PySLDANormal is a supervised Latent Dirichlet Allocation topic modeling package for regression.
Please download the latest version from our GitHub repository.
Please send any bugs or problems to Ke Zhai (kzhai@umd.edu).
This package depends on many external python libraries, such as numpy, scipy and nltk.
Assume the PySLDANormal package is downloaded under directory $PROJECT_SPACE/src/
, i.e.,
$PROJECT_SPACE/src/PySLDANormal
To prepare the example dataset,
tar zxvf review.scale.stem.tar.gz
To launch PySLDANormal, first redirect to the directory of PySLDANormal source code,
cd $PROJECT_SPACE/src/PySLDANormal
and run the following command on example dataset,
python -m launch_train --input_directory=./review.scale.stem/ --output_directory=./ --number_of_topics=10 --training_iterations=50
The generic argument to run PySLDANormal is
python -m launch_train --input_directory=$INPUT_DIRECTORY/$CORPUS_NAME --output_directory=$OUTPUT_DIRECTORY --number_of_topics=$NUMBER_OF_TOPICS --training_iterations=$NUMBER_OF_ITERATIONS
You should be able to find the output at directory $OUTPUT_DIRECTORY/$CORPUS_NAME
.
Under any cirsumstances, you may also get help information and usage hints by running the following command
python -m launch_train --help