Exploring Political Ideologies of Senators With Semantic Analysis Tools

Abstract

Phrase counting is an effective approach to capturing individual differences in language use. Specific phrases (e.g., “war on terrorism”) powerfully predict a congressperson’s political ideology. The question addressed in this study is whether there is additional information that can be extracted from the indirect relations among words in large Senatorial speech databases. Given that direct co-occurrence of target words is a very low likelihood event, we focus on higher-order co-occurrence (e.g., whether two target words appear in similar semantic contexts), using the free software Contrast Analysis of Semantic Similarity (CASS) to compute individual differences (www.casstools.org). We describe how we used CASS in detail and provide a tutorial. Using text transcripts from 86 Senators, totaling over 150 million words, we demonstrate that CASS can account for political ideology above and beyond phrase counts. By complementing phrase count methods, CASS may be a useful method for the digital humanities and social sciences more generally.

Keywords

CASS co-occurrence political ideology semantic space models

Get full access to this article

View all access options for this article.

References

Carroll

Lewis

McCarty

Poole

Rosenthal

(2013). DW-nominate scores with bootstrapped standard errors. Retrieved from http://voteview.org/dwnominate.asp

Carroll

Lewis

J. B.

Poole

K. T.

Rosenthal

(2009). Measuring bias and uncertainty in DW-NOMINATE ideal point estimates via the parametric bootstrap. Political Analysis, 17, 261-275.

Church

K. W.

Hanks

(1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16, 22-29.

Fast

L. A.

Funder

D. C.

(2008). Personality as manifest in word use: Correlations with self-report, acquaintance report, and behavior. Journal of Personality and Social Psychology, 94, 334-346.

Fetzer

(2008). “And I think that is a very straightforward way of dealing with it”: The communicative function of cognitive verbs in political discourse. Journal of Language and Social Psychology, 27, 384-396.

Gentzkow

Shapiro

J. M.

(2010). What drives media slant? Evidence from U.S. daily newspapers. Econometrica, 78, 35-71.

GPO. (2012). Digital collection of the congressional record. Retrieved from http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=CREC

Holtzman

N. S.

Schott

J. P.

Jones

M. N.

Balota

D. A.

Yarkoni

(2011). Exploring media bias with semantic analysis tools: Validation of the Contrast Analysis of Semantic Similarity (CASS). Behavior Research Methods, 43, 193-200.

Ireland

M. E.

Mehl

M. R.

(2014). Language use and personality. In Holtgraves

(Ed.), Oxford handbook of language and social psychology (pp. 201-218). New York, NY: Oxford University Press.

10.

Jones

M. N.

Mewhort

D. J. K.

(2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1-37.

11.

Landauer

T. K.

Dumais

S. T.

(1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104, 211-240.

12.

Lund

Burgess

(1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods Instruments & Computers, 28, 203-208.

13.

Matsumoto

Hwang

H. C.

(2013). The language of political aggression. Journal of Language and Social Psychology, 32, 335-348.

14.

McCarty

N. M.

Poole

K. T.

Rosenthal

(1997). Income redistribution and the realignment of American politics. Washington, DC: SEI Press.

15.

Mehl

M. R.

Gosling

S. D.

Pennebaker

J. W.

(2006). Personality in its natural habitat: Manifestations and implicit folk theories of personality in daily life. Journal of Personality and Social Psychology, 90, 862-877.

16.

Pennebaker

J. W.

Francis

M. E.

Booth

R. J.

(2001). Linguistic inquiry and word count: LIWC 2001. Mahwah, NJ: Erlbaum.

17.

Recchia

Jones

M. N.

(2009). More data trumps smarter algorithms: Comparing pointwise mutual information with latent semantic analysis. Behavior Research Methods, 41, 647-656.

18.

Tausczik

Y. R.

Pennebaker

J. W.

(2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29, 24-54.

19.

Yarkoni

(2010). Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of Research in Personality, 44, 363-373.