A probabilistic polarity scoring method for Latent Semantic Scaling uses masked language models to compute seed-word occurrence probabilities, claimed to outperform spatial models in accuracy, interpretability, and consistency.
In this comparison, I also included mini dictionaries that only comprise the seed words to highlight the contribution of the LSS models
1 Pith paper cite this work. Polarity classification is still indexing.
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A New Semisupervised Technique for Polarity Analysis using Masked Language Models
A probabilistic polarity scoring method for Latent Semantic Scaling uses masked language models to compute seed-word occurrence probabilities, claimed to outperform spatial models in accuracy, interpretability, and consistency.