This work presents our attempt to understand the research topics that characterize the papers submitted to a conference, by using topic modeling and data visualization techniques. We infer the latent topics from the abstracts of all the papers submitted to Interspeech2014 by means of Latent Dirichlet Allocation. Pertopic word distributions thus obtained are visualized through word clouds. We al...

Topic Modeling refers to a suit of algorithms that gives us an insight of the ‘latent’ semantic topics or themes in a collection of documents. This survey provides a brief classification of different topic modeling techniques and an introductory overview of the most popular topic modeling technique LDA (latent Dirichlet Allocation) and some of its extensions. This survey also summarizes few app...

As the popularity of micro-blogging increases, managing friends and followers and their tweets is becoming increasingly complex. In this project, we explore the usage of topic models in understanding both text and links in micro-blogs. On a data set of 21306 users, we find that LDA can find good topics that seem to capture meaningful topics of discussion in twitter. We also find that knowing wh...

Recently there has been significant activity in developing algorithms with provable guarantees for topic modeling. In this work we consider a broad generalization of the traditional topic modeling framework, where we no longer assume that words are drawn i.i.d. and instead view a topic as a complex distribution over sequences of paragraphs. Since one could not hope to even represent such a dist...

There are numerous news articles coming to news aggregators and important news are selected to be presented on the front-page. There are two types of news selection for the front-page of news aggregators: personalized and public news recommendation (selection). This study examines public news recommendation that aims to satisfy all users’ interest on the front-page. Public news recommendation i...

This paper describes the application of topic modeling techniques to quarterly earnings call transcripts of publicly traded companies. Earnings call transcripts represent an interesting case for analysis because the document is relatively unstructured and potentially more informative than 10K and 10Q disclosures due to the question and answer session consisting of unprepared statements. This pa...

Topic modeling with a tree-based prior has been used for a variety of applications because it can encode correlations between words that traditional topic modeling cannot. However, its expressive power comes at the cost of more complicated inference. We extend the SPARSELDA (Yao et al., 2009) inference scheme for latent Dirichlet allocation (LDA) to tree-based topic models. This sampling scheme...

In this paper, we propose a new chronological modeling of topics latent in documents. We apply sparse additive generative models (SAGE) [5] in a manner so that we diversify topic modeling results chronologically by using document timestamps. We call our approach ChronoSAGE. SAGE can represent each word probability by exponential of the sum of multiple parameters representing various facets of d...

Topic models are widely used to thematically describe a collection of text documents and have become an important technique for systems that measure document similarity for classification, clustering, segmentation, entity linking and more. While they have been applied to some non-text domains, their use for semi-structured graph data, such as RDF, has been less explored. We present a framework ...