NDLI: Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization

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Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization

Content Provider	ACM Digital Library
Author	Saha, Ankan Sindhwani, Vikas
Abstract	As massive repositories of real-time human commentary, social media platforms have arguably evolved far beyond passive facilitation of online social interactions. Rapid analysis of information content in online social media streams (news articles, blogs,tweets etc.) is the need of the hour as it allows business and government bodies to understand public opinion about products and policies. In most of these settings, data points appear as a stream of high dimensional feature vectors. Guided by real-world industrial deployment scenarios, we revisit the problem of online learning of topics from streaming social media content. On one hand, the topics need to be dynamically adapted to the statistics of incoming datapoints, and on the other hand, early detection of rising new trends is important in many applications. We propose an online nonnegative matrix factorizations framework to capture the evolution and emergence of themes in unstructured text under a novel temporal regularization framework. We develop scalable optimization algorithms for our framework, propose a new set of evaluation metrics, and report promising empirical results on traditional TDT tasks as well as streaming Twitter data. Our system is able to rapidly capture emerging themes, track existing topics over time while maintaining temporal consistency and continuity in user views, and can be explicitly configured to bound the amount of information being presented to the user.
Starting Page	693
Ending Page	702
Page Count	10
File Format	PDF
ISBN	9781450307475
DOI	10.1145/2124295.2124376
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-02-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Time series analysis Nmf Topic models Dictionary learning
Content Type	Text
Resource Type	Article

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