Sequential Modeling of Topic Dynamics with Multiple Timescales

Authors:
Tomoharu Iwata;Takeshi Yamada;Yasushi Sakurai;Naonori Ueda
Affiliations:
NTT Communication Science Laboratories;NTT Science and Core Technology Laboratory Group;NTT Communication Science Laboratories;NTT Communication Science Laboratories
Venue:
ACM Transactions on Knowledge Discovery from Data (TKDD)
Year:
2012

Citing 16
Cited 0

Collaborative filtering via gaussian probabilistic latent semantic analysis

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Latent dirichlet allocation

The Journal of Machine Learning Research
BRAID: stream mining through group lag correlations

Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Streaming pattern discovery in multiple time-series

VLDB '05 Proceedings of the 31st international conference on Very large data bases
Dynamic topic models

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
Multiscale topic tomography

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive, hands-off stream mining

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
The dynamic hierarchical Dirichlet process

Proceedings of the 25th international conference on Machine learning
Probabilistic latent semantic visualization: topic model for visualizing documents

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking

ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Dynamic mixture models for multiple time series

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Topic tracking model for analyzing consumer purchase behavior

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic tracking language model for speech recognition

Computer Speech and Language
Probabilistic latent semantic analysis

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, some words may be used consistently over one hundred years, while other words emerge and disappear over periods of a few days. Thus, in the proposed model, current topic-specific distributions over words are assumed to be generated based on the multiscale word distributions of the previous epoch. Considering both the long- and short-timescale dependency yields a more robust model. We derive efficient online inference procedures based on a stochastic EM algorithm, in which the model is sequentially updated using newly obtained data; this means that past data are not required to make the inference. We demonstrate the effectiveness of the proposed method in terms of predictive performance and computational efficiency by examining collections of real documents with timestamps.