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

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

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.

Suggested Citation

  • Ahmed Al-Saffar & Suryanti Awang & Hai Tao & Nazlia Omar & Wafaa Al-Saiagh & Mohammed Al-bared, 2018. "Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-18, April.
    • Handle: RePEc:plo:pone00:0194852
    DOI: 10.1371/journal.pone.0194852
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    References listed on IDEAS

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    1. Xinmiao Li & Jing Li & Yukeng Wu, 2015. "A Global Optimization Approach to Multi-Polarity Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-18, April.
    2. Muhammad Zubair Asghar & Aurangzeb Khan & Shakeel Ahmad & Imran Ali Khan & Fazal Masud Kundi, 2015. "A Unified Framework for Creating Domain Dependent Polarity Lexicons from User Generated Reviews," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
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    Cited by:

    1. Manal Mohammed & Nazlia Omar, 2020. "Question classification based on Bloom’s taxonomy cognitive domain using modified TF-IDF and word2vec," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.

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