Mining Temporal Evolution of Entities in a Stream of Textual Documents

One of the recently addressed research directions focuses on the problem of mining topic evolutions from textual documents. Following this main stream of research, in this paper we face the different, but related, problem of mining the topic evolution of entities (persons, companies, etc.) mentioned in the documents. To this aim, we incrementally analyze streams of time-stamped documents in order to identify clusters of similar entities and represent their evolution over time. The proposed solution is based on the concept of temporal profiles of entities extracted at periodic instants in time. Experiments performed both on synthetic and real world datasets prove that the proposed framework is a valuable tool to discover underlying evolutions of entities and results show significant improvements over the considered baseline methods.