Integrating a Lexicon based approach and K nearest neighbour for Malay sentiment analysis

Ahmed Alsaffar, Nazlia Omar

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)639-644
Number of pages6
JournalJournal of Computer Science
Volume11
Issue number4
DOIs
Publication statusPublished - 2015

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Learning systems
Classifiers

Keywords

  • Combinations techniques
  • Feature extraction
  • Machine learning
  • Malay sentiment analysis

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

Integrating a Lexicon based approach and K nearest neighbour for Malay sentiment analysis. / Alsaffar, Ahmed; Omar, Nazlia.

In: Journal of Computer Science, Vol. 11, No. 4, 2015, p. 639-644.

Research output: Contribution to journalArticle

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