# Sentiment analysis with genetically evolved Gaussian kernels

## TL;DR

This paper introduces a genetic programming approach to evolve Gaussian Process kernels tailored for sentiment analysis, improving accuracy over traditional kernels by optimizing structure and hyperparameters simultaneously.

## Contribution

It presents a novel method that uses genetic programming to adapt Gaussian Process kernels specifically for sentiment analysis tasks, enhancing performance.

## Key findings

- Evolved kernels outperform traditional Gaussian Process kernels in some sentiment analysis tasks.
- Multi-objective optimization balances accuracy and computational efficiency.
- The approach demonstrates the potential of kernel evolution for domain-specific Gaussian Process models.

## Abstract

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.