TopicModels.jl
TopicModels.jl offers a specialized implementation of Bayesian hierarchical mixture models tailored for topic modeling in Julia. Primarily focused on Latent Dirichlet Allocation (LDA), it facilitates data manipulation and inference procedures, enabling users to read documents, define model parameters, train models using collapsed Gibbs sampling, and extract top words associated with identified topics.
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Top TopicModels.jl Features
- Gibbs sampling implementation
- LDA model specialization
- Hyperparameter customization
- Document format flexibility
- Lexicon management utilities
- Topic summarization functions
- In-place model mutation
- Iterative training process
- Comprehensive documentation
- Feedback-driven development
- Bayesian inference techniques
- Discrete data handling
- Corpus loading utilities
- Top words retrieval feature
- Support for custom document formats
- Efficient memory usage
- Multi-topic analysis capability
- User-friendly interface
- Integration with Julia ecosystem.
