---
title: "Review Stat. 653"
author: "Prof. Eric A. Suess"
date: "April 28, 2021"
output:
  beamer_presentation: default
  ioslides_presentation: default
---

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

## Review

What have we learned about studying Text Mining?

Lot of new things about how to work with text data.

## Review

- Bag-of-Words Model
- Tokenizers
- Corpus and Document Term Matrix
- Stemming
- Word Counts
- TF-IDF
- Sentiment Analysis
- n-grams
- Pairwise Correlation of words
- Topic Modeling
- Parts of Speech

## Review

All of these ideas are as useful as computing means, standard deviation, correlations, t-tests, regression, etc. for numeric data.

You are now prepared to work with the other half of the data that is out there in the world!

## Review

We have studied Unsupervised Learning techniques for text based data.

**Sentiment Analysis** is very useful for learning about the sentiment in documents.  

## Review

We have studied Unsupervised Learning techniques for *clustering* text based data.

**Topic Analysis** is very useful for learning about the different topics discussed in documents.

## Review

We have studied Supervised Learning techniques for *classifying* text based data.

**Naive Bayes** and **Logistic Regression with lasso/regularization** are very useful for predicting which class documents are in.

## Review

There are a lot of other R packages that can be used for Text Mining.

- Rvest
- Quanteda
- Text2vec
- Spacy
- Rtweet

## Review

There are a lot of Python packages that can be used for Text Mining.

- NLTK
- Textblob
- SciKit Learn
- Beautiful soup
- Gensim
- Spacy
- CoreNLP
- Pattern
- Polyglot
- Twint

## Review

There are growing opportunties to work doing Text Mining.  This is a very interesting new field to work in and there are a growing number of excellent tools available to pursue such work.