Harmony and dissonance: organizing the people's voices on political controversies

ABSTRACT

The wikileaks documents about the death of Osama Bin Laden and the debates about the economic crisis in Greece and other European countries are some of the controversial topics being played on the news everyday. Each of these topics has many different aspects, and there is no absolute, simple truth in answering questions such as: should the EU guarantee the financial stability of each member country, or should the countries themselves be solely responsible? To understand the landscape of opinions, it would be helpful to know which politician or other stakeholder takes which position - support or opposition - on these aspects of controversial topics.

References

1. E. Agichtein and L. Gravano. Snowball: Extracting relations from large plain-text collections. In ICDL, 2000.
2. R. Awadallah, M. Ramanath, and G. Weikum. Language-model-based pro/con classification of political text. In SIGIR, 2010.
3. A. Balahur, E. Boldrini A. Montoyo, and P. Martínez-Barco. A unified proposal for factoid and opinionated question answering. In COLING, 2010.
4. S. Brin. Extracting patterns and relations from the world wide web. In Selected papers from the Intl. Workshop on The World Wide Web and Databases, 1999.
5. A. Doan, L. Gravano, R. Ramakrishnan, and S. Vaithyanathan. Special issue on managing information extraction. In ACM SIGMOD Record, 2008.
6. S. Elbassuoni, M. Ramanath, R. Schenkel, and G. Weikum. Searching RDF graphs with SPARQL and keywords. In IEEE Data Engg. Bulletin, 2010.
7. A. Esuli, S. Baccianella, and F. Sebastiani. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In LREC 2010, 2010.
8. D. Hiemstra. Using Language Models for Information Retrieval. PhD thesis, University of Twente, Enschede, 2001.
9. J. Hoffart, M. Amir, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol B. Taneva, S. Thater, and G. Weikum. Robust disambiguation of named entities in text. In EMNLP 2011, 2011.
10. R. Kaptein, M. Marx, and J. Kamps. Who said what to whom?: capturing the structure of debates. In SIGIR, 2009.
11. G. Karypis and V. Kumar. A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs. SIAM J. Sci. Comput., 20(1), 1998.
12. D. Klein and C. Manning. Accurate unlexicalized parsing. In ACL, 2003.
13. B. Liu. Sentiment analysis and subjectivity. In Handbook of Natural Language Processing, Second Edition. 2010.
14. Y. Lu, H. Duan, H. Wang, and C. Zhai. Exploiting structured ontology to organize scattered online opinions. In COLING, 2010.
15. T. Mullen and R. Malouf. Taking sides: User classification for informal online political discourse. Internet Research, 18(2), 2008.
16. B. Pang and L. Lee. Opinion mining and sentiment analysis. FnT in IR, 2(1--2), 2008.
17. S. Sarawagi. Information extraction. FnT in Databases, 1(3), 2008.
18. F. Suchanek, G. Kasneci, and G. Weikum. Yago: A Core of Semantic Knowledge. In WWW, 2007.
19. M. Thomas, B. Pang, and L. Lee. Get out the vote: Determining support or opposition from congressional floor-debate transcripts. In EMNLP, 2006.
20. E. Wilson. Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158), 1927.
21. B. Yu, S. Kaufmann, and D. Diermeier. Classifying party affiliation from political speech. Information Technology & Politics, 5(1), 2008.
22. C. Zhai. Statistical language models for information retrieval. FnT in IR, 2(3), 2008.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Natural language processing

• Information systems

  • Information retrieval

    • Document representation

      • Content analysis and feature selection