We are not able to deal with a mammoth text corpus without summarizing them into a
relatively small subset. A computational tool is extremely needed to understand such a …

This paper proposes a technique to leverage topic based sentiments from Twitter to help
predict the stock market. We first utilize a continuous Dirichlet Process Mixture model to …

News recommenders help users to find relevant online content and have the potential to
fulfill a crucial role in a democratic society, directing the scarce attention of citizens towards …

Rapid advancements in internet and social media technologies have made “information
overload” a rampant and widespread problem. Complex subjects, histories, or issues break …

We propose a rolling version of the Latent Dirichlet Allocation, called RollingLDA. By a
sequential approach, it enables the construction of LDA-based time series of topics that are …

We investigate whether the ideological polarisation of citizens has increased in Western
democracies. We propose a novel methodology to identify individual ideologies by applying …

Narratives are fundamental to our perception of the world and are pervasive in all activities
that involve the representation of events in time. Yet, modern online information systems do …

Over the past five years, topic models have been applied to bibliometrics research as an
efficient tool for discovering latent and potentially useful content. The combination of topic …

Previous approaches to the problem of measuring similarity between automatically
generated topics have been based on comparison of the topics' word probability …

Timely and accurate information disclosure is an important step towards open and
transparent governance. However, the decision on what to disclose at what time is a difficult …