What Is an Opinion About? Exploring Political Standpoints Using Opinion Scoring Model

Bi Chen, Leilei Zhu, Daniel Kifer, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

In this paper, we propose a generative model to automatically discover the hidden associations between topics words and opinion words. By applying those discovered hidden associations, we construct the opinion scoring models to extract statements which best express opinionists' standpoints on certain topics. For experiments, we apply our model to the political area. First, we visualize the similarities and dissimilarities between Republican and Democratic senators with respect to various topics. Second, we compare the performance of the opinion scoring models with 14 kinds of methods to find the best ones. We find that sentences extracted by our opinion scoring models can effectively express opinionists' standpoints.

Original languageEnglish (US)
Title of host publicationProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010
PublisherAAAI press
Pages1007-1012
Number of pages6
ISBN (Electronic)9781577354642
StatePublished - Jul 15 2010
Event24th AAAI Conference on Artificial Intelligence, AAAI 2010 - Atlanta, United States
Duration: Jul 11 2010Jul 15 2010

Publication series

NameProceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010

Conference

Conference24th AAAI Conference on Artificial Intelligence, AAAI 2010
Country/TerritoryUnited States
CityAtlanta
Period7/11/107/15/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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