Semantic genetic programming for sentiment analysis. (English) Zbl 1354.68223

Schütze, Oliver (ed.) et al., NEO 2015. Results of the numerical and evolutionary optimization workshop NEO 2015 held at September 23–25 2015 in Tijuana, Mexico. Cham: Springer (ISBN 978-3-319-44002-6/hbk; 978-3-319-44003-3/ebook). Studies in Computational Intelligence 663, 43-65 (2017).
Summary: 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.
For the entire collection see [Zbl 1355.90002].


68T05 Learning and adaptive systems in artificial intelligence
62H30 Classification and discrimination; cluster analysis (statistical aspects)
68T10 Pattern recognition, speech recognition


Full Text: DOI


This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.