Multidimensional political spectrum identification and analysis

Leilei Zhu, Prasenjit Mitra

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

1 Citation (Scopus)

Abstract

In this work, we show the importance of multidimensional opinion representation in the political context combining domain knowledge and results from principal component analysis. We discuss the differences of feature selection between political spectrum analysis and normal opinion mining tasks. We build regression models on each opinion dimension for scoring and placing new opinion entities, e.g. personal blogs or politicians, onto the political opinion spectrum. We apply our methods on the floor statement records of the United States Senate and evaluate it against the uni-dimensional representation of political opinion space. The experimental results show the effectiveness of the proposed model in explaining the voting records of the Senate.

Original languageEnglish
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
Pages2045-2048
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong
Duration: 2 Nov 20096 Nov 2009

Other

OtherACM 18th International Conference on Information and Knowledge Management, CIKM 2009
CityHong Kong
Period2/11/096/11/09

    Fingerprint

Keywords

  • Dimensionality analysis
  • Opinion mining
  • Political spectrum
  • Regression

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Zhu, L., & Mitra, P. (2009). Multidimensional political spectrum identification and analysis. In International Conference on Information and Knowledge Management, Proceedings (pp. 2045-2048) https://doi.org/10.1145/1645953.1646297