Abstract
Feature selection is an important problem for any pattern classification task. In this paper, we developed an ensemble of two Maximum Entropy classifiers for Twitter sentiment analysis: one for subjectivity and the other for polarity classification. Our ensemble employs surface-form, semantic and sentiment features. The classification complexity of this ensemble of linear models is linear with respect to the number of features. Our goal is to select a compact feature subset from the exhaustive list of extracted features in order to reduce the computational complexity without scarifying the classification accuracy. We evaluate the performance on two benchmark datasets, CrowdScale and SemEval. Our selected 20K features have shown very similar results in subjectivity classification to the NRC state-of-the-art system with 4 million features that has ranked first in 2013 SemEval competition. Also, our selected features have shown a relative performance gain in the ensemble classification over the baseline of uni-gram and bi-gram features of 9.9% on CrowdScale and 11.9% on SemEval.
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Mansour, R., Hady, M.F.A., Hosam, E., Amr, H., Ashour, A. (2015). Feature Selection for Twitter Sentiment Analysis: An Experimental Study. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_7
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DOI: https://doi.org/10.1007/978-3-319-18117-2_7
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