Human Emotion Classification Using Knn classifier and Recurrent Neural Networks With Seed dataset

Abstract

Emotions which can be commonly called to be as human feelings are variable and numerous. They vary according to the situation or according to perception. Analyzing and classifying those emotions are very crucial in current situations. For example, for knowing the review of the product, the developer can use this emotion detection to see whether the client is satisfied with the product and can understand the likeliness of the product. Accordingly, he can vary it, and in health care for finding the depression in a person. So this makes the classification of human feelings more vulnerable. Here initially the data is being collected from the brain via EEG Signals and it is fed into a mock dataset and then we can extract these EEG Signal features by using Knn Classifier to Classify the data but To improve several parameters like time of execution and accuracy this seed data can be classified using the RNN(recurrent neural networks). For a small dataset, K nearest neighbor may work efficiently but for large datasets and more classifications, a Recurrent neural network is more efficient. Here when a small seed dataset is being considered, It produces good accuracy and classification of the data. Computing using this process produces the Best accuracy of 96.22% by the Knn classifier and Test accuracy of 85.71% by Recurrent Neural Networks.