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Open Access Publications from the University of California

Emotion Evaluator: Expanding the Affective Lexicon with Neural Network Model


Measuring the emotion in words is valuable in that it analyzes emotions through language. However, it is difficult to find such measurements in low-resource languages. In this paper, we proposed a method to expand the affective lexicon by utilizing the context of words. The proposed model predicted the Valence and Arousal values of words using their dictionary definitions. In Study 1, we reviewed previous studies about the Korean affective lexicon and integrated data from these studies. The model was trained to minimize the MSE error between the Valence and Arousal values of the words and their predictions. We then checked the distribution of Valence and Arousal values of Korean vocabulary by applying our model to the Korean dictionary. In Study 2, a new affective lexicon was built to empirically validate our model. We found a negatively biased error pattern on model predictions and discussed why it happened.

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