The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, brought a new major challenge to government responses worldwide. Not only is creating confusion about medical science amongst citizens, but it also amplifying distrust in policy makers governments. To help tackle this, we developed computational methods categorise disinformation....

This paper deals with the problem of jointly mining topics, sentiments, and the association between them from online reviews in an unsupervised way. Previous methods often treat a sentiment as a special topic and assume a word is generated from a flat mixture of topics, where the discriminative performance of sentiment analysis is not satisfied. A key reason is that providing rich priors on the...

With the increasing number of published Web services providing similar functionalities, it’s very tedious for a service consumer to make decision to select the appropriate one according to her/his needs. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent...