# Overview

Due by 9:30am (Chicago) on December 7th.

# Fork the hw09 repository

Go here to fork the repo.

Perform text analysis.

Perform sentiment analysis, classification, or topic modeling using text analysis methods as demonstrated in class and in the readings.

## Okay, I need some data sources

Some suggested text data you could use include:

## How much do I really need to do?

Analyze the text for sentiment OR topic. Or build a statistical learning model using text features to predict some outcome of interest. You don’t have to do all these things, just pick one. The lecture notes and Tidy Text Mining with R are good starting points for templates to perform this type of analysis, but feel free to expand beyond these examples.

# Submit the assignment

Your assignment should be submitted as an R Markdown document using the github_document format. Whatever is necessary to show your code and present your results. Follow instructions on homework workflow. As part of the pull request, you’re encouraged to reflect on what was hard/easy, problems you solved, helpful tutorials you read, etc.

# Rubric

Needs improvement: Cannot get code to run or is poorly documented. Severe misinterpretations of the results. No effort is made to pre-process the text for analysis.1

Satisfactory: Solid effort. Hits all the elements. No clear mistakes. Easy to follow (both the code and the output). Nothing spectacular, either bad or good.

Excellent: Interpretation is clear and in-depth. Accurately interprets the results, with appropriate caveats for what the technique can and cannot do. Code is reproducible (i.e. if analyzing tweets, you have stored a copy in a local file so I can exactly reproduce your results as well as run it on a new sample of tweets). Uses a sentiment analysis or topic model approach not directly covered in class.

1. Or you provide no justification for keeping content such as numbers, stop words, etc. ^