Automatically generating a sentiment lexicon for the Malay language
This paper aims to propose an automated sentiment lexicon generation model specifically designed for the Malay language. Lexicon-based Sentiment Analysis (SA) models make use of a sentiment lexicon for SA tasks, which is a linguistic resource that comprises a priori information about the sentiment...
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Penerbit Universiti Kebangsaan Malaysia
2016
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| Online Access: | http://journalarticle.ukm.my/10056/ http://journalarticle.ukm.my/10056/ http://journalarticle.ukm.my/10056/1/11736-37831-1-PB.pdf |
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ukm-100562017-01-25T09:53:35Z http://journalarticle.ukm.my/10056/ Automatically generating a sentiment lexicon for the Malay language Mohammad Darwich, Shahrul Azman Mohd Noah, Nazlia Omar, This paper aims to propose an automated sentiment lexicon generation model specifically designed for the Malay language. Lexicon-based Sentiment Analysis (SA) models make use of a sentiment lexicon for SA tasks, which is a linguistic resource that comprises a priori information about the sentiment properties of words. A sentiment lexicon is an indispensable resource for SA tasks. This is evident in the emergence of a large volume of research focused on the development of sentiment lexicon generation algorithms. This is not the case for low-resource languages such as Malay, for which there is a lack of research focused on this particular area. This has brought up the motivation to propose a sentiment lexicon generation algorithm for this language. WordNet Bahasa was first mapped onto the English WordNet to construct a multilingual word network. A seed set of prototypical positive and negative terms was then automatically expanded by recursively adding terms linked via WordNet’s synonymy and antonymy semantic relations. The underlying intuition is that the sentiment properties of newly added terms via these relations are preserved. A supervised classifier was employed for the word-polarity tagging task, with textual representations of the expanded seed set as features. Evaluation of the model against the General Inquirer lexicon as a benchmark demonstrates that it performs with reasonable accuracy. This paper aims to provide a foundation for further research for the Malay language in this area. Penerbit Universiti Kebangsaan Malaysia 2016-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/10056/1/11736-37831-1-PB.pdf Mohammad Darwich, and Shahrul Azman Mohd Noah, and Nazlia Omar, (2016) Automatically generating a sentiment lexicon for the Malay language. Asia-Pacific Journal of Information Technology and Multimedia, 5 (1). pp. 49-59. ISSN 2289-2192 http://ejournals.ukm.my/apjitm/issue/view/709 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Kebangasaan Malaysia |
| building |
UKM Institutional Repository |
| collection |
Online Access |
| language |
English |
| description |
This paper aims to propose an automated sentiment lexicon generation model specifically designed for the Malay
language. Lexicon-based Sentiment Analysis (SA) models make use of a sentiment lexicon for SA tasks, which is
a linguistic resource that comprises a priori information about the sentiment properties of words. A sentiment
lexicon is an indispensable resource for SA tasks. This is evident in the emergence of a large volume of research
focused on the development of sentiment lexicon generation algorithms. This is not the case for low-resource
languages such as Malay, for which there is a lack of research focused on this particular area. This has brought up
the motivation to propose a sentiment lexicon generation algorithm for this language. WordNet Bahasa was first
mapped onto the English WordNet to construct a multilingual word network. A seed set of prototypical positive
and negative terms was then automatically expanded by recursively adding terms linked via WordNet’s synonymy
and antonymy semantic relations. The underlying intuition is that the sentiment properties of newly added terms
via these relations are preserved. A supervised classifier was employed for the word-polarity tagging task, with
textual representations of the expanded seed set as features. Evaluation of the model against the General Inquirer
lexicon as a benchmark demonstrates that it performs with reasonable accuracy. This paper aims to provide a
foundation for further research for the Malay language in this area. |
| format |
Article |
| author |
Mohammad Darwich, Shahrul Azman Mohd Noah, Nazlia Omar, |
| spellingShingle |
Mohammad Darwich, Shahrul Azman Mohd Noah, Nazlia Omar, Automatically generating a sentiment lexicon for the Malay language |
| author_facet |
Mohammad Darwich, Shahrul Azman Mohd Noah, Nazlia Omar, |
| author_sort |
Mohammad Darwich, |
| title |
Automatically generating a sentiment lexicon for the Malay language |
| title_short |
Automatically generating a sentiment lexicon for the Malay language |
| title_full |
Automatically generating a sentiment lexicon for the Malay language |
| title_fullStr |
Automatically generating a sentiment lexicon for the Malay language |
| title_full_unstemmed |
Automatically generating a sentiment lexicon for the Malay language |
| title_sort |
automatically generating a sentiment lexicon for the malay language |
| publisher |
Penerbit Universiti Kebangsaan Malaysia |
| publishDate |
2016 |
| url |
http://journalarticle.ukm.my/10056/ http://journalarticle.ukm.my/10056/ http://journalarticle.ukm.my/10056/1/11736-37831-1-PB.pdf |
| first_indexed |
2023-09-18T19:56:23Z |
| last_indexed |
2023-09-18T19:56:23Z |
| _version_ |
1777406556957048832 |