- Introduction and Foundations from NLP
- Sentiment Analysis and Regression
- Classification using Logistic Regression and Trees
- Vector Space Models (Latent Semantic Analysis)
- Topic Models and Deep Learning

Scripts for doing the analyses in R. The first few are more generic, covering things like regular expressions, getting data from certain sources, and basic NLP.

- basic_examples.R
- text_utils.R
- tweet_data.R
- amazon_ratings.R
- missing_data.R
- pos_tagging.R (skipped in 2016)
- svd_examples.R
- federalist_naiveb.R
- bigram.R

- wine_data.R
- wine_sentiment.R
- wine_regr.R
- wine_classify.R
- wine_spectral.R
- wine_topic_models.R
- wine_cluster.R (Not covered in 2016)

Some data sets to play with...

- Wine tasting notes (running example)
- Amazon auto and electronics reviews
- Federalist papers in raw form and as a csv file
- 2015 Trump tweets and 2016 Trump tweets
- Positive and negative word lists (sentiment example)

- Monday Introduction
This introductory lecture discusses roles for data mining in the social sciences. The lecture also introduces JMP and the data sets that follow in later examples, particularly the ANES 2008 data from ICPSR. We'll start using JMP by exploring this large data file in class, using JMP's plot linking to explore voting behavior and the use of feeling thermometers. We also use JMP to explore a more complex regression model that has categorical predictors and interactions.

- Tuesday Regression
This lecture starts by reviewing the key contribution of statistics to modeling data, namely the standard error of an estimator. Bootstrapping adds to the interpretation. The lecture then considers the use of regression as a data mining tool, with discussion of methods for dealing with missing data. The lecture also introduces the problem of over-fitting in the context of stepwise linear regression in an example of modeling stock market returns. The R-files used are bootstrap.R and missing_data.R . Here's a reduced version of the data used to overfit the stock market

- Wednesday Model Selection
How does one avoid the problem of overfitting and find a model that honestly reports its precision? Statistics offers a variety of choices, ranging from an alphabet soup of criteria (AIC, BIC, RIC) shrinkage methods like the lasso to cross-validation. This link ( link ) shows the JMP script used for cross-validating a regression in JMP. (We might skip that demo depending on time.) The R script used to build the lasso model is lasso.R . (Here is the loan data mentioned in the script.)

- Thursday
*Helen Newberry Lab Session*We will meet in the Michigan Lab in State Street side of Newberry during the usual class time period from 1:30-3:00 for some hands-one time with JMP and the ANES. Time permitting, we'll use R as well to fit a lasso model.

- Friday
*July 4th holiday* - Monday Logistic regression
Many data mining problems classify observations, such as classifying the choices of voters. Linear regression is a poor match to this problem, unless you calibrate it. Calibrated linear regression is often just as good as a logistic regression, depending on how you grade the models. We'll look at likelihoods, confusion matrices and ROC curves -- all of which are needed in data mining too. Logistic regression can be a better match and may have a simpler form than a comparable linear model because the multiplicative form of logistic models avoids interactions in some cases. Calibration and variable selection remain problems.

- Tuesday Neural networks and boosting
No more equations -- or at least visible equations. Neural networks combine several logistic regressions fit simulataneously. Is the added complexity worth it? For that, we'll compare fits from networks to those from logistic models. Neural networks add another 'layer' of choice for the modeler too. Not only do you have to pick features to offer the network, but you also have to choose the number and arrangement of the network.

Boosting is a general approach that iteratively refines the fit of a model by building a sequence of models, each trying to reduce the errors of the predecessor. JMP has a nice implementation of this that we'll explore.

- Wednesday Classification trees and bagging
Classification trees are a very different approach to modeling based on separating the data into homogeneous subsets rather than forming equations. Nonetheless, there's still are regression view of these models that reveals their weakness: the fits don't easily

*borrow strength*. Bagging (and boosting) however provide a remedy for that problem. - Thursday
*Helen Newberry Lab Session*We will meet in the Michigan lab in Newberry during the usual class time period for some hands-one time with JMP and the ANES.

- Friday Comparisons and Intro to Text Mining
We'll wrap things up with a peek at text mining and a discussion of how the various methods fit together to form a powerful modeling toolkit. The lecture slides include a list of 10 things every data miner should do. This file holds the source for the R commands used in the text modeling. The links for the data files for the two examples of PCA in this lecture appear in the list below.

- Stocks
- Flowers
- PCA example (one component, regression)
- PCA example (two components)
- Fradulent loans (Warning: this one is 14MB)

My paper "An Introduction to Bootstrap
Methods" (which appeared in * Sociological Methods & Research
* back in 1989) introduces you to the ideas of bootstrap resampling
through a variety of examples. The paper includes examples in
regression and illustrates situations in which the bootstrap does not
give the answer you'd like.

- Syllabus
- The syllabus presents a brief overview of what happens in
each class, along with some review questions. This syllabus also
appears in the introductory program information given to you if
you attended the ICPSR Summer Program.
- Bibliography
- This annotated list of references is not comprehensive and
rather is more representative of what is available on a
wide variety of topics, ranging from how to handle complex
surveys to the methods for time series. Like the syllabus, you
also have this in the information distributed to program participants.
- Lecture Notes
- The file for each lecture is a printed version of the Word files
that I use for each class. I may clean up some errors if I find them,
but they are pretty close to what was used in class. To use the
R scripts that accompany the lecture notes, you'll need to have
installed R on your own system.
- Introduction
( Lecture1.R )

I lost the data on the sample proportions when class ended on Monday. Darn, and sorry. - Exploring the Bootstrap
( Lecture2.R )
- Data on osteoporosis osteo.dat

- The Bootstrap in Simple Regression
and Correlation
( Lecture3.R )

Here are the data sets that we used in this class:- Efron's law school data lsat.dat
- Skeletal age data for smoothing skelage.dat
- Florida county presidential election results florida2000.dat
- State-level abortion rates abort.dat
- Modified abortion rates that make DC influential abort.out.dat

*An R and S-Plus Companion to Applied Regression*John Fox has discussions of both these. Look for his relevant Web appendices for the book. - Multiple Regression
( Lecture4.R )

We used Duncan's data on occupational prestige for some of these examples. - More Methods, Flaws, and Intervals ( Lecture5.R )

- Introduction
( Lecture1.R )

- AXIS
- In addition to the "raw lisp" software used in class, I also used
the AXIS interface functions. You can get a zip file of the needed
programs and further information about AXIS and Lisp-Stat on my
main web page.
- Lisp-Stat
- The official source for Lisp-Stat is the
software archive prepared by Luke Tierney (author of Lisp-Stat). The
archive is available from the
University of Minnesota Statistics Department .
- R
- The CRAN archives have the source for R for various systems, including unix and windows. The archives also offer quite a few supplements and documentation.

- References to Bootstrap Resampling
- In addition to the
bibliography mentioned
above, I list references to bootstrap resampling used in
social science applications. I don't see too many of these
journals, so any suggestions on your part are appreciated.
No one seems to want to do this, but I'll post them if you
do. Just send me a
mail message .
- Dalgleish, L. I. (1995). Discriminant analysis: statistical
inference using the jackknife and bootstrap procedures.
*Psychological Bulletin*, Vol 116. (Shows some SAS routines for testing the size of coefficients.) - Follow this link to see web pages describing recent work using the bootstrap to assess goodness-of-fit measures.

- Dalgleish, L. I. (1995). Discriminant analysis: statistical
inference using the jackknife and bootstrap procedures.