Novel Convex Polyhedron Classifier for Sentiment Analysis

Titre	Novel Convex Polyhedron Classifier for Sentiment Analysis
Publication Type	Conference Paper
Year of Publication	2020
Authors	S. Mrabtt, E, Lazaar, M, M. Achhab, A, Omara, H
Conference Name	Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020
Mots-clés	Artificial intelligence, Chi-Squared, Classification (of information), Cloud computing, Convex hull, Convex polyhedrons, F values, Feature selection methods, Geometric concepts, Geometry, Linearly separable, Piece-wise, Sentiment analysis
Abstract	In this paper, we propose a Novel Convex Polyhedron classifier (NCPC) based on the geometric concept convex hull. NCPC is basically a linear piecewise classifier (LPC). It partitions linearly non-separable data into various linearly separable subsets. For each of these subset of data, a linear hyperplane is used to classify them. We evaluate the performance of this classifier by combining it with two feature selection methods (Chi-squared and Anova F-value). Using two datasets, the results indicate that our proposed classifier outperforms other LPC-based classifiers. © 2020 IEEE.
URL	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102620927&doi=10.1109%2fCloudTech49835.2020.9365906&partnerID=40&md5=143fd1c523fe8ae24563a9b0482f632c
DOI	10.1109/CloudTech49835.2020.9365906