NMF(
            n_components=self.n_topics
            init='nndsvd'
            random_state=self.random_state
            max_iter=200
        )
        return Pipeline([('tfidf', vectorizer), ('nmf', nmf_model)])
        
    def execute(self, data: pd.DataFrame, text_column: str = 'text') -> Dict[str, Any]:
        """Execute Topic Modeling on data"""
        self._validate_input(data, text_column)
        
        self.pipeline = self._build_pipeline()
        tfidf_matrix = self.pipeline.named_steps['tfidf'].fit_transform(data[text_column])
        self.pipeline.fit(tfidf_matrix)
        
        feature_names = self.pipeline.named_steps['tfidf'].get_feature_names_out()
        topic_words = {}
        for i, topic in enumerate(self.pipeline.named_steps['nmf'].components_):
            top_indices = topic.argsort()[:-10:-1]
            topic_words[f'topic_{i}'] = [feature_names[idx] for idx in top_indices]
            
        reconstructed = self.pipeline.named_steps['nmf'].transform(tfidf_matrix) @ \
                        self.pipeline.named_steps['nmf'].components_
        inertia = float(np.linalg.norm(tfidf_matrix.toarray() - reconstructed))
        
        return {
            'topics': topic_words
            'inertia': inertia
            'pipeline': self.pipeline
            'document_topics': self.pipeline.named_steps['nmf'].transform(tfidf_matrix)
        }

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging
• Follow DRY (Don't Repeat Yourself) and KISS (Keep It Simple, Stupid) principles for maintainable code

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Topic Modeling — Wikipedia
• Gensim Documentation
• LDA Topic Modeling — Scikit-learn docs
• BERTopic Documentation
• NMF for Topic Extraction (Scikit-learn)