2011 TrackingTrendsIncorporatingTerm

• (Hong et al., 2011) ⇒ Liangjie Hong, Dawei Yin, Jian Guo, and Brian D. Davison. (2011). “Tracking Trends: Incorporating Term Volume Into Temporal Topic Models.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020485

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Tracking+Trends%3A+Incorporating+Term+Volume+Into+Temporal+Topic+Models
• http://dl.acm.org/citation.cfm?id=2020408.2020485&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; clustering; experimentation; miscellaneous; temporal dynamics; text mining; theory; topic models

Abstract

Text corpora with documents from a range of time epochs are natural and ubiquitous in many fields, such as research papers, newspaper articles and a variety of types of recently emerged social media. People not only would like to know what kind of topics can be found from these data sources but also wish to understand the temporal dynamics of these topics and predict certain properties of terms or documents in the future. Topic models are usually utilized to find latent topics from text collections, and recently have been applied to temporal text corpora. However, most proposed models are general purpose models to which no real tasks are explicitly associated. Therefore, current models may be difficult to apply in real-world applications, such as the problems of tracking trends and predicting popularity of keywords. In this paper, we introduce a real-world task, tracking trends of terms, to which temporal topic models can be applied. Rather than building a general-purpose model, we propose a new type of topic model that incorporates the volume of terms into the temporal dynamics of topics and optimizes estimates of term volumes. In existing models, trends are either latent variables or not considered at all which limits the potential for practical use of trend information. In contrast, we combine state-space models with term volumes with a supervised learning model, enabling us to effectively predict the volume in the future, even without new documents. In addition, it is straightforward to obtain the volume of latent topics as a by-product of our model, demonstrating the superiority of utilizing temporal topic models over traditional time-series tools (e.g., autoregressive models) to tackle this kind of problem. The proposed model can be further extended with arbitrary word-level features which are evolving over time. We present the results of applying the model to two datasets with long time periods and show its effectiveness over non-trivial baselines.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 TrackingTrendsIncorporatingTerm	Dawei Yin Liangjie Hong Brian D. Davison Jian Guo			Tracking Trends: Incorporating Term Volume Into Temporal Topic Models				10.1145/2020408.2020485		2011