2012 TMLDAEfficientOnlineModelingofL

• (Wang et al., 2012) ⇒ Yu Wang, Eugene Agichtein, and Michele Benzi. (2012). “TM-LDA: Efficient Online Modeling of Latent Topic Transitions in Social Media.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339552

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+TM-LDA%3A+Efficient+Online+Modeling+of+Latent+Topic+Transitions+in+Social+Media
• http://dl.acm.org/citation.cfm?id=2339530.2339552&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; mining social media data; temporal language models; topic transition modeling

Abstract

Latent topic analysis has emerged as one of the most effective methods for classifying, clustering and retrieving textual data. However, existing models such as Latent Dirichlet Allocation (LDA) were developed for static corpora of relatively large documents. In contrast, much of the textual content on the web, and especially social media, is temporally sequenced, and comes in short fragments, including microblog posts on sites such as Twitter and Weibo, status updates on social networking sites such as Facebook and LinkedIn, or comments on content sharing sites such as YouTube. In this paper we propose a novel topic model, Temporal-LDA or TM-LDA, for efficiently mining text streams such as a sequence of posts from the same author, by modeling the topic transitions that naturally arise in these data. TM-LDA learns the transition parameters among topics by minimizing the prediction error on topic distribution in subsequent postings. After training, TM-LDA is thus able to accurately predict the expected topic distribution in future posts. To make these predictions more efficient for a realistic online setting, we develop an efficient updating algorithm to adjust the topic transition parameters, as new documents stream in. Our empirical results, over a corpus of over 30 million microblog posts, show that TM-LDA significantly outperforms state-of-the-art static LDA models for estimating the topic distribution of new documents over time. We also demonstrate that TM-LDA is able to highlight interesting variations of common topic transitions, such as the differences in the work-life rhythm of cities, and factors associated with area-specific problems and complaints.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 TMLDAEfficientOnlineModelingofL	Eugene Agichtein Yu Wang Michele Benzi			TM-LDA: Efficient Online Modeling of Latent Topic Transitions in Social Media				10.1145/2339530.2339552		2012