This is code that accompanies the paper titled "Keyword Assisted Embedded Topic Model" by Bahareh Harandizadeh, J. Hunter Priniski, and Fred Morstatter. ETM was originally published by Adji B. Dieng, Francisco J. R. Ruiz, and David M. Blei on a article titled Topic Modeling in Embedding Spaces. The backbone of this repo has been extracted from the ETM original code and an adoption code from it. Most of the original code was kept here, with some changes to equip ETM with the ability to incorporate user knowledge in the form of informative topic-level priors over the vocabulary.

Create a new virtual enviroment and install ETM model package using pip by running: pip install -U embedded_topic_model

You first need to create a config file and put it in configs folder(a sample for 20newsg dataset is provided). In the configuration file the following items should be set:

Dataset:
 - folder-path: the root folder path, all data files(result-file,data-file,sw-file) should be placed in the this path.
 - result-file: the results folder path
 - data-file: input file name
 - sw-file: seed_words file name
Model:
 - bs: batch_size
 - nt: number of topics
 - epochs: number of epochs
 - drop_out: drop_out percentage 
 - theta_act: activation function 
 - lr: learning rate 
 - lambda_theta: Lambda_1 
 - lambda_alpha: Lambda_2 
 - path: The path for save the trained model

Then go the the main director(~/embedded-topic-model_mod) and in the terminal running code using:

python run.py --config configs/name_config_file.yaml

To site KeyETM, please use the following link:

@article{harandizadeh2021keyword,
  title={Keyword Assisted Embedded Topic Model},
  author={Harandizadeh, Bahareh and Priniski, J Hunter and Morstatter, Fred},
  journal={arXiv preprint arXiv:2112.03101},
  year={2021}
}