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An Approach to Analyse a Hashtag-Based Topic Thread in Twitter

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Natural Language Processing and Information Systems (NLDB 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9612))

Abstract

In last years, the spread of social Web has promoted a strong interest in analyzing how information related to a given topic diffuses. Nevertheless, this is still quite an unexplored field in the literature. In this paper we propose a general approach that makes use of a set of Natural Language Processing (NLP) techniques to analyse some of the most important features of information related to a topic. The domain of this study is Twitter, since here topics are easily identified by means of hashtags. In particular, our aim is to analyse the possible change over time of the content sub-topicality and sentiment in the tracked tweets, and bring out their relationships with the users’ demographic features.

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Notes

  1. 1.

    http://www.uclassify.com/.

  2. 2.

    http://sentistrength.wlv.ac.uk/.

  3. 3.

    http://voyant-tools.org/.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126, Milan, Italy

    Ekaterina Shabunina, Stefania Marrara & Gabriella Pasi

Authors
  1. Ekaterina Shabunina
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  2. Stefania Marrara
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  3. Gabriella Pasi
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Corresponding author

Correspondence to Ekaterina Shabunina .

Editor information

Editors and Affiliations

  1. ConservatoireNational desArts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, United Kingdom

    Farid Meziane

  3. University of Salford, Salford, United Kingdom

    Mohamad Saraee

  4. Oakland University, Rochester, Michigan, USA

    Vijayan Sugumaran

  5. University of Salford, Salford, United Kingdom

    Sunil Vadera

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© 2016 Springer International Publishing Switzerland

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Shabunina, E., Marrara, S., Pasi, G. (2016). An Approach to Analyse a Hashtag-Based Topic Thread in Twitter. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_34

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  • DOI: https://doi.org/10.1007/978-3-319-41754-7_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41753-0

  • Online ISBN: 978-3-319-41754-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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