LSS: Semi-supervised algorithm for document scaling

In quantitative text analysis, the cost of training supervised machine
learning models tend to be very high when the corpus is large. Latent
Semantic Scaling (LSS) is a semi-supervised document scaling technique
that I developed to perform large scale analysis at low cost. Taking
user-provided seed words as weak supervision, it estimates polarity of
words in the corpus by latent semantic analysis and locates documents on
a unidimensional scale (e.g. sentiment).

Installation

From CRAN:

install.packages("LSX")

From Github:

devtools::install_github("koheiw/LSX")

Examples

Please visit the package website to understand the usage of the
functions:

• Introduction to
  LSX
• Application in
  research
• Selection of seed
  words

Please read the following papers for the algorithm and methodology, and
its application to non-English texts (Japanese and Hebrew):

• Watanabe, Kohei. 2020. “Latent Semantic Scaling: A Semisupervised
  Text Analysis Technique for New Domains and
  Languages”,
  Communication Methods and Measures.
• Watanabe, Kohei, Segev, Elad, & Tago, Atsushi. (2022). “Discursive
  diversion: Manipulation of nuclear threats by the conservative leaders
  in Japan and
  Israel”,
  International Communication Gazette.

Other publications

LSS has been used for research in various fields of social science.

• Nakamura, Kentaro. 2022 Balancing Opportunities and Incentives: How
  Rising China’s Mediated Public Diplomacy Changes Under
  Crisis,
  International Journal of Communication.
• Zollinger, Delia. 2022 Cleavage Identities in Voters’ Own Words:
  Harnessing Open-Ended Survey
  Responses,
  American Journal of Political Science.
• Brändle, Verena K., and Olga Eisele. 2022. “A Thin Line: Governmental
  Border Communication in Times of European
  Crises”
  Journal of Common Market Studies.
• Umansky, Natalia. 2022. “Who gets a say in this? Speaking security on
  social
  media”.
  New Media & Society.
• Rauh, Christian, 2022. “Supranational emergency politics? What
  executives’ public crisis communication may tell
  us”,
  Journal of European Public Policy.
• Trubowitz, Peter and Watanabe, Kohei. 2021. “The Geopolitical Threat
  Index: A Text-Based Computational Approach to Identifying Foreign
  Threats”,
  International Studies Quarterly.
• Vydra, Simon and Kantorowicz, Jaroslaw. 2020. “Tracing
  Policy-relevant Information in Social Media: The Case of Twitter
  before and during the COVID-19
  Crisis”.
  Statistics, Politics and Policy.
• Watanabe, Kohei. 2017. “Measuring News Bias: Russia’s Official News
  Agency ITAR-TASS’s Coverage of the Ukraine
  Crisis”,
  European Journal Communication.

More publications are available on Google
Scholar.