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Sense-Level Semantic Clustering of Hashtags

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Information Management and Big Data (SIMBig 2015, SIMBig 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 656))

Abstract

We enhance the accuracy of the currently available semantic hashtag clustering method, which leverages hashtag semantics extracted from dictionaries such as Wordnet and Wikipedia. While immune to the uncontrolled and often sparse usage of hashtags, the current method distinguishes hashtag semantics only at the word-level. Unfortunately, a word can have multiple senses representing the exact semantics of a word, and, therefore, word-level semantic clustering fails to disambiguate the true sense-level semantics of hashtags and, as a result, may generate incorrect clusters. This paper shows how this problem can be overcome through sense-level clustering and demonstrates its impacts on clustering behavior and accuracy.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Vermont, Burlington, Vermont, USA

    Ali Javed & Byung Suk Lee

Authors
  1. Ali Javed
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  2. Byung Suk Lee
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Corresponding author

Correspondence to Byung Suk Lee .

Editor information

Editors and Affiliations

  1. College of Medicine, University of Florida, Gainesville, Florida, USA

    Juan Antonio Lossio-Ventura

  2. Faculty of Engineering, Universidad del Pacífico, Jesús María, Lima, Peru

    Hugo Alatrista-Salas

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Javed, A., Lee, B.S. (2017). Sense-Level Semantic Clustering of Hashtags. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig SIMBig 2015 2016. Communications in Computer and Information Science, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-319-55209-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-55209-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55208-8

  • Online ISBN: 978-3-319-55209-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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