Statistical Analysis of High-Level Features from State of the Union Addresses

A computational political science approach is taken to analyze the State of the Union Addresses SUA from 1790 to 2015. While low-level features, e.g. linguistic characteristics, are commonly used for lexical analysis, the authors herein illustrate the utility of high-level features, e.g. Flesch-Kincaid readability, for knowledge discovery and discrimination between types of speeches. A process is developed and employed to exploit high-level features which employs 1 statistical clustering k-means and a literature review to define types of speeches e.g. written or oral, 2 classification methods via logistic regression to examine the validity of the defined classes, and 3 classifier-based feature selection to determine salient features. Recent interest in the SUA has posited that changes in readability in the SUA are due to declining audience capabilities; however, the authors' results show that changes in readability are a reflection of changes in the SUA delivery medium.

[1]  K A Spackman.  Maximum likelihood training of connectionist models: comparison with least squares back-propagation and logistic regression. , 1991, Proceedings. Symposium on Computer Applications in Medical Care.

[2]  Huan Liu,et al.  Feature Selection: An Ever Evolving Frontier in Data Mining , 2010, FSDM.

[3]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[4]  Emmanuel Adugu,et al.  Political Consumption as Supplement to Conventional Political Participation in Promoting Social Change , 2016, Int. J. Inf. Syst. Soc. Chang..

[5]  N. Japkowicz.  Learning from Imbalanced Data Sets: A Comparison of Various Strategies * , 2000 .

[6]  Dain Kaplan,et al.  ELIxIR: Expertise Learning and Identification x Information Retrieval , 2011, Int. J. Inf. Syst. Soc. Chang..

[7]  Dustin Hillard,et al.  Automated classification of congressional legislation , 2006, DG.O.

[8]  Thomas H. Koenig,et al.  Digital Scarlet Letters: Social Media Stigmatization of the Poor and What Can Be Done , 2015 .

[9]  Morteza Shafiee Sardasht,et al.  Use of Data Reduction Process to Bankruptcy Prediction: Evidence from an Emerging Market , 2016, Int. J. Inf. Syst. Soc. Chang..

[10]  Brian Manhire.  Grade Inflation, Ethics, And Engineering Education , 2004 .

[11]  A. Sheth,et al.  Discovering Fine-grained Sentiment in Suicide Notes , 2012, Biomedical informatics insights.

[12]  S. Manel,et al.  Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird , 1999 .

[13]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  C. Mills,et al.  Power Elite , 1956, Encyclopedia of Social Network Analysis and Mining.

[15]  Lawrence Carin.  Adaptive Multi-Sensor Interrogation of Targets Embedded in Complex Environments , 2010 .

[16]  D. P. Hayes,et al.  Schoolbook Simplification and Its Relation to the Decline in SAT-Verbal Scores , 1996 .

[17]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[18]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[19]  Eric K. Ringger,et al.  Topics Over Nonparametric Time: A Supervised Topic Model Using Bayesian Nonparametric Density Estimation , 2012, BMA.

[20]  S L Mailloux,et al.  How reliable is computerized assessment of readability? , 1995, Computers in nursing.

[21]  Langche Zeng.  Prediction and Classification with Neural Network Models , 1999 .

[22]  W. Hauck,et al.  Wald's Test as Applied to Hypotheses in Logit Analysis , 1977 .

[23]  Valérie Bourdès,et al.  Comparison of Artificial Neural Network with Logistic Regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient Outcomes , 2010, Adv. Artif. Neural Syst..

[24]  C. Y. Peng,et al.  An Introduction to Logistic Regression Analysis and Reporting , 2002 .

[25]  Allan Lichtman.  The Keys to the White House: A Preliminary Forecast for 2012 , 2010, Int. J. Inf. Syst. Soc. Chang..

[26]  J. Habbema,et al.  Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. , 2001, Journal of clinical epidemiology.

[27]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[28]  N. Postman.  Amusing ourselves to death : public discourse in the age of show business. , 1985 .

