The recent influx in generation, storage, and availability of textual data presents researchers
with the challenge of develo** suitable methods for their analysis. Latent Semantic …

Narratives are fundamental to our understanding of the world, providing us with a natural
structure for knowledge representation over time. Computational narrative extraction is a …

How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

Crowdsourcing is a process of acquisition, integration, and analysis of big and
heterogeneous data generated by a diversity of sources in urban spaces, such as sensors …

In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a
novel probabilistic model to capture the mixture of topics and sentiments simultaneously …

Researchers in software engineering have attempted to improve software development by
mining and analyzing software repositories. Since the majority of the software engineering …

This paper presents Online Topic Model (OLDA), a topic model that automatically captures
the thematic patterns and identifies emerging topics of text streams and their changes over …

Social networks generate a large amount of text content over time because of continuous
interaction between participants. The mining of such social streams is more challenging than …

Multinomial distributions over words are frequently used to model topics in text collections. A
common, major challenge in applying all such topic models to any text mining problem is to …