Emotion Analysis on Text Using Multiple Kernel Gaussian...

Abstract

The ability to discern human emotions is critical for making chatbox behave like humans. Gaussian Process (GP) is a non-parametric Bayesian modeling and can be used to predict the presence of either a single emotion (single-task GP) or multiple emotions (multi-task GP) in natural language text. Employing multiple kernels in GP can enhance the performance of the emotion analysis tasks. The particular choice of kernel functions determines the properties such as smoothness, length scales, sharpness, and amplitude, drawn from the GP prior. Using a specific kernel may be a source of bias and can be avoided by using different kernels together. The default kernel used with GP is a Radial Basis Function (RBF). It is infinitely differentiable; GP with this function has mean square derivatives of all orders and is thus very smooth. The sharpness which occurs in the midst of the smoothness can be detected using the exponential kernel. The multi-layer perceptron kernel has greater generalization for each training example and is good for extrapolation. Our experiments show that, for learning the presence of a single emotion in a natural language sentence (single-task), multiple kernel GP with the sum of RBF and multi-layer perceptron kernels performs better than single kernel GP. Likewise, for learning the presence of several different emotions in a sentence (multi-task), multiple kernel GP with the sum of RBF, exponential and multi-layer perceptron kernels performs better than single kernel GP. Multiple Kernel Gaussian Process also outperforms Convolutional Neural Network (CNN).