SeaNMF

This the implementation of the paper

• Tian Shi, Kyeongpil Kang, Jaegul Choo and Chandan K. Reddy, "Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations", In Proceedings of the International Conference on World Wide Web (WWW), Lyon, France, April 2018. PDF

Requirements

• Python 3.5.2
• argparse

usage:

Data Process

• Tokenize with NLTK, SpaCy or CoreNLP
• Remove special characters.
• Remove stop-words.
• Edit the argument of data_process.py
• Run python3 data_process.py to prepare the document-term matrix and vocabulary.

Train

• Run python3 train.py --help to see the full list of options.

Evaluation

• Run python3 vis_topic.py to calculate the PMI and visualize the top keywords in each topic.