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 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 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 breakdown into events allows interpreting the reputation in a transparent and self-explanatory way.

Multi-Source Spatial Entity Extraction and Linkage

“…Typically, this type of crawling has not been used for location-based data, but rather on works on text mining, opinion mining, named entities, constructing the reputation of an entity, etc. [59][60][61]. However, querying with the location's name might return data that is geo-located in the area indicated by the keyword, but the results might also contain irrelevant data.…”