EDS 231: Text and Sentiment Analysis for Environmental Problems

Master’s of Environmental Data Science Program, UC Santa Barbara

Figure 1: Text and sentiment analysis for environmental problems

Course description

This course will cover foundations and applications of natural language processing. Problem sets and class projects will leverage common and emerging text-based data sources relevant to environmental problems and will build capacity and experience in common tools, including text processing and classification, semantics, and natural language parsing.

Instructor

Mateo Robbins (mjrobbins@ucsb.edu)

• Office: MEDS Group Office
• Office hours: Monday 1:15pm -2:15pm

Important links

• Link to full course syllabus

Weekly course schedule

• Lectures: NCEAS classroom

  M 12:00pm -1:15pm  

  W 11:00am - 12:15pm

Learning objectives

The goal of EDS 231 (Text and Sentiment Analysis for Environmental Problems) is to expose students to a range of text and sentiment analysis data sources, techniques and tools that can be applied to environmental problems. During this course, students will:

• Become familiar with the R packages used in text-as-data applications

• Conduct and explain each step in the text data collection, analysis, and presentation pipeline

Course requirements

Computing

• Minimum MEDS device requirements (bring to all sessions + charger!)

• Up-to-date R and RStudio

• Python version 3.x installed (although most, if not all, work will be done in RStudio)

Textbook

• Readings will be drawn from free online ebooks:
  • Text Mining with R (TMR)
  • Supervised Machine Learning for Text Analysis in R (SMLTAR)

Topics

Week	Session	Lecture/Demo	Reading	Assignment
1	4/01	Course Intro and Text Analysis Overview
	4/03	NYT Lab - Key	TMR 1.0-1.3, Appendix A	Lab 1
2	4/08	Sentiment Analysis I	TMR 2.0-2.7	In-class demo
	4/10	Sentiment Analysis I Lab - key		Lab 2
3	4/15	Topic Analysis Lecture	TMR 6.0-6.4
	4/17	Topic Analysis Lab - key		Lab 3
4	4/22	Classification Lecture	SMLTR 7.1-7.4	In-class demo
	4/24	Classification demo - key		Lab 4
5	4/29	Break
	5/01	Break
6	5/08	Word Embeddings Lecture	SMLTR 5.1-5.7
	5/10	Word Embeddings Lab demo - key		Lab 5