E2SAM: Evolutionary ensemble of sentiment analysis methods for domain adaptation.

Highlights • Domain adaptation is one of the main challenges of sentiment analysis. • Most polarity detection methods are focused on a specific domain. • Ensemble methods may improve the performance of a set of polarity detection methods. • Evolutionary algorithms may find out the right combination of polarity classification methods in an ensemble classifier. • The results show that our claim holds on 13 corpora. Abstract 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. [ABSTRACT FROM AUTHOR]