Computer Science > Computation and Language
arXiv:2604.26230 (cs)
COVID-19 e-print
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[Submitted on 29 Apr 2026]
Abstract:I developed a new version of Latent Semantic Scaling (LSS) employing word2vec as a masked language model. Unlike original spatial models, it assigns polarity scores to words and documents as predicted probabilities of seed words to occur in given contexts. These probabilistic polarity scores are more accurate, interpretable and consistent than those spatial polarity models can produce in text analysis. I demonstrate these advantages by applying both probabilistic and spatial models to China Daily's coverage of China and other countries during the coronavirus disease (COVID) pandemic in terms of achievement in health issues. The result suggests that more advanced masked language models would further improve the semisupervised machine learning technique.
| Subjects: | Computation and Language (cs.CL); Methodology (stat.ME) |
| Cite as: | arXiv:2604.26230 [cs.CL] |
| (or arXiv:2604.26230v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26230 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kohei Watanabe [view email]
[v1]
Wed, 29 Apr 2026 02:17:38 UTC (1,015 KB)
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