The Journal of Machine Learning Research
Topic Detection from Blog Documents Using Users' Interests
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Information Retrieval
Introduction to Information Retrieval
Fast collapsed gibbs sampling for latent dirichlet allocation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Detect and track latent factors with online nonnegative matrix factorization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
Journal of the ACM (JACM)
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Keep It Simple with Time: A Reexamination of Probabilistic Topic Detection Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic evaluation of topic coherence
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Trend analysis model: trend consists of temporal words, topics, and timestamps
Proceedings of the fourth ACM international conference on Web search and data mining
Peaks and persistence: modeling the shape of microblog conversations
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Emerging topic detection using dictionary learning
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the fifth ACM international conference on Web search and data mining
Dynamical classes of collective attention in twitter
Proceedings of the 21st international conference on World Wide Web
TM-LDA: efficient online modeling of latent topic transitions in social media
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Discovering and tracking topic shifts in news constitutes a new challenge for applications nowadays. Topics evolve,emerge and fade, making it more difficult for the journalist -or the press consumer- to decrypt the news. For instance, the current Syrian chemical crisis has been the starting point of the UN Russian initiative and also the revival of the US France alliance. A topical mapping representing how the topics evolve in time would be helpful to contextualize information. As far as we know, few topic tracking systems can provide such temporal topic connections. In this paper, we introduce a novel framework inspired from Collective Factorization for online topic discovery able to connect topics between different time-slots. The framework learns jointly the topics evolution and their time dependencies. It offers the user the ability to control, through one unique hyper-parameter, the tradeoff between the past accumulated knowledge and the current observed data. We show, on semi-synthetic datasets and on Yahoo News articles, that our method is competitive with state-of-the-art techniques while providing a simple way to monitor topics evolution (including emerging and disappearing topics).