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Human emotion detection based on questionnaire and text analysis

Human emotions have been described by some theorists as discrete and consistent responses to internal or external events which have a particular significance for the organism. Emotions are the topic of extensive research in the recent times. However, state of the art describes that most of the approaches on emotion detection have been designed on the basis of complex and costly approaches like physiological features, brain signals, etc. The present article presents a simple and cost-effective emotion detection model by combining questionnaire and text analysis-based approaches and then combining the probability scores of two different classifiers (support vector machine and artificial neural network) using Dempster-Shafer theory (DST). In the present work, DST has been used effectively in combining multiple information sources which provides incomplete, imprecise, and biased knowledge. Experimental results show that the proposed system outperforms all existing emotion detection systems available in the literature.