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Background and Related Work

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Topic Detection and Classification in Social Networks

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Abstract

Topic detection and tracking aims at extracting topics from a stream of textual information sources, or documents, and to quantify their “trend” in real time. These techniques apply on pieces of texts, i.e. posts, produced within social media platforms. Topic detection can produce two types of complementary outputs: cluster output or term output are selected and then clustered. In the first method, referred to as document-pivot , a topic is represented by a cluster of documents, whereas in the latter, commonly referred to as feature-pivot , a cluster of terms is produced instead. In the following, we review several popular approaches that fall in either of the two categories. Six state-of-the-art methods: Latent Dirichlet Allocation (LDA) , Document-Pivot Topic Detection (Doc-p) , Graph-Based Feature-Pivot Topic Detection (GFeat-p) , Frequent Pattern Mining (FPM) , Soft Frequent Pattern Mining (SFPM) , BNgram are described in detail, as they serve as the performance benchmarks to the proposed system.

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Authors and Affiliations

  1. Massachusetts Institute of Technology, Cambridge, MA, USA

    Dimitrios Milioris

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  1. Dimitrios Milioris
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Milioris, D. (2018). Background and Related Work. In: Topic Detection and Classification in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-66414-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-66414-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66413-2

  • Online ISBN: 978-3-319-66414-9

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