Topic modeling includes a variety of machine learning techniques for identifying latent
themes in a corpus of documents. Generating an exact solution (ie, finding global optimum) …

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 …

Topic models based on latent Dirichlet allocation and related methods are used in a range
of user-focused tasks including document navigation and trend analysis, but evaluation of …

Study objective Topic identification can facilitate knowledge curation, discover thematic
relationships, trends, and predict future direction. We aimed to determine through an …

Tweets are the most up-to-date and inclusive stream of in-formation and commentary on
current events, but they are also fragmented and noisy, motivating the need for systems that …

This paper introduces distributional semantic similarity methods for automatically measuring
the coherence of a set of words generated by a topic model. We construct a semantic space …

Topic models are a useful and ubiquitous tool for understanding large corpora. However,
topic models are not perfect, and for many users in computational social science, digital …

Topic models are statistical models for learning the latent structure in document collections,
and have gained much attention in the machine learning community over the last decade …

Recommender systems suggest to users items that they might like (eg, news articles, songs,
movies) and, in doing so, they help users deal with information overload and enjoy a …

While most topic modeling algorithms model text corpora with unigrams, human
interpretation often relies on inherent grouping of terms into phrases. As such, we consider …