Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm

Ahmed Al-Saffar, Suryanti Awang, Hai Tao, Nazlia Omar, Wafaa Al-Saiagh, Mohammed Al-bared

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach.

Original languageEnglish
Article numbere0194852
JournalPLoS One
Volume13
Issue number4
DOIs
Publication statusPublished - 1 Apr 2018

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artificial intelligence
Learning systems
Classifiers
Population Groups
Semantics
Feature extraction
Websites
Agglomeration
Maintenance
methodology
Experiments
Supervised Machine Learning
Machine Learning

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm. / Al-Saffar, Ahmed; Awang, Suryanti; Tao, Hai; Omar, Nazlia; Al-Saiagh, Wafaa; Al-bared, Mohammed.

In: PLoS One, Vol. 13, No. 4, e0194852, 01.04.2018.

Research output: Contribution to journalArticle

Al-Saffar, Ahmed ; Awang, Suryanti ; Tao, Hai ; Omar, Nazlia ; Al-Saiagh, Wafaa ; Al-bared, Mohammed. / Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm. In: PLoS One. 2018 ; Vol. 13, No. 4.
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