There are four main text data sets we use throughout this book to demonstrate building features for machine learning and training models. These data sets include texts of different languages, different lengths (short to long), and from very recent time periods to a few hundred years ago.
These text data sets are not overly difficult to read into memory and prepare for analysis; by contrast, in many text modeling projects, the data itself may be in any of a number of formats from an API to literal paper. Practitioners may need to use skills such as web scraping or connecting to databases to even begin their work.
B.1 Hans Christian Andersen fairy tales
The hcandersenr (Hvitfeldt 2019a) package includes the text of the 157 known fairy tales by the Danish author Hans Christian Andersen (1805 - 1875). There are five different languages available, with:
156 fairy tales in English,
154 in Spanish,
150 in German,
138 in Danish, and
58 in French.
The package contains a data set for each language with the naming convention
** is a country code.
Each data set comes as a dataframe with two columns,
book where the
book variable has the text divided into strings of up to 80 characters.
The package also makes available a data set called
EK which includes information about the publication date, language of origin, and names in the different languages.
B.2 Opinions of the Supreme Court of the United States
The scotus (Hvitfeldt 2019b) package contains a sample of the Supreme Court of the United States’ opinions.
scotus_sample dataframe includes one opinion per row along with the year, case name, docket number, and a unique ID number.
The text has had minimal preprocessing and includes header information in the text field, such as shown here:
#> No. 97-1992 #> VAUGHN L. MURPHY, Petitioner v. UNITED PARCEL SERVICE, INC. #> ON WRIT OF CERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE TENTH #> CIRCUIT #> [June 22, 1999] #> Justice O'Connor delivered the opinion of the Court. #> Respondent United Parcel Service, Inc. (UPS), dismissed petitioner Vaughn #> L. Murphy from his job as a UPS mechanic because of his high blood pressure. #> Petitioner filed suit under Title I of the Americans with Disabilities Act of #> 1990 (ADA or Act), 104 Stat. 328, 42 U.S.C. § 12101 et seq., in Federal District #> Court. The District Court granted summary judgment to respondent, and the Court #> of Appeals for the Tenth Circuit affirmed. We must decide whether the Court #> of Appeals correctly considered petitioner in his medicated state when it held #> that petitioner's impairment does not "substantially limi[t]" one or more of #> his major life activities and whether it correctly determined that petitioner #> is not "regarded as disabled." See §12102(2). In light of our decision in Sutton #> v. United Air Lines, Inc., ante, p. ____, we conclude that the Court of Appeals' #> resolution of both issues was correct.
B.3 Consumer Financial Protection Bureau (CFPB) complaints
Consumers can submit complaints to the United States Consumer Financial Protection Bureau (CFPB) about financial products and services; the CFPB sends the complaints to companies for response.
The data set of consumer complaints used in this book has been filtered to 117,214 complaints submitted to the CFPB after 1 January 2019 that include a consumer complaint narrative (i.e., some submitted text). Each observation has a
complaint_id, various categorical variables, and a text column
consumer_complaint_narrative containing the written complaints, for a total of 18 columns.
B.4 Kickstarter campaign blurbs
The crowdfunding site Kickstarter provides people a platform to gather pledges to “back” their projects, such as films, music, comics, journalism, and more. When setting up a campaign, project owners submit a description or “blurb” for their campaign to tell potential backers what it is about. The data set of campaign blurbs used in this book was scraped from Kickstarter; the blurbs used here for modeling are from 2009-04-21 to 2016-03-14, with a total of 269,790 campaigns in the sample. For each campaign, we know its
state, whether it was successful in meeting its crowdfunding goal or not.