This Bren School of Environmental Science & Management Master of Environmental Data Science course, EDS231 Text and Sentiment Analysis for Environmental Problems, covers the 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 but not limited to social media feeds (e.g., Twitter) and text documents (e.g., agency reports), and will build capacity and experience in common tools, including text processing and classification, semantics, and natural language parsing.
Professor: Mateo Robbins
The goal of EDS 231 Text and Sentiment Analysis is to expose students to a range of text analysis and natural language processing data sources, techniques and tools for analysis 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
- Evaluate examples of text analysis in the environmental science literature
- Work with peers on a group text analysis project, then communicate the analysis to the rest of the class
| jsonlite | ggplot2 | tidytext |
| tidyverse | tidyr | LexisNexisTools |
| sentimentr | readr | pdftools |
| quanteda | lubridate | wordcloud |
| reshape2 | here | rtweet |
| paletteer | kableExtra | sentimentr |