Interpreting Reputation Through Frequent Named Entities in Twitter

  • Nacéra Bennacer
  • Francesca BugiottiEmail author
  • Moditha Hewasinghage
  • Suela Isaj
  • Gianluca Quercini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10569)


Twitter is a social network that provides a powerful source of data. The analysis of those data offers many challenges among those stands out the opportunity to find the reputation of a product, of a person, or of any other entity of interest. Several tools for sentiment analysis have been built in order to calculate the general opinion of an entity using a static analysis of the sentiments expressed in tweets. However, entities are not static; they collaborate with other entities and get involved in events. A simple aggregation of sentiments is then not sufficient to represent this dynamism. In this paper, we present a new approach that identifies the reputation of an entity on the basis of the set of events it is involved into by providing a transparent and self explanatory way for interpreting reputation. In order to perform this analysis we define a new sampling method based on a tweet weighting to retrieve relevant information. In our experiments we show that the 90% of the reputation of the entity originates from the events it is involved into, especially in the case of entities that represent public figures.


Reputation Frequent itemsets Sampling Opinion mining 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nacéra Bennacer
    • 1
  • Francesca Bugiotti
    • 1
    Email author
  • Moditha Hewasinghage
    • 1
  • Suela Isaj
    • 1
  • Gianluca Quercini
    • 1
  1. 1.LRI, CentraleSupélecParis-Saclay UniversityGif-sur-YvetteFrance

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