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

10 Citations (Scopus)

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 publicationAAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference
Pages1007-1012
Number of pages6
Volume2
StatePublished - Nov 1 2010
Event24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10 - Atlanta, GA, United States
Duration: Jul 11 2010Jul 15 2010

Other

Other24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
CountryUnited States
CityAtlanta, GA
Period7/11/107/15/10

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Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Chen, B., Zhu, L., Kifer, D., & Lee, D. (2010). What is an opinion about? Exploring political standpoints using opinion scoring model. In AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference (Vol. 2, pp. 1007-1012)
Chen, Bi ; Zhu, Leilei ; Kifer, Daniel ; Lee, Dongwon. / What is an opinion about? Exploring political standpoints using opinion scoring model. AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference. Vol. 2 2010. pp. 1007-1012
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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.",
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Chen, B, Zhu, L, Kifer, D & Lee, D 2010, What is an opinion about? Exploring political standpoints using opinion scoring model. in AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference. vol. 2, pp. 1007-1012, 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10, Atlanta, GA, United States, 7/11/10.

What is an opinion about? Exploring political standpoints using opinion scoring model. / Chen, Bi; Zhu, Leilei; Kifer, Daniel; Lee, Dongwon.

AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference. Vol. 2 2010. p. 1007-1012.

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

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AB - 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.

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Chen B, Zhu L, Kifer D, Lee D. What is an opinion about? Exploring political standpoints using opinion scoring model. In AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference. Vol. 2. 2010. p. 1007-1012