The surge of opinionated on-line texts provides a wealth of information that can be exploited to analyze users’ viewpoints and opinions on various topics. This article presents VODUM, an unsupervised Topic Model designed to jointly discover viewpoints, topics, and opinions in text. We hypothesize that partitioning topical words and viewpointspecific opinion words using part-of-speech helps to d...

This study proposes topic model to cluster a group of high school teenager's Instagram account in Surabaya, Indonesia by using the author-topic models method. We collect valid 235 accounts (133 female, 102 male students). gather total 3,346 captions posts from 18 senior schools. find topics that define their Instagram's post or caption; these seven are namely: feeling, Surabaya events, photogra...

Documents from the same domain usually discuss similar topics in a similar order. However, the number of topics and the exact topics discussed in each individual document can vary. In this paper we present a simple topic model that uses generalised Mallows models and incomplete topic orderings to incorporate this ordering regularity into the probabilistic generative process of the new model. We...

Traditional works in sentiment analysis and aspect rating prediction do not take author preferences and writing style into account during rating prediction of reviews. In this work, we introduce Joint Author Sentiment Topic Model (JAST), a generative process of writing a review by an author. Authors have different topic preferences, ‘emotional’ attachment to topics, writing style based on the d...