Continuous Top-K Remarkable Comments over Textual Streaming Data Using ELM

The increasing popularity of location-based social networks encourages more and more users to share their experience. It deeply impact the decision of the other users. In this paper, we study the problem of top-K remarkable comments over textual streaming data. We first study how to efficiently identify the mendacious comments. Through using a novel machine learning technique named ELM, we could filter most of mendacious comments. We then study how to maintain these vital comments. For one thing, we propose a two-level index to maintain their position information. For another, we employ domination transitivity to remove meaningless comments. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.

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