User interest prediction for tweets using semantic enrichment with DBpedia
Denis Lukovnikov, Mathias Verbeke, Bettina Berendt · Lirias · 2013
This paper focuses on topic-based prediction of interest of individual users to posts in the context of Twitter. Two methods for enriching tweets using DBpedia for the purposes of classification are proposed. The first method incorporates entity linking and uses linked entities in a tweet to improve classification, whereas the second method aims to improve upon the first one by adding information derived from DBpedia about entities found using the first method. The two methods are evaluated with respect to tweet classification.