MELex: The Construction of Malay-English Sentiment Lexicon  

作　　者：Nurul Husna Mahadzir Mohd Faizal Omar Mohd Nasrun Mohd Nawi Anas ASalameh Kasmaruddin Che Hussin Abid Sohail 

机构地区：[1]Faculty of Computer and Mathematical Sciences,Universiti Teknologi MARA(UiTM)Kedah,08400,Merbok,Kedah,Malaysia [2]Department of Decision Sciences,School of Quantitative Sciences,Universiti Utara Malaysia,06010,Kedah,Malaysia [3]Disaster Management of Institute,School of Technology Management and Logistic,Universiti Utara Malaysia,06010,Kedah,Malaysia [4]Department of Management Information System,College of Business Administration,Prince Sattam Bin Abdulaziz University,165,Al-Kharj,Saudi Arabia [5]Faculty of Entrepreneurship and Business,Universiti Malaysia Kelantan,Malaysia [6]Department of Computer Science,COMSATS University Islamabad,Lahore,Pakistan

出　　处：《Computers, Materials & Continua》2022年第4期1789-1805,共17页计算机、材料和连续体（英文）

摘　　要：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 that MELex 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 in Malaysia 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.

关 键 词：Machine learning data sciences artificial intelligence opinion mining sentiment analysis sentiment lexicon lexicon-based bilingual lexicon 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]