Ranking Linked-Entities in a Sentiment Graph

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


Reputation analysis is naturally associated to a sentiment analysis task of the targeted named-entities. This analysis leverages on a sentiment lexicon that includes general sentiment words that characterize the general sentiment towards the targeted named-entity. However, in most cases, target entities are themselves part of the sentiment lexicon, creating a loop from which it is difficult to infer an entity reputation. Sometimes, the entity became a reference in the domain and is vastly cited as an example of a highly reputable entity. For example, in the movies domain it is not uncommon to see reviews citing Batman or Anthony Hopkins as esteemed references. In this paper we describe a three-step procedure to perform reputation analysis of linked entities. First, our method jointly extracts named entities reputation and a domain specific sentiment lexicon. Second, an entities graph is created by analyzing cross-citations in subjective sentences. Third, the entities reputation are updated through an iterative optimization that exploits the graph of the linked-entities. The proposed approach closely models real-world domains, where domain specific jargon is common and entities are so popular that they become widely used as sentiment references. The evaluation on a graph with 12,687 vertices, of which 3,177 are linked entities and 9,510 are sentiment words, shows that our approach can improve the correct detection of an entity's reputation.
Original languageEnglish
Title of host publicationWeb Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on
Publication statusPublished - 2014
EventIEEE/ACM Web Intelligence -
Duration: 1 Jan 2014 → …


ConferenceIEEE/ACM Web Intelligence
Period1/01/14 → …


Dive into the research topics of 'Ranking Linked-Entities in a Sentiment Graph'. Together they form a unique fingerprint.

Cite this