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Generates related keywords from a corpus bundled together into topic areas. 10 keywords are generated per topic. A single text file is uploaded and each line is treated as a separate document.
Baylor University Libraries: LSI Topic Model
Implements the Latent Semantic Index
From Wikipedia https://en.wikipedia.org/wiki/Latent_semantic_analysis#Latent_semantic_indexing “Latent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text. LSI is based on the principle that words that are used in the same contexts tend to have similar meanings.”
This Python application was built by the Baylor University Libraries to assist researchers to implement unsupervised topic modelling on 1-line documents, such as Twitter social media.