LDAvis is an R package designed to help users interpret topic models by providing an interactive, web-based visualization. It extracts key information from fitted topic models, enabling users to explore relationships between topics, terms, and documents, thereby facilitating a deeper understanding of textual data.

How It Works

LDAvis leverages a scatter plot to represent topics, where proximity indicates semantic similarity. Terms are displayed as word clouds, sized by their relevance to the selected topic. The visualization uses a two-dimensional projection (often t-SNE or similar) of the topic-term distributions to position topics. Users can adjust a slider to dynamically re-weight terms based on their relevance and frequency, allowing for nuanced exploration of topic content.

Quick Start & Requirements

• Install from CRAN: install.packages("LDAvis")
• Install from GitHub: devtools::install_github("cpsievert/LDAvis")
• Requires R.
• Official documentation and examples are available via help(createJSON, package = "LDAvis") and the vignette vignette("details", package = "LDAvis").

Highlighted Details

• Facilitates interpretation of LDA topic models.
• Interactive visualization with topic-term relationships.
• Supports dynamic term re-weighting via a lambda slider.
• Can encode visualization state into URLs for sharing.

Maintenance & Community

• Developed by Carson Sievert.
• Additional data sets are available in the LDAvisData package.
• Links to video demos and a research paper are provided.

Licensing & Compatibility

• License: Not explicitly stated in the README.
• Compatibility: Designed for R, can integrate with topic models generated by other tools like MALLET and gensim.

Limitations & Caveats

LDAvis itself does not perform topic model fitting; it requires pre-fitted models. The README does not specify the license, which could impact commercial use or closed-source integration.