Text Analysis Topic Modeling Text Analysis Topic Modeling

Text Analysis Topic Modeling

Text Analysis Topic Modeling Logical Process of Topic Modeling (simplified) Ted Underwood "Topic Modeling Made Just Simple Enough" (2012) LDA (Latent Dirichlet Allocation)

Text Analysis Topic Modeling “Bag of words”

Text Analysis Topic Modelingwords words words words words words

Text Analysis Topic Modeling 4 Humanities. org “What. Eery 1 Says” Project -Topic model of articles in New York Times mentioning “humanities”, 201014

Text Analysis Topic Modeling Tools Example run of Mallet on William Gibson’s Neuromancer

Text Analysis Topic Modeling Andrew Goldstone's interface (Dfr-Browser) for browsing topic models created from JSTOR journals Example: Topic model of PMLA, 1889– 2007

Text Analysis Topic Modeling Other Interfaces for Visualizing and Exploring Topic Models

Text Analysis Topic Modeling Tools Mallet v Java-based command-line tool Topic Modeling Tool v Java-based GUI implementation of MALLET (used locally on texts of user’s choice)

Text Analysis Topic Modeling Tools 90 -topic model of Neuromancer (created using Topic Modeling Tool) Example run of Mallet on William Gibson’s Neuromancer 90 -topic model of Crying of Lot 49 (created using Topic Modeling Tool)

Text Analysis Topic Modeling Matt Burton, "The Joy of Topic Modeling": “… the brown squiggles along the bottom represent a vocabulary of words and the grey peaks represent individual word’s probability density…. The list of top words, words that are “heavy” with more probabilistic mass, are the interesting group of words to examine because they are the co-occurring words in that topic distribution. ”

Text Analysis Topic Modeling A Probabilistic Universe

Text Analysis Topic Modeling A Probabilistic Universe Boris Tomashevsky’s example of a narrative motif (theme) (“Thematics, ” 1925): “Raskolnikov kills the old woman” Probablistic rewriting: “There is a 74% chance that in this document Raskolnikov kills (82%) / wounds (15%) / ignores (3%) the old woman (68%) / young woman (23%) / other (9%). ”