Abstract
The genesis of the internet and web has created huge information on the web, including users’ digital or textual opinions and reviews. This leads to compiling many features in document-level. Consequently, we will have a high-dimensional feature space. In this paper, we propose an algorithm based on standard deviation method to solve the high-dimensional feature space. The algorithm constructs feature subsets based on dispersion of features. In other words, algorithm selects the features with higher value of standard deviation for construction of the subsets. To do this, the paper presents an experiment of performance estimation on sentiment analysis dataset using ensemble of classifiers when dimensionality reduction is performed on the input space using three different methods. Also different types of base classifiers and classifier combination rules were used.
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Yousefpour, A., Ibrahim, R., Abdull Hamed, H.N., Hajmohammadi, M.S. (2014). Feature Reduction Using Standard Deviation with Different Subsets Selection in Sentiment Analysis. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_4
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DOI: https://doi.org/10.1007/978-3-319-05458-2_4
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