Probabilistic Topic Modeling Algorithm

Context:
- It can produce a Probabilistic Topic Model.
- It can be a Word-level Analysis Algorithm.
Counter-Example(s):
- a Document Topic Clustering Algorithm.
See: Manual Topic Modeling Process.

References

(Blei, 2008) ⇒ David M. Blei. (2008). "Modeling Science." Presentation. April 17, 2008
(Mimno & McCallum, 2008) ⇒ David Mimno, and Andrew McCallum. (2008). "Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression." In: Proceedings of UAI 2008.
- Text documents are usually accompanied by metadata, such as the authors, the publication venue, the date, and any references. Work in topic modeling that has taken such information into account, such as Author-Topic, Citation-Topic, and Topic-over-Time models, has generally focused on constructing specific models that are suited only for one particular type of metadata. This paper presents a simple, unified model for learning topics from documents given arbitrary non-textual features, which can be discrete, categorical, or continuous.

(Blei & Lafferty) ⇒ David M. Blei, and John D. Lafferty. (2006). "Dynamic Topic Models." In: Proceedings of the 23rd International Conference on Machine Learning ([[ICML] 2006).
(Wallach, 2006) ⇒ Hanna M. Wallach. (2006). "Topic modeling: beyond bag-of-words." In: Proceedings of the 23rd ICML Conference (ICML 2006) doi:10.1145/1143844.1143967
(Newman & al, 2006) ⇒ David Newman, Chaitanya Chemudugunta, and Padhraic Smyth. (2006). "Statistical Entity-Topic Models." In: Proceedings of SIGKDD-2006.

(Griffiths & Steyvers, 2004) ⇒ Thomas L. Griffiths, and Mark Steyvers. (2004). "Finding Scientific Topics." In: PNAS, 101(Suppl. 1). doi:10.1073/pnas.0307752101
- ... We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics.