1. Corpus understanding before modeling: Explore the data before running models. Know your documents.
2. Method selection based on research question: Different questions need different methods. Topic models answer different questions than classifiers.
3. Validation is essential: Algorithmic output is not ground truth. Human validation and multiple diagnostics are required.
4. Reproducibility: Document all preprocessing decisions, parameters, and random seeds.
5. Appropriate interpretation: Text analysis results require careful, qualified interpretation. Avoid overclaiming.

• Clarify the research question (descriptive, exploratory, or inferential)
• Determine corpus characteristics (size, type, language)
• Select appropriate methods based on research goals
• Choose language (R or Python) based on method needs
• Plan validation approach

Pause: Confirm design with user before corpus preparation.

• Load and inspect the corpus
• Make preprocessing decisions (tokenization, stopwords, stemming)
• Create document-term matrix or embeddings
• Generate descriptive statistics
• Visualize corpus characteristics

Pause: Review corpus characteristics and confirm preprocessing.

• Specify model parameters (K for topics, embedding dimensions, etc.)
• Define training/validation splits if applicable
• Document preprocessing pipeline explicitly
• Plan evaluation metrics
• Pre-specify dictionary/lexicon choices

Pause: User approves specification before analysis.

• Run primary models
• Extract and interpret results
• Create initial visualizations
• Assess model fit and convergence
• Document any deviations from specification

Pause: User reviews results before validation.

• Human validation (sample coding, topic labeling)
• Model diagnostics (coherence, classification metrics)
• Sensitivity analysis (different K, preprocessing, seeds)
• Compare to alternative methods if applicable

Pause: User assesses validity before final outputs.

• Create publication-quality tables and figures
• Write results narrative with appropriate caveats
• Document limitations
• Prepare replication materials

1. Ask about the research question:

   "What are you trying to learn from the text? Are you exploring themes, measuring concepts, classifying documents, or something else?"

2. Ask about the corpus:

   "What text data do you have? How many documents, what type (articles, social media, interviews), and what language?"

3. Ask about methods:

   "Do you have specific methods in mind (topic models, sentiment, classification), or would you like help selecting based on your question?"

4. Recommend language based on methods:
   • Topic models with covariates → R
   • Neural methods (BERT, BERTopic) → Python
   • Both classical and neural → May need both
5. Then proceed with Phase 0 to formalize the research design.

• Preprocessing matters: Document every decision (stopwords, stemming, thresholds)
• K is not a tuning parameter: Number of topics should be interpretable, not just optimal by metrics
• Validation is not optional: Algorithmic output needs human validation
• Show your dictionaries: If using lexicons, readers should see the word lists
• Uncertainty exists: Topic models and classifiers have uncertainty; acknowledge it
• Corpus defines scope: Findings apply to the analyzed corpus, not "language" generally