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.
LIWC contains keywords about ‘health’ (294 words) and ‘achievement’ (213 words), which were manually collected for social and psychological research.9
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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.