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  • 标题:Integrating a Lexicon Based Approach and K Nearest Neighbour for Malay Sentiment Analysis
  • 本地全文:下载
  • 作者:Ahmed Alsaffar ; Nazlia Omar
  • 期刊名称:Journal of Computer Science
  • 印刷版ISSN:1549-3636
  • 出版年度:2015
  • 卷号:11
  • 期号:4
  • 页码:639-644
  • DOI:10.3844/jcssp.2015.639.644
  • 出版社:Science Publications
  • 摘要:Sentiment analysis or opinion mining refers to the automatic extraction of sentiments from a natural language text. Although many studies focusing on sentiment analysis have been conducted, there remains a limited amount of studies that focus on sentiment analysis in the Malay language. In this article, a new approach for automatic sentiment analysis of Malay movie reviews is proposed, implemented and evaluated. In contrast to most studies that focus on supervised or unsupervised machine learning approaches, this research aims to propose a new model for Malay sentiment analysis based on a combination of both approaches. We used sentiment lexicons in the new model to generate a new set of features to train a k-Nearest Neighbour (k-NN) classifier. We further illustrated that our hybrid method outperforms the state of-the-art unigram baseline.
  • 关键词:Malay Sentiment Analysis; Feature Extraction; Machine Learning; Combinations Techniques
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