Work

SMARTies: Sentiment Models for Arabic Target entities

Year: 2017

Type: conference-paper

Abstract: We consider entity-level sentiment analysis in Arabic, a morphologically rich language with increasing resources.We present a system that is applied to complex posts written in response to Arabic newspaper articles.Our goal is to identify important entity "targets" within the post along with the polarity expressed about each target.We achieve significant improvements over multiple baselines, demonstrating that the use of specific morphological representations improves the performance of identifying both important targets and their sentiment, and that the use of distributional semantic clusters further boosts performances for these representations, especially when richer linguistic resources are not available.

Source: Conference of the European Chapter of the Association for Computational Linguistics

DOI: 10.18653/v1/e17-1094

Authors: Noura Farra, Kathy McKeown

Institution: Columbia University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: U.S. Department of Defense, Fonds National de la Recherche Luxembourg, Defense Advanced Research Projects Agency, Qatar National Research Fund

Awards: G1525618996, G4173932784, G4713059963