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Interpreting Reputation Through Frequent Named Entities in Twitter

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Abstract

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 forinterpreting reputation. In order to perform this analysis we define a new sampling method based on a tweetweighting 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.

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Author information

Authors and Affiliations

  1. LRI, CentraleSupélec, Paris-Saclay University, 91190, Gif-sur-Yvette, France

    Nacéra Bennacer, Francesca Bugiotti, Moditha Hewasinghage, Suela Isaj & Gianluca Quercini

Authors
  1. Nacéra Bennacer

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  2. Francesca Bugiotti

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  3. Moditha Hewasinghage

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  4. Suela Isaj

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  5. Gianluca Quercini

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Corresponding author

Correspondence toFrancesca Bugiotti.

Editor information

Editors and Affiliations

  1. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  2. Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Institute of Computing for Physics and Technology, Protvino, Russia

    Andrey Klimenko

  4. Nanyang Technological University, Singapore, Singapore

    Lu Chen

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Xiangliang Zhang

  6. Institute of Computing for Physics and Technology, Protvino, Russia

    Fedor Dzerzhinskiy

  7. Shanghai Jiao Tong University, Minhang Qu, China

    Weijia Jia

  8. Institute of Computing for Physics and Technology, Protvino, Russia

    Stanislav V. Klimenko

  9. City University of Hong Kong, Kowloon, Hong Kong

    Qing Li

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© 2017 Springer International Publishing AG

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Bennacer, N., Bugiotti, F., Hewasinghage, M., Isaj, S., Quercini, G. (2017). Interpreting Reputation Through Frequent Named Entities in Twitter. In: Bouguettaya, A.,et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_4

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