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Found 2 papers, 1 papers with code
Semantics derived automatically from language corpora contain human-like biases
Here we show for the first time that human-like semantic biases result from the application of standard machine learning to ordinary language---the same sort of language humans are exposed to every day.
Measuring Cultural Relativity of Emotional Valence and Arousal using Semantic Clustering and Twitter
We demonstrate that in our sample, the valence and arousal levels of the same emotion keywords differ significantly with respect to these geographical regions --- Europeans are, or at least present themselves as more positive and aroused, North Americans are more negative and Asians appear to be more positive but less aroused when compared to global valence and arousal levels of the same emotion keywords.
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