E2SAM: Evolutionary ensemble of sentiment analysis methods for domain adaptation Valdivia García, Ana Martínez Cámara, Eugenio Luzón García, María Victoria Herrera Triguero, Francisco Sentiment analysis Ensembles classifiers Genetic algorithms Currently, a plethora of industrial and academic sentiment analysis methods for classifying the opinion polarity of a text are available and ready to use. However, each of those methods have their strengths and weaknesses, due mainly to the approach followed in their design (supervised/unsupervised) or the domain of text used in their development. The weaknesses are usually related to the capacity of generalisation of machine learning algorithms, and the lexical coverage of linguistic resources. Those issues are two of the main causes of one of the challenges of Sentiment Analysis, namely the domain adaptation problem. We argue that the right ensemble of a set of heterogeneous Sentiment Analysis Methods will lessen the domain adaptation problem. Thus, we propose a new methodology for optimising the contribution of a set of off-the-shelf Sentiment Analysis Methods in an ensemble classifier depending on the domain of the input text. The results clearly show that our claim holds. 2025-01-16T09:49:03Z 2025-01-16T09:49:03Z 2019 journal article Miguel López, Ana Valdivia, Eugenio Martínez-Cámara, M. Victoria Luzón, Francisco Herrera, E2SAM: Evolutionary ensemble of sentiment analysis methods for domain adaptation, Information Sciences, Volume 480, 2019, Pages 273-286, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2018.12.038. https://hdl.handle.net/10481/99352 https://doi.org/10.1016/j.ins.2018.12.038 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier