Research Article Open Access

Opinion Extraction on Online Malay Text

Surendran Selvaraju1 and Kalaiarasi Sonai Muthu Anbananthen1
  • 1 Faculty of Information Science Technology, Multimedia University, Malacca, 75450, Malaysia


Growing of social media usage present a new set of opportunities and challenges in the way of information is retrieved and searched. Opinions on social media has become an important factor in influencing people choices on purchasing a product and service. Hence, sentiment analysis has become the most crucial tool in tracking people feedbacks on products and services. For Malay language there is limited sources available for this language. Thus, in this paper we present the method of extracting opinion on online Malay text. The traditional method using POS extraction is not adequate. Thus, rule based method is integrated with POS extraction method to improve opinion words extraction. Most of the existing tools are able to retrieve opinion at sentence and document level. More detail analysis is acquired to have detail information and summarization of a product. This is where feature level sentiment analysis is needed. The process of identifying opinion of a particular feature in a sentence, can be quite tedious and troublesome. This is because opinion of the feature can be hidden and scattered in the sentence. Therefore, opinion mapping is employed for opinion extraction at feature level in this paper. A set of tweets from telecommunication domain is used to evaluate the proposed framework. From the experiment, the accuracy of the extraction performed is 88%. The detail description of the feature level opinion extraction steps is discussed in this paper.

American Journal of Applied Sciences
Volume 16 No. 4, 2019, 134-142


Submitted On: 22 October 2018 Published On: 12 June 2019

How to Cite: Selvaraju, S. & Muthu Anbananthen, K. S. (2019). Opinion Extraction on Online Malay Text. American Journal of Applied Sciences, 16(4), 134-142.

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  • Sentiment Analysis
  • Opinion Word
  • Malay Online Text
  • Feature Level Extraction