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A Frequent Named Entities Based Approach for Interpreting Reputation in Twitter

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 reputation of a product, a person, or any other entity of interest. Several approaches for sentiment analysis have been proposed in the literature to assess the general opinion expressed in tweets on an entity. Nevertheless, these methods aggregate sentiment scores retrieved from tweets, which is a static view to evaluate the overall reputation of an entity. The reputation of an entity is not static; entities collaborate with each other and they get involved in different events over time. A simple aggregation of sentiment scores is then not sufficient to represent this dynamism. In this paper, we present a new approach to determine the reputation of an entity on the basis of the set of events in which it is involved. To achieve this we propose a new 2 Nacéra Bennacer Seghouani et al. sampling method driven by a tweet weighting measure to give a better quality and summary of the target entity. We introduce the concept of Frequent Named Entities (FNE) to determine the events involving the target entity. Our evaluation achieved for different entities shows that 90% of the reputation of an entity originates from the events it is involved in and the break down into events allows interpreting the reputation in a transparent and self-explanatory way.
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https://hal.inria.fr/hal-01816523
Contributor : Francesca Bugiotti <>
Submitted on : Friday, June 15, 2018 - 1:38:34 PM
Last modification on : Monday, November 23, 2020 - 4:04:05 PM
Long-term archiving on: : Sunday, September 16, 2018 - 1:35:09 PM

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  • HAL Id : hal-01816523, version 1

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Nacéra Bennacer Seghouani, Francesca Bugiotti, Moditha Hewasinghage, Suela Isaj, Gianluca Quercini. A Frequent Named Entities Based Approach for Interpreting Reputation in Twitter. Data Science and Engineering, Springer, In press. ⟨hal-01816523⟩

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