A scoping review of topic modelling on online data

• Published in: Indonesian Journal of Electrical Engineering and Computer Science
• Main Author: Sharif M.M.M.; Maskat R.; Baharum Z.; Maskat K.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171730967&doi=10.11591%2fijeecs.v31.i3.pp1633-1641&partnerID=40&md5=c4531f3b7fa588e66414209db105ef8f

With the increasing prevalence of unstructured online data generated (e.g., social media, online forums), mining them is important since they provide a genuine viewpoint of the public. Due to this significant advantage, topic modelling has become more important than ever. Topic modelling is a natural language processing (NLP) technique that mainly reveals relevant topics hidden in text corpora. This paper aims to review recent research trends in topic modelling and state-of-the-art techniques used when dealing with online data. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) methodology was used in this scoping review. This study was conducted on recent research works published from 2020 to 2022. We constructed 5 research questions for the interest of many researchers. 36 relevant papers revealed that more work on non-English languages is needed, common pre-processing techniques were applied to all datasets regardless of language e.g., stop word removal; latent dirichlet allocation (LDA) is the most used modelling technique and also one of the best performing; and the produced result is most evaluated using topic coherence. In conclusion, topic modelling has largely benefited from LDA, thus, it is interesting to see if this trend continues in the future across languages. © 2023 Institute of Advanced Engineering and Science. All rights reserved.