Interpreting Reputation Through Frequent Named Entities 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 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.
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Communication dans un congrès
Web Information Systems Engineering - WISE, Oct 2017, Puschino - Moscow, Russia. Springer, 10569, pp.49-56, Lecture Notes in Computer Science. 〈10.1007/978-3-319-68783-4_4〉
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Nacéra Bennacer Seghouani, Francesca Bugiotti, Moditha Hewasinghage, Suela Isaj, Gianluca Quercini. Interpreting Reputation Through Frequent Named Entities in Twitter. Web Information Systems Engineering - WISE, Oct 2017, Puschino - Moscow, Russia. Springer, 10569, pp.49-56, Lecture Notes in Computer Science. 〈10.1007/978-3-319-68783-4_4〉. 〈hal-01611593〉

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