Work

Supervised and unsupervised methods in employing discourse relations for improving opinion polarity classification

Year: 2009

Type: conference-paper

Abstract: This work investigates design choices in modeling a discourse scheme for improving opinion polarity classification. For this, two diverse global inference paradigms are used: a supervised collective classification framework and an unsupervised optimization framework. Both approaches perform substantially better than baseline approaches, establishing the efficacy of the methods and the underlying discourse scheme. We also present quantitative and qualitative analyses showing how the improvements are achieved.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.3115/1699510.1699533

Authors: Swapna Somasundaran, Galileo Namata, Janyce Wiebe, Lise Getoor

Institutions: University of Pittsburgh, University of Maryland, College Park

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Homeland Security

Award: CAREER: Graph Identification