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, including policy documents, public comments, scientific literature, and social media. Methods covered include text processing, sentiment analysis, text classification and topic modeling.
Instructor
Mateo Robbins (mjrobbins@ucsb.edu)
- Office: Bren Hall 2027
- Office hours: Tuesdays, by appointment
Important links
Weekly course schedule
Class meets: Bren 1510
Tuesdays 12:30pm -1:45pm
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 common software packages used in text-as-data applications
Conduct and explain each step in the text data collection, analysis, and presentation pipeline
Evaluate examples of text analysis in the environmental science literature
Course requirements
Computing
Minimum MEDS device requirements
Up-to-date R and RStudio
Textbook
- Readings will be drawn from free online ebooks:
Topics
| Week | Topic |
|---|---|
| 1 | Course Intro |
| 2 | Text Preprocessing and Cleaning |
| 3 | Exploratory Text Analysis and Visualization |
| 4 | Text Representation I |
| 5 | Text Representation II |
| 6 | Sentiment Analysis I |
| 7 | Sentiment Analysis II |
| 8 | Classification |
| 9 | TBD |
| 10 | Group presentations |
