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The President, Polarization and the Party Platforms, 1944–2012

Soren Jordan, Clayton McLaughlin Webb and B. Dan Wood
From the journal The Forum

Abstract

Scholars generally agree that political elites in the US are polarized. Yet most of our evidence, especially longitudinal evidence, is built on proxy measures of elite ideology that fail to identify the unique dimensions that drive the cleavages between the parties. And our understanding of when elite polarization reemerged is also unclear. This study leverages the party platforms, along with the tools of content analysis, to shed new light on elite polarization. We find that, consistent with the literature, elite polarization is an asymmetric phenomenon driven by Republicans primarily motivated by economic issues. Further, we show that modern elite polarization emerged starting with the 1980 election.


Corresponding author: B. Dan Wood, Department of Political Science, Texas A&M University, 4348 TAMU College Station, TX 77843-4348, USA, Phone: (979) 845-1610, Fax: (979) 847-8924, e-mail:

  1. 1

    The actual 2012 platform hearings for the Democratic Party can be viewed on C-SPAN at http://www.c-span.org/Events/Democrats-Draft-Platform-for-National-Convention/10737432897/

  2. 2

    The actual 2012 Republican platform hearings can be viewed on C-SPAN at http://www.c-span.org/Events/Platform-Cmte-Sends-Platform-to-GOP-Convention-Delegates/10737433193-5/

Appendix

Basically, Text Mining is the process of deriving high quality information from text input. Information is derived by uncovering patterns and trends in the text through a variety of statistical techniques. These techniques include, but are not limited to developing frequency distributions for particular words, finding associations between words within documents, categorizing document text by concepts, mapping word importance, as well as clustering either documents or terms. Once such techniques are implemented, the analyst turns to interpreting the emerging patterns.

Text Mining requires the analyst first to load and pre-condition the documents by eliminating extraneous terms and information. The entire set of documents is loaded just as a quantitative dataset is loaded into common statistical packages. However, the documents must be pre-conditioned to get them into a matrix form. The entire body of texts is called a Corpus. The Corpus is processed to remove numbers, punctuation, and whitespace. Stop words (such as the, is, at, which, on, etc.) are removed using a stop word dictionary. All words are converted to lower case text. Then, all words are converted to their stems, with endings removed. Once the Corpus is pre-conditioned in this manner, a sorted document term matrix is created containing all of the remaining words. The document term matrix contains the documents in the rows and counts of unique terms for each document in the columns. Finally, sparse terms are removed from the document term matrix according to some arbitrary criterion (e.g., removing terms from the matrix so that only 5% of any column is null). The resulting document term matrix then becomes the data for analysis.

Cluster analysis is a set of techniques for partitioning a set of objects (in our case party platforms) into relatively homogeneous subsets based on their inter-object similarities. A cluster is defined by the similarity of its members, where similarity is determined using a geometric measure of vector distance. Once the distance between objects is calculated, an algorithm is applied to cluster the objects into sub-groups based on their inter-object similarities. Thus, we first scaled the document term matrix using the built-in R function scale to standardize the columns for each term. Then, the Euclidian distance between each column was calculated using the built-in R function dist. The resultant distances were then clustered hierarchically using hclust and a variety of clustering algorithms.

Appendix Table 1

Democrat Polarizing Stemwords.

Economic (n=22)Foreign (n=33)Morality (n=14)Regulation (n=17)Social (n=30)
ClassAfghanistanAbortAgriculturAccess
CompaniAlliancBelievBanAfford
CreditArsenalBiologCivicAid
CutAssaultChoicCleanCare
DisparAttackChoosCleanerChild
DistributBorderCreationClimatChildren
EconomiChinaEthicEnvironClassroom
GapCombatFaithEnvironmentColleg
InvestDefendFamiliFarmCommuniti
JobDiplomaciGayFarmerCoverag
ManufacturDisarmaSexualFuelCrack
MiddlGlobalViolencGunCrime
MoneyHomelandWhiteInfrastructurDoctor
MonopoliImmigrWomanInstitutDrug
PayIranLobbyistGraduat
SkillIraqPollutHealth
TaxKoreaSustainHealthi
TradeMexicoHivaid
ValuNuclearHomeless
WealthiOverseaMedicaid
WealthiestPakistanMedicar
WorkerProliferPovert
RussiaRelief
RussianSchool
SafeScienc
SanctionSenior
SecurStudent
SovereigntiTeach
TerrorTeacher
TerroristVoucher
Threat
Troop
Weapon

Appendix Table 2

Republican Polarizing Stemwords.

Economic (n=60)Foreign (n=38)Morality (n=38)Regulation (n=53)Social (n=30)
AffordAfricaAbortAlternAccess
AssetAlienAbusAmendBenefit
BailoutArmAdultAppropriCare
BankAsiaBeliefBanChild
BearAssaultCellBorrowChildren
BudgetAttackChoicBureaucratColleg
BurdenBaseChoosCentralCoverag
BusiBattlConsciencCivilCrime
CapitBilaterConsentClimatCrimin
ClassBorderCulturCoalDisabl
CommercChinaEthicCommunitiDrug
CompaniCombatEthnicCongressEduc
CompetitCommunistFaithCongressionEntitl
ConsumConflictFaithbasConstitutHealth
CorporCubaFamiliCorruptLearn
CostDefensInfantCourtMedic
CosteffectGlobalKillEndangMedicaid
CutImmigrLifeEnergiMedicar
DebtIranMainstreamEnvironMedicin
DollarIraqMarriagEnvironmentPatient
EarnIsraelMoralExpandPrescript
EconomKoreaMurderFederRead
EconomiMexicoParentFraudSchool
EmployeMilitariPrayerFreedomScienc
EntrepreneurMissilPregnancFuelSenior
FinanciNuclearPunishGasSentenc
FundPalestinianReligiGovernSocial
GainPatriotReligionGunStudent
GrowRussiaSexualIllegTeacher
GrowthSovereigntiStemIndividuTuition
HomeownershipTaiwanTeenInfrastructur
ImportTerrorTolerLaw
InvestThreatTraditLawsuit
JobThreatenUnbornLawyer
LowincomTroopVictimLegal
ManufacturWarViolencLiberti
MarketWeaponViolentLimit
MarketplacWorldWomenLitig
MiddlLocal
MoneyLower
OwnMandatori
OwnerOil
OwnershipProperti
PayProsecut
PrivatPublic
PurchasRegul
RateRegulatori
SectorRepeal
SpendResourc
TaxSelfgovern
TaxandspendSelfsuffici
TaxfreSenat
TaxpaySize
Taxpayerfund
Trade
Trillion
Valu
Worker
Workforc
Workplac

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Published Online: 2014-5-8
Published in Print: 2014-4-1

©2014 by Walter de Gruyter Berlin/Boston

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