Role-based Association of Verbs, Actions, and Sentiments with Entities in Political Discourse

Yair Fogel-Dror*, Shaul R. Shenhav, Tamir Sheafer, Wouter Van Atteveldt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


A crucial challenge in measuring how text represents an entity is the need to associate each representative expression with a relevant entity to generate meaningful results. Common solutions to this problem are usually based on proximity methods that require a large corpus to reach reasonable levels of accuracy. We show how such methods for the association between an entity and a representation yield a high percentage of false positives at the expression level and low validity at the document level. We introduce a solution that combines syntactic parsing, semantic role labeling logic, and a machine learning approach—the role-based association method. To test our method, we compared it with prevalent methods of association on the news coverage of two entities of interest—the State of Israel and the Palestinian Authority. We found that the role-based association method is more accurate at the expression and the document levels.

Original languageAmerican English
Pages (from-to)69-82
Number of pages14
JournalCommunication Methods and Measures
Issue number2
StatePublished - 3 Apr 2019

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© 2018, © 2018 Taylor & Francis Group, LLC.


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