Text Mining with R

Chapter 8. Case Study: Mining NASA Metadata

There are over 32,000 datasets hosted and/or maintained by NASA; these datasets cover topics from Earth science to aerospace engineering to management of NASA itself. We can use the metadata for these datasets to understand the connections between them.

Note

What is metadata? Metadata is a term that refers to data that gives information about other data; in this case, the metadata informs users about what is in these numerous NASA datasets but does not include the content of the datasets themselves.

The metadata includes information like the title of the dataset, a description field, what organization(s) within NASA is responsible for the dataset, keywords for the dataset that have been assigned by a human being, and so forth. NASA places a high priority on making its data open and accessible, even requiring all NASA-funded research to be openly accessible online. The metadata for all its datasets is publicly available online in JSON format.

In this chapter, we will treat the NASA metadata as a text dataset and show how to implement several tidy text approaches with this real-life text. We will use word co-occurrences and correlations, tf-idf, and topic modeling to explore the connections between the datasets. Can we find datasets that are related to each other? Can we find clusters of similar datasets? Since we have several text fields in the NASA metadata, most importantly the title, description, and keyword fields, we can explore ...