About

Practice of LDA and other Topic Model based Collapsed Gibbs Sampling.

todo

• Normal LDA
• Dynamic Topic Model

Implementation

• Michal Rosen-Zvi, Tom Griffiths, Mark Steyvers, Padhraic Smyth. The Author-Topic Model for Authors and Documents. UAI (2004)
• Yi Fang, Luo Si, Naveen Somasundaram, Zhengtao Yu. Mining contrastive opinions on political texts using cross-perspective topic model. WSDM (2012)
• Daniel Ramage, David Hall, Ramesh Nallapati, Christopher D. Manning. Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. EMNLP (2009)

Requirements

• g++ ( >= 4.6)
• boost

Author-Topic Model

usage

make author_topic
./author_topic input.tsv alpha t iter lim

params

• input.tsv: input file(separated \t and :). Each line is document.
  • format: author:author:...:author \t word:word:word ... :word
• alpha: \alpha.
  • \beta if fix(= 0.01)
• t: # of topics.
• iter: # of gibbs sampling iterations.
• lim: limit of outputs.

Cross-Perspective Topic Model

usage

make cpt
./cpt per1_topic.tsv per1_opinion.tsv per2_topic.tsv per2_opinion.tsv alpha beta beta1 beta2 k iter lim

params

• per1_topic, per2_topic: input topic file. Each line is document.
  • format: word \t word ...
• per1_opinion, per2_opinion: input opinion file. Each line is document.
  • format: word \t word ...
  • topic/opinion file must same size.
• alpha: \alpha.
• beta, beta1, beta2: \beta.
• k: # of topics.
• iter: # of sampling iterations.
• lim: limit of outputs.

Labeled LDA

usage

make labeled_lda
./labeled_lda document_word_file document_label_file alpha iter lim

params

• document_word_file: input doc-word file. Each line is document.
  • format: document_name \t word \t word ...
• document_label_file: input doc-label file. Each line is document.
  • format: document_name \t label_1 \t label_2 ...
  • doc-word/doc-label file must same size.
• alpha: \alpha.
• iter: # of sampling iterations.
• lim: limit of outputs.

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