Sentiment analysis with genetically evolved Gaussian kernels
Sentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a prede- fined kernels with hyperparameters that can be tuned but whose structure can not be adapted. In this paper, we propose the application of Genetic Programming for the evolution of 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 considers si- multaneously two quality metrics and the computational time required to evaluate those kernels. Our results show that the algorithm can outper- form Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.