TOPIC MODELLING.

Create Data Directory.

Steps:

• mkdir data
• mkdir -p data/corpus
• mkdir -p data/embedded
• Extract all the documents in ./data. (EXPERIMENTAL)
  • dvc pull
• If the above steps fails, please raise an issue on this repository.

Create Logs Directory.

Steps:

• mkdir logs

Create Results Directory.

Steps:

• mkdir res

Execute the scripts.

• Executing Latent Dirichlet Allocation with TF-IDF vectorization.
  • python simple_topic_modelling.py

REFERENCES

1. Topic modelling with Word Embedding, F. Esposito, A. Corraza, F. Cutugno, Third Italian Conference on Computational Linguistics CLiC-it 2016