  Sentiment analysis consists of evaluating opinions or statements from the
analysis of text. Among the methods used to estimate the degree in which a text
expresses a given sentiment, are those based on Gaussian Processes. However,
traditional Gaussian Processes methods use a predefined kernel with
hyperparameters that can be tuned but whose structure can not be adapted. In
this paper, we propose the application of Genetic Programming for evolving
Gaussian Process kernels that are more precise for sentiment analysis. We use
use a very flexible representation of kernels combined with a multi-objective
approach that simultaneously considers two quality metrics and the
computational time spent by the kernels. Our results show that the algorithm
can outperform Gaussian Processes with traditional kernels for some of the
sentiment analysis tasks considered.
