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.
CITATION STYLE
Bennacer, N., Bugiotti, F., Hewasinghage, M., Isaj, S., & Quercini, G. (2017). Interpreting reputation through frequent named entities in twitter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10569 LNCS, pp. 49–56). Springer Verlag. https://doi.org/10.1007/978-3-319-68783-4_4
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