Content Analysis and Natural Language Processing

About

Workshop contents and objectives

This workshop provides participants with a practical hands-on and theoretical understanding of new methods in deriving quantitative data from natural language. This approach asks how we can extend qualitative content analysis to the available digital tools, of which there are many. Moreover, we also ask how existing tools can help us ask new qualitative and quantitative questions when we are dealing with text as data. This approach scales from words to documents to large text corpora.

Some of the issues this approach addresses include the following:

Understanding the speech of political leaders.
Detecting historical and cultural change in well-being, views towards immigrants, trust, etc.
Predicting views of brands: What does it mean to be a luxury brand? What associations do people have with different products?
Using language to predict personality or changes across an individual’s lifespan: How did the writing of Darwin, Mozart, and Van Gogh change across their lifespan?
The course will begin by providing participants with an understanding of what natural language processing offers content analysis. Automation can allow interesting content questions to be answered in very short periods of time (sometimes minutes), saving weeks or months of research time. It can also introduce new questions that lead to innovative research programs.

Each day will present published research and then unpack the methodology to show how the research was done, providing code and data for students to replicate these approaches with their own data, including analysis and visualization of results.

On completion of the course, participants will be able to recognize and implement many common approaches to content analysis using natural language processing. Students will also take the first steps towards formulating and addressing problems of their own. Participants will also be provided with detailed information about how to follow up and learn more with respect to their area of interest.

This workshop is good for experienced researchers and early researchers alike, as much of it takes a hands-on approach where you can use tools to formulate and address your own research questions.

Workshop design

The course will alternate between lectures and interactive programming using pre-written code in R.

Detailed lecture plan (daily schedule)

Day 1: Intro to content analysis and natural language processing, off the shelf tools and the art of the simple approach

Day 2: Word features (sentiment analysis and feature analysis)

Day 3: Word and document semantics and similarity (categorizing documents and words)

Day 4: Topics (what are my documents about and how can I organize them?)

Day 5: Advanced topics and short presentations from students

Class materials

All materials including code, readings, and slides will be provided online
**The Summer School cannot grant credits. We only deliver a Certificate of Participation, i.e. we certify your attendance.**

If you consider using Summer School workshops to obtain credits (ECTS), you will have to investigate at your home institution (contact the person/institute responsible for your degree) to find out whether they recognise the Summer School, how many credits can be earned from a workshop/course with roughly 35 hours of teaching, no graded work, and no exams.

Make sure to investigate this matter before registering if this is important to you.

Course leader

Thomas Hills is currently the Director of the Behavioural and Data Science MSc and the Bridges Doctoral Training Centre in Mathematical and Social Sciences (University of Warwick).

Target group

doctoral researchers, early career researchers, experienced researchers

Prerequisites

Students taking this workshop should have some experience with R and RStudio. There are free or inexpensive online courses (e.g., Datacamp) that offer introductory courses in R that are sufficient prerequisites for this course. A general introductory book to statistics in R will also work (e.g., Dalgaard, P. 2008. Introductory statistics with R is where I started). Though the course will primarily use R, I will provide all the code. Therefore, this course can be a way to improve your R skills as well.

Fee info