TOPIC MODELLING FOR IDENTIFYING TOPICS FROM MALAYSIAN BUSINESS NEWS WEBSITES.
| dc.contributor.author | VIANNEY CAMELIE ANAK KERBUN | |
| dc.date.accessioned | 2026-04-27T02:28:06Z | |
| dc.date.issued | 2025 | |
| dc.description | This research focuses on the topic modelling for identifying topics from Malaysian business news websites. In the digital age, businesses and organizations face challenges due to the large amount of information, particularly within the business news sector. The vast volume of data complicates the process of identifying meaningful insights, making it difficult for professionals to stay informed. This research investigates the Natural Language Processing (NLP) techniques, specifically topic modelling, to address this challenge. Topic modelling is a statistical method that helps identify key themes within large datasets, making it easier to summarize and extract valuable information. The study focuses on Malaysian business news websites, applying techniques such as Non-negative Matrix Factorization (NMF), Latent Dirichlet Allocation (LDA), Top2Vec, and BERTopic. Furthermore, a web-based application is developed to offer an interactive platform for users to explore the identified topics. The application illustrates the potential of topic modelling in enhancing decision-making and business intelligence by summarizing key topics within extensive datasets. | |
| dc.identifier.uri | https://scholarhub.unimas.my/handle/123456789/487 | |
| dc.language.iso | English | |
| dc.publisher | UNIVERSITI MALAYSIA SARAWAK | |
| dc.relation.ispartofseries | Faculty of Computer Science and Information Technology | |
| dc.subject | Topic Modelling, Natural Language Processing (NLP), Malaysian Business News | |
| dc.title | TOPIC MODELLING FOR IDENTIFYING TOPICS FROM MALAYSIAN BUSINESS NEWS WEBSITES. | |
| dc.type | Final Year Project |