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Exploring Temporal Analysis of Tweet Content from Cultural Events

  • Mathias Quillot
  • Cassandre Ollivier
  • Richard Dufour
  • Vincent Labatut
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10583)

Abstract

Online social networking platforms are an important communication medium for cultural events, as they allow exchanging opinions almost in real-time, by publishing messages during the event itself, but also outside of this period. Word embedding has become a popular way to represent and extract information from such messages. In this paper, we propose a preliminary work aiming at assessing the benefits of taking temporal information into account when modeling messages in the context of a cultural event. We perform statistical and visual analyses on two word different representations: one including temporal information (Temporal Embedding), the second ignoring it (Word2Vec approach). Our preliminary results show that the obtained models exhibit some similarities, but also differ significantly in the way they represent certain specific words. More interestingly, the temporal information conveyed by the Temporal Embedding model allows to identify more relevant word associations related to the domain at hand (cultural festivals).

Keywords

Word embedding Temporal representation Statistical analysis Cultural events 

Notes

Acknowledgments

This work was funded by the GaFes project supported by the French National Research Agency (ANR) under contract ANR-14-CE24-0022.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mathias Quillot
    • 1
  • Cassandre Ollivier
    • 1
  • Richard Dufour
    • 1
  • Vincent Labatut
    • 1
  1. 1.LIA, University of AvignonAvignonFrance

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