Congressional speech data
This page is a distribution site for a congressional-speech corpus and
related extracted information. This data includes
speeches as individual documents, together with:
If you have used this data, we would appreciate hearing about it (Lillian Lee is our
designated contact person); a list of those papers we know about can
be found below.
- automatically-derived labels for whether the speaker supported or opposed the
legislation discussed in the debate the speech appears in, allowing for experiments
with this kind of sentiment analysis
- indications of which "debate" each speech comes from,
allowing for consideration of conversational structure
- indications of by-name references between speakers, and the
scores that our agreement/disagreement classifier(s) automatically
assigned to such references, allowing for experiments on agreement
classification if one assigns "true" labels from
the support/oppose labels assigned to the pair of speakers in question
- the edge weights and other information we derived to
create the graphs we used for our experiments upon this data, facilitating
implementation of alternative graph-based methods upon the graphs we constructed
References This data was introduced in Matt Thomas, Bo Pang, and Lillian Lee, Get out the vote: Determining support or opposition from
Congressional floor-debate transcripts. The original version of
the paper appeared in the Proceedings of EMNLP, 2006,
pp. 327–335. However, the paper has been updated since then; the
link provided is to the most current version.
convote dataset v1.1 (9.8 Mb, tar.gz format), including
January 2008. The only difference from v1.0 is that a typo in the first line of
graph_edge_data/edges_individual_document.v1.0.csv has been
corrected. (This affects just a single file and our calculations used
the correct value.)
convote dataset v1.0 was released in December 2006. Please use the
one-line-different newer version v.1.1.
Other papers using this data Chronological order, then
alphabetically within a given year.
- Stephan Greene. Spin:
Lexical Semantics, Transitivity, and the Identification of Implicit
Sentiment. Ph.D. thesis, University of Maryland, 2007.
- Bei Yu, Stefan Kaufmann, and Daniel Diermeier. Ideology classifiers for political speech.
Available at SSRN: http://ssrn.com/abstract=1026925 (click on
“download” link), working paper
dated November 1, 2007.
- Ben Allison. Sentiment
Detection Using Lexically-Based Classifiers. Proceedings of TSD '08.
- Mohit Bansal, Claire Cardie and Lillian Lee. The
power of negative thinking: Exploiting label disagreement in the
min-cut classification framework. Proceedings of COLING: Companion volume: Posters, pp. 13–16, 2008.
- Clint Burfoot. Using multiple sources of agreement information
for sentiment classification of political transcripts. Australasian
Language Technology Workshop (ALTA) 2008.
- Marina Sokolova and Guy Lapalme. Verbs
Speak Loud: Verb Categories in Learning Polarity and Strength of
Opinions. Proceedings of the 20th Canadian Conference on
Artificial Intelligence (AI 2008), vol. 5032, series. Lecture Notes
in Artificial Intelligence, p. 320--331, 2008.
- Marina Sokolova and Guy Lapalme. Verbs as the most "affective" words. Affective language in human and machine, 2008.
- Bei Yu, Stefan Kaufmann, and Daniel Diermeier. 2008. Classifying party affiliation from political speech. Journal of Information Technology & Politics 5 (1): 33-48.
- Alexandra Balahur, Zornitsa Kozareva, Andrés Montoyo: Determining the Polarity and Source of Opinions Expressed in Political Debates. CICLing 2009: 468-480.
- Eric Gilbert, Tony Bergstrom, and Karrie Karahalios. Blogs
Are echo chambers: Blogs Are echo chambers. HICSS 2009.
- Justin Martineau and Tim Finin. Delta TFIDF: An
improved feature space for sentiment analysis. ICWSM 2009.
- Justin Martineau, Tim Finin, Anupam Joshi, and Shamit Patel. Improving binary classification on text problems using differential word features. CIKM 2009.
- Daniel Hopkins and Gary King. A method of automated
nonparametric content analysis for social science. American
Journal of Political Science 54(1):229–247 (2010).
- Dong Nguyen, Elijah Mayfield, and Carolyn Penstein Rosé. An analysis of perspectives in interactive settings. Workshop on Social Media Analytics, KDD 2010.
- Fernanda S. Pimenta, Darko Obradovi, Rafael Schirru, Stephan
Baumann and Andreas Dengel. Automatic sentiment monitoring of specific
topics in the blogosphere. ECML PKDD Workshop on Dynamic Networks
and Knowledge Discovery, 2010.
- Robert West, Doina Precup, and Joelle Pineau. Automatically suggesting topics for augmenting text documents. CIKM 2010.
- Ainur Yessenalina, Yisong Yue, and Claire Cardie. Multi-level structured models for document-level sentiment classification. EMNLP 2010
- Clinton Burfoot, Steven Bird, and Timothy Baldwin. Collective classification of Congressional floor-debate transcripts. ACL poster, 2011.
- Christopher Potts. On the negativity of negation. SALT, 2011.
- Jordan Bates, Jennifer Neville, and Jim Tyler. Using latent communication styles to predict individual characteristics. 3rd SOMA workshop, KDD, 2012
- Mahesh Joshi, Mark Dredze, William W. Cohen and Carolyn P. Rosé. Multi-Domain Learning: When Do Domains Matter? EMNLP 2012
- Veselin Stoyanov and Jason Eisner. Minimum-risk training of approximate CRF-based NLP systems. NAACL 2012.
- Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, and Philip Resnik. Political Ideology Detection Using Recursive Neural Networks. ACL 2014
- Robert West, Hristo S. Paskov, Jure Leskovec, Christopher Potts. Exploiting Social Network Structure for Person-to-Person Sentiment Analysis. TACL(2), 2014.
- Vesile Evrim, Aliyu Awwal. Effect of Personality Traits on Classification of
Political Orientation. International Journal of Social, Behavioral, Educational, Economic and Management Engineering Vol:9, No:6, 2015
The creation of this website is based upon work supported in part by
the National Science Foundation (NSF) under grant no. IIS-0329064, an
Alfred P. Sloan Research Fellowship, and Google Anita Borg Memorial
Scholarship funds. Any opinions, findings, and conclusions or
recommendations expressed above are those of the authors and do not
necessarily reflect the views of the National Science Foundation or
Sloan Foundation and should not be interpreted as representing the
official policies, either expressed or implied, of any sponsoring
institution, the U.S. government or any other entity.
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