a5c-ai Topic Modeling and Text Mining
Apply LDA, NMF, and other computational methods to discover patterns in large text corpora with appropriate parameter tuning.
Overview
This skill enables computational analysis of large text collections. It encompasses topic modeling, text mining techniques, and pattern discovery to reveal structures and themes in textual data for humanistic inquiry.
Capabilities
Topic Modeling
- LDA implementation
- NMF analysis
- Structural topic models
- Dynamic topic models
- Parameter optimization
Text Preprocessing
- Tokenization
- Stopword removal
- Lemmatization/stemming
- N-gram extraction
- Document-term matrices
Pattern Discovery
- Word frequency analysis
- Collocation detection
- Named entity recognition
- Sentiment analysis
- Network extraction
Visualization
- Word clouds
- Topic distributions
- Temporal trends
- Network graphs
- Interactive displays
Usage Guidelines
Analysis Process
- Prepare text corpus
- Preprocess documents
- Select modeling approach
- Tune parameters
- Run analysis
- Interpret results
- Validate findings
Parameter Considerations
- Number of topics
- Iteration counts
- Hyperparameters
- Coherence metrics
- Validation approaches
Interpretation Guidelines
- Examine topic words
- Review representative documents
- Consider domain knowledge
- Validate with close reading
- Acknowledge limitations
Integration Points
Related Processes
- Text Mining and Distant Reading
- Corpus Linguistics Analysis
- Network Analysis for Humanities
Collaborating Skills
- tei-text-encoding
- gis-mapping-humanities
- literary-close-reading
References
- Digital humanities methodology
- Topic modeling tutorials
- Text analysis tools
- Computational linguistics resources