Sentiment Classification And Personality Detection Via Galvanic Skin Response Based on Deep Learning Models

Sentiment and personality have great impact on our lives; they affect our cognition, creativity, and decision making. Many approaches have been proposed to automatically recognize users' sentiment through images and speech. Accurately predicting a person's sentiment and personality traits can be beneficial for certain situations, such as interviews and polygraphs. In this work, several models including Convolutional Neural Network-Long Short-Term Memory Joint Learning Model, and spatiotemporal hybrid models are proposed to automatically learn galvanic skin response (GSR) and video clips rating for the sentiments recognition and personality detection tasks. Comparisons are made between the proposed model and state-of-the-art models in related works. The experimental results demonstrate that better results of sentiment recognition and personality detection are gained in terms of precision, recall, and F1 score.

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