Novel Convex Polyhedron Classifier for Sentiment Analysis

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.

• Publication year

  2020

• Venue

  International Conference on Cloud Computing Technologies and Applications

• Publication date

  2020-11-24

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/CloudTech49835.2020.9365906
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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