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, Erik Cambria
2018 PLoS ONE  
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
more » ... e 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. OPEN ACCESS Citation: Al-Saffar A, Awang S, Tao H, Omar N, Al-Saiagh W, Al-bared M (2018) Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm. PLoS ONE 13(4): e0194852. https://doi.org/10.
doi:10.1371/journal.pone.0194852 pmid:29684036 pmcid:PMC5912726 fatcat:hjyygmdjv5bjdpdjnt6ibckrcm