[29]  Kevin Coe,et al.  The Major Addresses of Modern Presidents: Parameters of a Data Set , 2011 .

[30]  Loren Collingwood,et al.  Tradeoffs in Accuracy and Efficiency in Supervised Learning Methods , 2012 .

[31]  Gary King,et al.  Improving Quantitative Studies of International Conflict: A Conjecture , 2000, American Political Science Review.

[32]  Charu C. Aggarwal,et al.  Mining Text Data , 2012 .

[33]  Scott de Marchi,et al.  Untangling Neural Nets , 2004, American Political Science Review.

[34]  Louis Fisher,et al.  Invoking Inherent Powers: A Primer , 2007 .

[35]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[36]  Chen Lou.  News Framing of the 1984 Bhopal Gas Leak in India and the 2010 BP Oil Spill in the Gulf of Mexico: A Content Analysis of The New York Times and The Washington Post Coverage , 2011 .

[37]  Ryan Lee Teten,et al.  Evolution of the Modern Rhetorical Presidency: Presidential Presentation and Development of the State of the Union Address , 2003 .

[38]  Dustin Hillard,et al.  Computer-Assisted Topic Classification for Mixed-Methods Social Science Research , 2008 .

[39]  T. Butterworth,et al.  Lend me your ears. , 2004, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[40]  Ingmar Weber,et al.  Data-driven political science , 2013, WSDM '13.

[41]  Jerald B. Johnson,et al.  Model selection in ecology and evolution. , 2004, Trends in ecology & evolution.

[42]  Kevin Gold,et al.  An information pipeline model of human-robot interaction , 2009, 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[43]  Lusa Lo,et al.  Readability of Individualized Education Programs , 2014 .

[44]  Edward R. Tufte,et al.  Data Analysis for Politics and Policy , 1974 .

[45]  A. Kohn.  The Dangerous Myth of Grade Inflation. , 2002 .

[46]  Maria Elizabeth Grabe,et al.  Taking Television Seriously: A Sound and Image Bite Analysis of Presidential Campaign Coverage, 1992–2004 , 2007 .

[47]  Hyoil Han,et al.  Survey of semantic annotation platforms , 2005, SAC '05.

[48]  Wilfred E. Binkley.  The President As Chief Legislator , 1956 .

[49]  E. Lim.  Five Trends in Presidential Rhetoric: An Analysis of Rhetoric from George Washington to Bill Clinton , 2002 .

[50]  Lawrence Carin,et al.  Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[51]  Andrew McCallum,et al.  Topics over time: a non-Markov continuous-time model of topical trends , 2006, KDD '06.

[52]  Daniel R. Wissinger,et al.  Effects of Critical Discussions on Middle School Students' Written Historical Arguments. , 2016 .

[53]  Stefan Kaufmann,et al.  Classifying Party Affiliation from Political Speech , 2008 .

[54]  Timo Honkela,et al.  Subjects on objects in contexts: Using GICA method to quantify epistemological subjectivity , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[55]  J V Tu,et al.  Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , 1996, Journal of clinical epidemiology.

[56]  James N. Druckman,et al.  Does Presidential Rhetoric Matter? Priming and Presidential Approval , 2004 .

[57]  D. Hallin.  Sound Bite News: Television Coverage of Elections, 1968–1988 , 1992 .

[58]  Donna R. Hoffman,et al.  Addressing the State of the Union: The Evolution And Impact of the Presidents's Big Speech , 2006 .

[59]  H. W. Hill.  Projecting the future of overhead projection , 1991, Proceedings Frontiers in Education Twenty-First Annual Conference. Engineering Education in a New World Order.

[60]  Tim Pitman,et al.  Selling Visions for Education: What Do Australian Politicians Believe in, Who are They Trying to Convince and How? , 2012 .

[61]  Jennifer S. Holmes,et al.  Political Science Computing: A Review of Trends in Computer Evolution and Political Science Research , 2009 .