We describe a hierarchical topic model. We assume that there are various levels of specificity in a document collection. For example, a collection of mailing list posts might be organized according to sentence, paragraph, post and thread. We describe a model that captures the structure at each level of the hierarchy. We use a trace norm penalty on a matrix composed of natural parameters for the...

This study proposes the bilingual segmented topic model (BiSTM), which hierarchically models documents by treating each document as a set of segments, e.g., sections. While previous bilingual topic models, such as bilingual latent Dirichlet allocation (BiLDA) (Mimno et al., 2009; Ni et al., 2009), consider only cross-lingual alignments between entire documents, the proposed model considers cros...

In a document, the topic distribution of a sentence depends on both the topics of preceding sentences and its own content, and it is usually affected by the topics of the preceding sentences with different weights. It is natural that a document can be treated as a sequence of sentences. Most existing works for Bayesian document modeling do not take these points into consideration. To fill this ...

Faceted topic models combine topical content with extraneous facets, such as ideology or dialect. In this model, the “pure” topics are corrupted by the facets, using a hierarchical generative model in which the pure topics act as priors on the faceted topics. This is most easily modeled using the logistic-normal distribution, which admits a normal prior on the mean. 1 Model We build on latent D...

Various topic models have been developed for sentiment analysis tasks. But the simple topic-sentiment mixture assumption prohibits them from finding fine-grained dependency between topical aspects and sentiments. In this paper, we build a Hidden Topic Sentiment Model (HTSM) to explicitly capture topic coherence and sentiment consistency in an opinionated text document to accurately extract late...