@inproceedings{2b3cd29602b6473f9276872e43e523ca,
title = "Online topic evolution modeling based on hierarchical dirichlet process",
abstract = "This paper presents a model based on Hierarchical Dirichlet Process (HDP), that automatically captures the evolutionary thematic patterns in texts. Our approach allows HDP to work in an online fashion, such that it can build an up-todate model for new documents given the old model, without accessing historic data. Since exact calculation is infeasible, we turn to Gibbs sampling to carry out approximate posterior inference. After the topics are found, we can analyze the evolution relationships between time-adjacent topics. Experiments on a real world dataset (Reuters-21578) validate the effectiveness of the model quantitatively, showing its advantage over both OLDA and plain HDP in modeling topic evolution.",
author = "Tao Ma and Dacheng Qu and Rui Ma and Wei Feng and Kan Li",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 1st IEEE International Conference on Data Science in Cyberspace, DSC 2016 ; Conference date: 13-06-2016 Through 16-06-2016",
year = "2017",
month = feb,
day = "27",
doi = "10.1109/DSC.2016.60",
language = "English",
series = "Proceedings - 2016 IEEE 1st International Conference on Data Science in Cyberspace, DSC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "400--405",
booktitle = "Proceedings - 2016 IEEE 1st International Conference on Data Science in Cyberspace, DSC 2016",
address = "United States",
}