LDA.js
LDA.js enables efficient topic modeling in Node.js using the Latent Dirichlet Allocation algorithm. It skillfully identifies multiple topics within documents, extracting relevant keywords while filtering out common terms. Users can customize stop-words for various languages and control randomness in results, ensuring tailored and reproducible outcomes for diverse datasets.
Top LDA.js Alternatives
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ml.js
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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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Company Information
- Company: LDA.js
- Country: United States
Top LDA.js Features
- Probabilistic topic extraction
- Multi-language support
- Custom stop-words lists
- Random seed configuration
- Node.js compatibility
- Document-specific topic identification
- Keyword association mapping
- Feedback-driven updates
- Easy integration with existing projects
- Array-based topic results
- Flexible output format
- Enhanced document analysis
- User-defined topic counts
- Language-specific customization
- Support for diverse document types
- Visualization tools for topics
- Efficient keyword filtering
- High performance and scalability
- Open-source implementation.
