Given a corpus of documents, focused analysis aims to find topics relevant to aspects that a user is interested in. The aspects are often expressed by a set of keywords provided by the user. Short texts such as microblogs and tweets pose several challenges to this task because the sparsity of word co-occurrences may hinder the extraction of meaningful and relevant topics. Moreover, most of the existing topic models perform a full corpus analysis that treats all topics equally, which may make the learned topics not be on target. In this paper, we propose a novel targeted topic model for semantic short-text embedding which aims to learn all topics and low-dimensional visual representations of documents, while preserving relevant topics for focused analysis of short texts. To preserve the relevant topics in the visualization space, we propose jointly modeling topics and the pairwise document ranking based on document-keyword distances in the visualization space. The extensive experiments on several real-world datasets demonstrate the effectiveness of our proposed model in terms of targeted topic modeling and visualization.

Visualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from text, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative model to jointly find topics and visualization, allowing the semantics to be infused in the visualization space for a meaningful interpretation. A major challenge that prevents these methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black box, it can handle model changes efficiently with little mathematical rederivation effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines.