We consider the use of Bayesian topic models in the analysis of computer network traffic. Our approach utilizes latent Dirichlet allocation and time-varying dynamic latent Dirich...
Abstract--The automatic discovery of group conversational behavior is a relevant problem in social computing. In this paper, we present an approach to address this problem by defin...
We propose a new paradigm for displaying comments: showing comments alongside parts of the article they correspond to. We evaluate the effectiveness of various approaches for thi...
Dyut Kumar Sil, Srinivasan H. Sengamedu, Chiranjib...
This paper presents a user recommendation system that recommends to a user new friends having similar interests. We automatically discover users’ interests using Latent Dirichle...
This paper presents the novel task of best topic word selection, that is the selection of the topic word that is the best label for a given topic, as a means of enhancing the inte...
Jey Han Lau, David Newman, Sarvnaz Karimi, Timothy...
The latent topic model plays an important role in the unsupervised learning from a corpus, which provides a probabilistic interpretation of the corpus in terms of the latent topic...
Recently there has been considerable interest in topic models based on the bag-of-features representation of images. The strong independence assumption inherent in the bag-of-feat...
In this paper we propose a new approach to capture the inclination towards a certain election candidate from the contents of blogs and to explain why that inclination may be so. T...
Discovering and summarizing opinions from online reviews is an important and challenging task. A commonly-adopted framework generates structured review summaries with aspects and ...
Wayne Xin Zhao, Jing Jiang, Hongfei Yan, Xiaoming ...
This paper establishes a connection between two apparently very different kinds of probabilistic models. Latent Dirichlet Allocation (LDA) models are used as "topic models&qu...