Sabino Miranda-Jiménez

Semantic Genetic Programming for Sentiment Analysis

Sentiment analysis is one of the most important tasks in text mining. This field has a high impact for government and private companies to support major decision-making policies. Even though Genetic Programming (GP) has been widely used to solve real world problems, GP is seldom used to tackle this trendy problem. This contribution starts rectifying this research gap by proposing a novel GP system, namely, Root Genetic Programming, and extending our previous genetic operators based on projections on the phenotype space. The results show that these systems are able to tackle this problem being competitive with other state-of-the-art classifiers, and, also, give insight to approach large scale problems represented on high dimensional spaces.

An automated text categorization framework based on hyperparameter optimization

A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning problem, whereas others are specifically designed to tackle a particular task, using complex and computational expensive processes such as lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we propose a minimalistic and wide system able to tackle text classification tasks independent of domain and language, namely microTC. It is composed by some easy to implement text transformations, text representations, and a supervised learning algorithm. These pieces produce a competitive classifier even in the domain of informally written text. We provide a detailed description of microTC along with an extensive experimental comparison with relevant state-of-the-art methods. mircoTC was compared on 30 different datasets. Regarding accuracy, microTC obtained the best performance in 20 datasets while achieves competitive results in the remaining 10. The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution. Furthermore, it is important to state that our approach allows the usage of the technology even without knowledge of machine learning and natural language processing.

EvoDAG: A semantic Genetic Programming Python library

Genetic Programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard real-world problems. Lately, there has been considerable interest in GPs community to develop semantic genetic operators, i.e., operators that work on the phenotype. In this contribution, we describe EvoDAG (Evolving Directed Acyclic Graph) which is a Python library that implements a steady-state semantic Genetic Programming with tournament selection using an extension of our previous crossover operators based on orthogonal projections in the phenotype space. To show the effectiveness of EvoDAG, it is compared against state-of-the-art classifiers on different benchmark problems, experimental results indicate that EvoDAG is very competitive.