rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Learn about sentiment analysis with supervised learning in R with data from the Economic News Article Tone dataset (2016)
  name:
    name part: Shi, Feng
    name part: active 2019
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Odum Institute
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: enk
    date issued: 2019
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations
  abstract: This dataset is designed for teaching sentiment analysis of text data with supervised learning. The dataset is a subset of the 2016 Economic News Article Tone dataset, and the example investigates the change over time of sentiment on the U.S. Economy from the news articles. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for R.
  target audience: specialized
  note: Feng Shi and Odum Institute.
  note: Includes bibliographical references and index.
  subject:
    topic: Supervised learning (Machine learning)
  subject:
    topic: Emotive (Linguistics)
  subject:
    geographic: United States
    topic: Economic conditions
  subject:
    topic: Apprentissage supervisé (Intelligence artificielle)
  subject:
    topic: Sens affectif (Linguistique)
  subject:
    geographic: États-Unis
    topic: Conditions économiques
  subject:
    topic: Economic history
  subject:
    topic: Emotive (Linguistics)
  subject:
    topic: Supervised learning (Machine learning)
  subject:
    geographic: United States
  classification: Q325.75 .S55 2019
  classification: 006.31
  identifier: 9781526495686
  identifier: 1526495686
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: SGPBL
    record creation date: 190425
    record change date: 20240923170516.2
    record identifier: in00005496778
    language of cataloging:
      language term: eng