lda-project/lda
Lda
This project is a statistical library and computational framework designed for topic modeling within large document collections. It functions as a natural language processing toolkit that identifies hidden thematic structures by analyzing word frequency patterns across unstructured text data.
The library employs Latent Dirichlet Allocation to model documents as mixtures of topics and topics as mixtures of words. It utilizes Gibbs sampling and iterative state-space updating to estimate the posterior distribution of latent variables, refining topic assignments until the model reaches statistical convergence.
To handle large-scale datasets, the framework incorporates memory-efficient sparse matrix structures to manage document-term matrices. It provides capabilities for calculating and extracting probability distributions of topics for individual documents and entire collections, while including tools to monitor computational performance during analysis.
Features
- Latent Dirichlet Allocations - Employs Latent Dirichlet Allocation to model documents as mixtures of topics and topics as mixtures of words.
- Gibbs Samplers - Implements Gibbs sampling to estimate posterior distributions of latent variables during model inference.
- Topic Modeling Libraries - Identifies latent thematic structures within document collections by analyzing word frequency patterns using statistical sampling.
- Natural Language Processing Libraries - Acts as a natural language processing toolkit for analyzing and categorizing unstructured text data into thematic clusters.
- Iterative State Updates - Refines topic assignments for individual words by repeatedly sampling from conditional distributions until statistical convergence.
- Topic Distribution Analysis - Calculates the probability distribution of topics for individual documents to reveal their specific thematic composition.
- Dataset Topic Distributions - Calculates probability distributions of topics for entire collections to reveal the underlying thematic composition of the dataset.
- Document-Term Matrices - Provides memory-efficient sparse matrix structures to manage document-term frequency data for topic modeling.
- Sparse Matrix Storage - Incorporates memory-efficient sparse matrix structures to reduce computational overhead when processing massive document collections.
