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Malay due to the main resources for generating this Malaysentiment lexicon was only basedon formal Malay
and does not include the informal Malay or slang.As far that we can see, user-generated data is greatly grown
day by day and need to be analyzed.In future work, the enhancement of the proposed method will be explored
to cater to the needs for generating sentiment bearable words from other classes of POS which are nouns,
adverbs, and verbs. On top of that, an improved version of the algorithm that will make use of the corpus-based
approach will be also considered so that it can fulfill the need for lexical resources on social media data. It is
hoped that more significant coverage of the sentiment lexicon may enhance the quality of the sentiment analysis
task.

Acknowledgement

The work was supported by Ministry of Higher Education for SLAB Scholars and the Universiti Kebangsaan
Malaysia (GUP-2018-058).

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