Sentiment analysis with genetically evolved gaussian kernels

  title={Sentiment analysis with genetically evolved gaussian kernels},
  author={Ibai Roman and Alexander Mendiburu and Roberto Santana and Jos{\'e} Antonio Lozano},
  journal={Proceedings of the Genetic and Evolutionary Computation Conference},
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 predefined 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… 

Figures and Tables from this paper

Evolution of Gaussian Process kernels for machine translation post-editing effort estimation

It is shown that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and, by using a multi-objective variant of the Genetic Programming approach, kernels that are suitable for predicting several metrics can be learned.

Boomerang-shaped neural embeddings for NK landscapes

This paper proposes to use neural embedding, that is a continuous vectorial representation obtained as a result of applying a neural network to a prediction task, in order to investigate the characteristics of NK landscapes and evaluates the performance of optimizers that solve the continuous representations of NK models by searching for solutions in the embedding space.

Evolvability degeneration in multi-objective genetic programming for symbolic regression

A new version of NSGA-II is extended to track, over time, the evolvability of models of different levels of complexity, and it is found that the over-replication of low complexity-models is due to a lack of evolVability, i.e., the inability to produce offspring with improved accuracy.

Optimización evolutiva de contextos para la corrección fonética en sistemas de reconocimiento del habla

The results show the viability of a genetic algorithm as a tool for context optimization, which added to a post-processing correction based on phonetic representations is able to reduce the errors on the recognized speech.

Evolutionary optimization of contexts for phonetic correction in speech recognition systems

The results show the viability of a genetic algorithm as a tool for context optimization, which, added to a post-processing correction based on phonetic representations, can reduce the errors on the recognized speech.

Semantic Composition of Word-Embeddings with Genetic Programming

  • R. Santana
  • Computer Science
    Heuristics for Optimization and Learning
  • 2020



Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions

A novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions that outperform other state-of-the-art approaches on commonly used datasets, without using any pre-defined sentiment lexica or polarity shifting rules.

Gaussian Processes for Text Regression

This thesis proposes new kernels for text which aim at capturing richer linguistic information in text, and proposes new architectures for GP-based regression which are able to obtain better results compared to baselines while also providing uncertainty estimates for predictions in the form of posterior distributions.

Opinion Mining and Sentiment Analysis

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.

Modelling Representation Noise in Emotion Analysis using Gaussian Processes

A model for Emotion Analysis using Gaussian Processes and kernels that are better suitable for functions that exhibit noisy behaviour is proposed and outperforms commonly used baselines for regression.

Learning Structural Kernels for Natural Language Processing

This paper shows how to perform model selection by maximizing the evidence on the training data through gradient-based methods in the context of structural kernels by using Gaussian Processes and shows that this procedure results in better prediction performance compared to hyperparameter optimization via grid search.

Enhancing Feature Selection Using Word Embeddings: The Case of Flu Surveillance

This paper uses neural word embeddings, trained on social media content from Twitter, to determine, in an unsupervised manner, how strongly textual features are semantically linked to an underlying health concept, and proposes a hybrid feature selection method that creates a more reliable model.

Learning Kernels over Strings using Gaussian Processes

This work derives a vectorised version of the string kernel algorithm and their gradients, allowing efficient hyperparameter optimisation as part of a Gaussian Process framework.

Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

This paper presents a novel approach for multi-lingual sentiment classification in short texts by leveraging large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrates the importance of using pre-training of such networks.

Evolving kernel functions for SVMs by genetic programming

Numerical experiments show that the SVM embedding the evolved kernel performs statistically better than standard kernels, but also than previous genetic kernel for all considered classification problems.

A temporal model of text periodicities using Gaussian Processes

Gaussian Processes, a state-ofthe-art bayesian non-parametric model, with a novel periodic kernel is used, which is used for regression in order to forecast the volume of a hashtag based on past data.