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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "LSX" in publications use:'
type: software
license: GPL-3.0-only
title: 'LSX: Semi-Supervised Algorithm for Document Scaling'
version: 1.4.1
doi: 10.1080/19312458.2020.1832976
identifiers:
- type: doi
  value: 10.32614/CRAN.package.LSX
abstract: A word embeddings-based semi-supervised model for document scaling Watanabe
  (2020) <https://doi.org/10.1080/19312458.2020.1832976>. LSS allows users to analyze
  large and complex corpora on arbitrary dimensions with seed words exploiting efficiency
  of word embeddings (SVD, Glove). It can generate word vectors on a users-provided
  corpus or incorporate a pre-trained word vectors.
authors:
- family-names: Watanabe
  given-names: Kohei
  email: watanabe.kohei@gmail.com
preferred-citation:
  type: article
  title: 'Latent Semantic Scaling: A Semisupervised Text Analysis Technique for New
    Domains and Languages'
  authors:
  - family-names: Watanabe
    given-names: Kohei
    email: watanabe.kohei@gmail.com
  journal: Communication Methods and Measures
  year: '2020'
  volume: '15'
  issue: '2'
  doi: 10.1080/19312458.2020.1832976
  start: 81-102
repository: https://koheiw.r-universe.dev
repository-code: https://github.com/koheiw/LSX
commit: 1a06b33dd840ea23c5bc25f4fbb1ac5bd37c6f2e
url: https://koheiw.github.io/LSX/
contact:
- family-names: Watanabe
  given-names: Kohei
  email: watanabe.kohei@gmail.com