Improving sentiment analysis through ensemble learning of meta-level features

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Alnashwan, Rana
O'Riordan, Adrian P.
Sorensen, Humphrey
Hoare, Cathal
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Sun SITE Central Europe (CEUR) / RWTH Aachen University
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In this research, the well-known microblogging site, Twitter, was used for a sentiment analysis investigation. We propose an ensemble learning approach based on the meta-level features of seven existing lexicon resources for automated polarity sentiment classification. The ensemble employs four base learners (a Two-Class Support Vector Machine, a Two-Class Bayes Point Machine, a Two-Class Logistic Regression and a Two-Class Decision Forest) for the classification task. Three different labelled Twitter datasets were used to evaluate the effectiveness of this approach to sentiment analysis. Our experiment shows that, based on a combination of existing lexicon resources, the ensemble learners minimize the error rate by avoiding poor selection from stand-alone classifiers.
Opinion mining , Sentiment analysis , Lexicon , Machine learning , Twitter
Alnashwan, R., O’Riordan, A., Sorensen, H. and Hoare, C. (2016) ‘Improving sentiment analysis through ensemble learning of meta-level features’ (from the Proceedings of the 2nd International Workshop on Knowledge Discovery on the WEB, Cagliari, Italy, 8 – 10 September), CEUR Workshop Proceedings, 1748.
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