Sentiment analysis with genetically evolved Gaussian kernels
This work addresses sentiment analysis practitioners by offering an incremental improvement through automated kernel design.
The paper tackled the problem of improving sentiment analysis accuracy by evolving Gaussian Process kernels using Genetic Programming, achieving better performance than traditional kernels on some sentiment analysis tasks.
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.