LSS: Semi-supervised algorithm for document scaling

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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.