Sentiment classification of Arabic documents : experiments with multi-type features and ensemble algorithms

Bayoudhi, Amine (ANLP Group, MIRACL FSEGS, Sfax University, Sfax, Tunisia) ; Hadrich Belguith, Lamia (ANLP Group, MIRACL FSEGS, Sfax University, Sfax, Tunisia) ; Ghorbel, Hatem (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland)

Document sentiment classification is often processed by applying machine learning techniques, in particular supervised learning which consists basically of two major steps: feature extraction and training the learning model. In the literature, most existing researches rely on n-grams as selected features, and on a simple basic classifier as learning model. In the context of our work, we try to improve document classification findings in Arabic sentiment analysis by combining different types of features such as opinion and discourse features; and by proposing an ensemble-based classifier to investigate its contribution in Arabic sentiment classification. Obtained results attained 85.06% in terms of macro-averaged Fmeasure, and showed that discourse features have moderately improved Fmeasure by approximately 3% or 4%.


Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HE-Arc Ingénierie
Institute:
Aucun institut
Subject(s):
Ingénierie
Publisher:
Shanghai, China, 30 October - 1 November 2015
Date:
2015-11
Shanghai, China
30 October - 1 November 2015
Pagination:
10 p.
Published in:
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation, 30 October - 1 November 2015, Shanghai, China
External resources:
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 Record created 2019-05-28, last modified 2019-06-11

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