Document Type

Article

Source of Publication

Egyptian Informatics Journal

Publication Date

1-1-2021

Abstract

Background/introduction: Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. Methods: This work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an extended set of linguistic rules for concept-feature pair extraction along with enhanced set ontological relations. Additionally, a supervised a machine learning technique is implemented for performing concept-level sentiment analysis. Results and conclusions: Experimental results depict the effectiveness of the proposed system in terms of improved efficiency (P: 88%, R: 88%, F-score: 88%, and A: 87.5%).

ISSN

1110-8665

Publisher

Elsevier BV

Disciplines

Computer Sciences

Keywords

Concept lattice, Formal concept analysis (FCA), Machine learning techniques, Ontological relations, Support vector machine

Scopus ID

85103702907

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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