TY - JOUR
T1 - Melex
T2 - The construction of malay-english sentiment lexicon
AU - Mahadzir, Nurul Husna
AU - Omar, Mohd Faizal
AU - Nawi, Mohd Nasrun Mohd
AU - Salameh, Anas A.
AU - Hussin, Kasmaruddin Che
AU - Sohail, Abid
N1 - Publisher Copyright:
© 2022 Tech Science Press. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Currently, the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon. Thus, this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis. In this study, a new lexicon for sentiment analysis is constructed. A detailed review of existing approaches has been conducted, and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. Constructing MELex involves three activities: Seed words selection, polarity assignment, and synonym expansions.Our approach differs from previous works in thatMELex can analyze text for the two most widely used languages in Malaysia, Malay, and English, with the accuracy achieved, is 90%. It is evaluated based on the experimentation and case study approaches where the affordable housing projects inMalaysia are selected as case projects. This finding has given an implication on the ability of MELex to analyze public sentiments in the Malaysian context. The novel aspects of this paper are two-fold. Firstly, it introduces the new technique in assigning the polarity score, and second, it improves the performance over the classification of mixed language content.
AB - Currently, the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon. Thus, this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis. In this study, a new lexicon for sentiment analysis is constructed. A detailed review of existing approaches has been conducted, and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. Constructing MELex involves three activities: Seed words selection, polarity assignment, and synonym expansions.Our approach differs from previous works in thatMELex can analyze text for the two most widely used languages in Malaysia, Malay, and English, with the accuracy achieved, is 90%. It is evaluated based on the experimentation and case study approaches where the affordable housing projects inMalaysia are selected as case projects. This finding has given an implication on the ability of MELex to analyze public sentiments in the Malaysian context. The novel aspects of this paper are two-fold. Firstly, it introduces the new technique in assigning the polarity score, and second, it improves the performance over the classification of mixed language content.
KW - Artificial intelligence
KW - Bilingual lexicon
KW - Data sciences
KW - Lexicon-based
KW - Machine learning
KW - Opinion mining
KW - Sentiment analysis
KW - Sentiment lexicon
UR - http://www.scopus.com/inward/record.url?scp=85118626345&partnerID=8YFLogxK
U2 - 10.32604/cmc.2022.021131
DO - 10.32604/cmc.2022.021131
M3 - Article
AN - SCOPUS:85118626345
SN - 1546-2218
VL - 71
SP - 1789
EP - 1805
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 1
ER -