2014 LargeScaleHighPrecisionTopicMod

• (Yang et al., 2014) ⇒ Shuang-Hong Yang, Alek Kolcz, Andy Schlaikjer, and Pankaj Gupta. (2014). “Large-scale High-precision Topic Modeling on Twitter.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623336

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Large-scale+High-precision+Topic+Modeling+on+Twitter
• http://dl.acm.org/citation.cfm?id=2623330.2623336&preflayout=flat#citedby

Quotes

Author Keywords

• Concept learning; data mining; large-scale machine learning; social media; text classification; topic modeling

Abstract

We are interested in organizing a continuous stream of sparse and noisy texts, known as “tweets ", in real time into an ontology of hundreds of topics with measurable and stringently high precision. This inference is performed over a full-scale stream of Twitter data, whose statistical distribution evolves rapidly over time. The implementation in an industrial setting with the potential of affecting and being visible to real users made it necessary to overcome a host of practical challenges. We present a spectrum of topic modeling techniques that contribute to a deployed system. These include non-topical tweet detection, automatic labeled data acquisition, evaluation with human computation, diagnostic and corrective learning and, most importantly, high-precision topic inference. The latter represents a novel two-stage training algorithm for tweet text classification and a close-loop inference mechanism for combining texts with additional sources of information. The resulting system achieves 93% precision at substantial overall coverage.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 LargeScaleHighPrecisionTopicMod	Shuang-Hong Yang Alek Kolcz Andy Schlaikjer Pankaj Gupta			Large-scale High-precision Topic Modeling on Twitter				10.1145/2623330.2623336		2014