LDA: Implementation of Topic Models with Collapsed Gibbs Sampling

Jonathan Chang's software implements latent Dirichlet allocation and related models like sLDA and corrLDA. The core inference for these models is performed via a fast collapsed Gibbs sampler written in C. Utility functions for reading/writing topic model data and examining posterior distributions are also included.

Use Cases

• Implementing LDA topic modeling based on the provided collapsed Gibbs sampler.
• Extending topic models to supervised settings based on the included sLDA implementation.
• Modeling correlated topics based on the corrLDA model mentioned in the description.
• Analyzing network data based on the mixed-membership stochastic blockmodel feature.
• Examining posterior distributions from topic models using the included utility tools.

Strengths

• Implements multiple related models including LDA, sLDA, and corrLDA.
• Core sampler is written in C, which suggests a focus on computational performance.
• Includes utility functions for data handling and posterior analysis.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and dataset scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Jonathan Chang

Collection Method

Software implementation of statistical models.

Time Range

null

Freshness

Last update date is unknown; freshness unverified.

Geography

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