---
title: "Topic Modeling"
author: "Prof. Eric A. Suess"
date: "April 14, 2021"
output:
  beamer_presentation: default
  ioslides_presentation: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

## Topic Modeling

Unsupervised Learning

Clustering of documents

Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds natural groups of items even when we’re not sure what we’re looking for.

## Latent Dirichlet allocation (LDA)

- It treats each document as a mixture of topics, and each topic as a mixture of words. 

This allows documents to “overlap” each other in terms of content, rather than being separated into discrete groups, in a way that mirrors typical use of natural language.

## LDA

- Every document is a mixture of topics.
- Every topic is a mixture of words. 

## Example

The example in the book runs topic analysis on the Associated Press articles from around 1988.

The two topics found are *Financial News* and *Politics*.

## Document-topic probabilities

- gamma