Topic Modeling
David Blei’s paper on the Topic Modeling caused quite a commotion. With the rise of its popularity, people applied such methodology with ease yet had not quite grasping the concept.[1] In one of Beli’s lectures, Topic Modeling shows a relationship to the Bayesian system but a ‘hierarchical Bayesian system.’ Essentially, Topic Modeling is selecting topics from a body of data, Beli’s mentioned ‘Wikipedia.’ Then by selecting a file you can connect the topics, which means ‘annotating’ the file with the use of algorithms to locate different topics.[2] This is equivalent to the classification process in data mining but by using a different method. Once located, there are different ways in which they can be presented. For example, seeing topics change over time can be depicted through a graph; similar to Ngram. To see connections or relations between topics, branching would assist with this. Finally, images can also be annotated by the algorithms or by gridding so it can be treated like a document. Latent Semantic Analysis (LSA) and Latent Dirichlit Allocation (LDA) are some of the forms of modeling and in association, classification in data mining. LDA helps associate word to a topic but the topic is not named.
[1] Topic Modeling and Network Analysis,http://www.scottbot.net/HIAL/?p=221; consulted 15 April 2012; consulted 18 April 2012
[2]Topic models, http://videolectures.net/mlss09uk_blei_tm/; consulted 18 April 2012