The Journal of Machine Learning Research
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
Statistical entity-topic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering users' topics of interest on twitter: a first look
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Comparing twitter and traditional media using topic models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Learning semantic relationships between entities in twitter
ICWE'11 Proceedings of the 11th international conference on Web engineering
Transferring topical knowledge from auxiliary long texts for short text clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Named entity recognition in tweets: an experimental study
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
G-WSTD: a framework for geographic web search topic discovery
Proceedings of the 21st ACM international conference on Information and knowledge management
Panorama: a semantic-aware application search framework
Proceedings of the 16th International Conference on Extending Database Technology
Limosa: a system for geographic user interest analysis in Twitter
Proceedings of the 16th International Conference on Extending Database Technology
Stochastic variational inference
The Journal of Machine Learning Research
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Microblogging platforms, such as Twitter, already play an important role in cultural, social and political events around the world. Discovering high-level topics from social streams is therefore important for many downstream applications. However, traditional text mining methods that rely on the bag-of-words model are insufficient to uncover the rich semantics and temporal aspects of topics in Twitter. In particular, topics in Twitter are inherently dynamic and often focus on specific entities, such as people or organizations. In this paper, we therefore propose a method for mining multifaceted topics from Twitter streams. The Multi-Faceted Topic Model (MfTM) is proposed to jointly model latent semantics among terms and entities and captures the temporal characteristics of each topic. We develop an efficient online inference method for MfTM, which enables our model to be applied to large-scale and streaming data. Our experimental evaluation shows the effectiveness and efficiency of our model compared with state-of-the-art baselines. We further demonstrate the effectiveness of our framework in the context of tweet clustering.