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Sentiment analysis (SA) is a study where people's opinions and emotions are automatically extracted in the form of sentiments from the natural language text. In social media monitoring, it is very useful because it allows user to gain an overall picture of the extensive public opinion behind many topics. Most works on SA are for the English text. Only a few works focus on the Malay language. Currently, a review on SA for the Malay language only focus on the SA approaches and the dataset. Some major issues such as the pre-processing techniques used to normalize the noisy text, the most employed performance measures for Malay SA, and the challenges for Malay SA has not been reviewed. Malaysians tend not to fully follow any abbreviations rules when writing on social media. Thus, a lot of noisy text can be found in social media sites like Facebook and Twitter which create some issues to SA process. Hence, the aim of this study is to investigate the state of the art, challenges and future works of SA for Malay social media text. This study provides a review on various approaches, datasets, performance measures, and pre-processing techniques used in the previous works on SA of the Malay text. More than 700 articles from journals and conference proceedings have been identified using the search keywords, however, only 17 relevant articles published from year 2013 to 2018 were reviewed. The findings from this review focus on three commonly used SA approaches which are lexicon-based, machine learning, and hybrid.

Sentiment Analysis of Noisy Malay Text: State of Art, Challenges and Future Work