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Semantics for Social Media

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Book cover Future and Emergent Trends in Language Technology (FETLT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9577))

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Abstract

In this paper we present four experiments on the analysis Italian social media texts using a linguistically-based semantic approach. The experiments are respectively: two on newspaper articles about two political crises, one on a twitter corpus centered on political themes, and one on a case study of strategic plan programs of candidates to the presidency of our university. The analyses carried out by the same system, focus on semantic features of texts highlighting three main traits: “factivity” or factuality, “subjectivity” and polarity. The system uses semantic knowledge derived from deep linguistic analysis at propositional level to classify texts at a fine-grained level. As will be shown in the paper, linguistically-based semantic information allows for neat distinction of writing styles when comparing newspapers writing styles, for irony detection in tweets, and in different degrees, for making readability judgements.

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Notes

  1. 1.

    www.corriere.it, www.liberoquotidiano.it, www.repubblica.it.

  2. 2.

    Final overall results are available online, http://www.slideshare.net/vivianapatti9/evalita-sentipolc14.

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Authors and Affiliations

  1. Department of Language Science and Department of Computer Science, Ca’ Foscari University, Ca’ Bembo, dd. 1075, 30123, Venice, Italy

    Rodolfo Delmonte & Rocco Tripodi

Authors
  1. Rodolfo Delmonte
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  2. Rocco Tripodi
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Correspondence to Rodolfo Delmonte .

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Editors and Affiliations

  1. University of Seville, Seville, Spain

    José F. Quesada

  2. University of Seville, Seville, Spain

    Francisco-Jesús Martín Mateos

  3. University of Seville, Seville, Spain

    Teresa Lopez-Soto

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Delmonte, R., Tripodi, R. (2016). Semantics for Social Media. In: Quesada, J., Martín Mateos, FJ., Lopez-Soto, T. (eds) Future and Emergent Trends in Language Technology. FETLT 2015. Lecture Notes in Computer Science(), vol 9577. Springer, Cham. https://doi.org/10.1007/978-3-319-33500-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-33500-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33499-8

  • Online ISBN: 978-3-319-33500-1

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