This is a microservice that can run in a docker container and perform LDA topic detection when queried by a REST call.
The project uses the Gensim Topic Modeling Library: (https://radimrehurek.com/gensim/)
See swagger.yaml for details. The tool at https://editor.swagger.io/ can be used to render the swagger file.
chunksize - Number of documents to be used in each training chunk. Default: 2000
passes - Number of passes through the corpus during training. Default: 1
iterations - Maximum number of iterations through the corpus when inferring the topic distribution of a corpus. Default: 500
n_topics - The number of topics that shall be detected. Higher topic coherence indicates a better n_topics. Can be any number > 0. Default: 10
stemming - Include stemming in preprocessing. Default: False
fix_random - Set to true to fix random seed to 0. This will make the results reproducible. Default: false
Free use of this software is granted under the terms of the GPL version 3 (GPL 3.0).