Multimodal topic model for texts and images utilizing their embeddings

Topic Modeling over Short Texts by Incorporating Word Embeddings

Inferring topics from the overwhelming amount of short texts becomes a critical but challenging task for many content analysis tasks, such as content charactering, user interest profiling, and emerging topic detecting. Existing methods such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA) cannot solve this problem very well since only very limited word co-o...

Visual Images Interpretive Strategies in Multimodal Texts

A wide range of semiotic resources has been used to construct meaning. Visual systems of meaning offer different resources and potentials for meaning making. Readers need to familiarize themselves with a variety of ways to make sense and read visual images. This paper, drawing on the theories of semiotics, art and visual communication grammar, presents a framework of interpretive strategies to ...

Expanding Perspectives for Comprehending Visual Images in Multimodal Texts

Topic Sentiment Joint Model with Word Embeddings

Topic sentiment joint model is an extended model which aims to deal with the problem of detecting sentiments and topics simultaneously from online reviews. Most of existing topic sentiment joint modeling algorithms infer resulting distributions from the co-occurrence of words. But when the training corpus is short and small, the resulting distributions might be not very satisfying. In this pape...

A Correlated Topic Model Using Word Embeddings

Conventional correlated topic models are able to capture correlation structure among latent topics by replacing the Dirichlet prior with the logistic normal distribution. Word embeddings have been proven to be able to capture semantic regularities in language. Therefore, the semantic relatedness and correlations between words can be directly calculated in the word embedding space, for example, ...