Linguistics 460: Textual Data Analysis with R
UNC-Chapel Hill Linguistics
Fall 2024
Elliott Moreton

2024.04.24.W

Topics: Midterm 2 review

Class:

• HO on Canvas about final-project presentations
• Go over questions about course content
• Readings?
• Labs?
• Midterm review guide (on Canvas)?

Assignments:

• Today, 3 p.m.: First draft of project due
• For 5/3 (F), 2 p.m.: Upload presentation slides to Canvas
• For 5/3 (F), 4 p.m.: Revise project draft in response to feedback.

2024.04.22.M

Topics: Project clinic in class.

Class:

• How to hand in the first draft of the project (Canvas, Assignments, by "project groups")
• Work together in groups on project while instructor circulates.

Assignments:

• Press ahead on projects.
• For 4/24 W, 3 p.m.: First draft of project due.

2024.04.17.W

Topics: LASSO regression for document classification.

Class:

• Still using the IMDb database in the textdata library. (Can download imdb_jr.csv from the Canvas site)
• Linear models (reprise from 2/26)
• Logistic models for binary responses
  • Fit by maximum likelihood
  • Fit by LASSO
• How does performance compare on the database?

Assignments:

• Respond to email feedback about initial draft of project proposal if you haven't already.
• Press ahead on projects.
• For 4/24 W, 3 p.m.: First draft of project due.

2024.04.15.M

Topics: Evaluating classifier performance. Changing parameters to optimize performance

Class:

• Still using the IMDb database in the textdata library. (Can download imdb_jr.csv from the Canvas site)
• Fitting the Naive Bayes classifierl
• Using the classifier to make predictions
• Evaluating model performance: Cohen's kappa
• Adjusting model parameters to optimize performance
  • Varying document-term-matrix sparsity
  • Choice of weighting schemes: binary term frequency, term frequeny, tf-idf
  • Overfitting
• In-class project clinic

Assignments:

• For 4/17 (W): Review earlier reading (2/28) of Navarro 2017, Ch. 15, through end of Section 6, on linear regression
• For 4/17 (W): Install the glmnet package
• Respond to email feedback about initial draft of project proposal if you haven't already.
• Press ahead on projects.
• For 4/24 W, 3 p.m.: First draft of project due.

2024.04.10.W

Topics: Naive Bayes classifiers for document classification.

Class:

• Resume with the IMDb database in the textdata library.
• The Inverse Fallacy returns (from 2/14)
• Bayes's Rule
• Naive Bayes classifiers: theory
• Building a Naive Bayes classifier in R
• Making predictions with the classifier

Assignments:

• Respond to email feedback about initial draft of project proposal.
• Press ahead on projects.
• For 4/24 W, 3 p.m.: First draft of project due.

2024.04.08.M

Topics: Document classification using supervised learning. Document-term matrices.

Class:

• Introduction to classification models (slides, Canvas):
  • Document classification
  • Supervised learning
  • Example: Internet Movie Database (IMDb) reviews (Maas et al, 2011)
  • Document-term matrices
• Check in about final projects.

Assignments:

• Respond to email feedback about initial draft of project proposal.
• For 4/10 W: Read Navarro 2017, Ch. 17, through the end of Section 17.2, about Bayesian statistics.
• For 4/10 W: Install the "e1071" package.
• For 4/10 W: Please try to install the "glmnet" package, and let me know in class if it works.
• For 4/24 W, 3 p.m.: First draft of project due.

2024.04.03.W

Topics: Sentiment analysis.

Class:

• Finish sentiment-analysis slides and in-class problem from last time
• Discuss Lab 6 on tidy text
• Start Lab 7 on sentiment analysis

Assignments:

• For 4/8 M, 3 p.m.: Lab 7 on sentiment analysis.
• Respond to email feedback about initial draft of project proposal.
• For 4/24 W, 3 p.m.: First draft of project due.

2024.04.01.M

Topics: Sentiment analysis.

Class:

• Sentiment
• The "bag-of-words" model
• Sentiment lexicons
• Using inner_join () to look text words up in a sentiment lexicon
• Checking the sentiment lexica against each other.
• (Time permitting:) Check-in about project proposals

Assignments:

• For 4/2 T (11 a.m.): Lab 6 on "tidy" text. Note unusual due date!
• For 4/3 W: Read Chapter 2 of Silge & Robinson's book
• For 4/3 W: Do Quiz 5 on that reading
• For 4/3 W (3 p.m.) Submit an initial proposal for the final project

2024.03.27.W

Topics: "Tidying" text.

Class:

• Finish examples from Chapter 1 of Silge & Robinson's book, in great detail.
• Important functions: unnest_tokens (), group_by (), anti_join (), bind_rows (), pivot_wider (), pivot_longer ().
• Start Lab 6 ("tidy" text).

Assignments:

• For 4/2 T (11 a.m.): Lab 6 on "tidy" text. Note unusual due date!
• For 4/3 W (3 p.m.): Submit an initial proposal for the final project.

2024.03.25.M

Topics: "Tidying" text

Class:

• How can we break a text document down into individual words?
• Go through examples from Chapter 1 of Silge & Robinson's book, in great detail.
• Important functions: unnest_tokens (), group_by (), anti_join (), bind_rows (), pivot_wider (), pivot_longer ().
• (Last 10--15 minutes) Go over Lab 5 on regular expressions.

Assignments:

• Before 3/27 W: Introduce yourself in the Discussions area of Canvas. This is optional, but it could help with the next assignment:
• For 3/27 W: Find two or three partners for your final-project group.
• For 4/3 W: Submit an initial proposal for the final project.

2024.03.20.W

Topics: Regular expressions.

Class:

• Slides on Canvas: Liquid co-occurrence in U.S. baby names (Martin, 2007, Ch. 3), exemplifying
  • Structure of a final project
  • Introduction to the tidyverse
  • Regular expressions for describing strings
• Examples of projects from last semester's LING 460.
• Some places to look for data sets:
  • Compendium of R data sets (more than just in the datasets package)
  • Words in the World
  • Experimental Linguistics in the Field
  • A page of psycholinguistic databases and miscellaneous resource
  • This list of text corpora (en.wikipedia.org)
  • Project Gutenberg, of which more next week
  • Web scraping (see Lab 5 and references therein)
• Start on Lab 5, regular expressions.

Assignments:

• For 3/25 M: Lab 5, regular expressions.
• For 3/25 M: Read Chapter 1 of Silge & Robinson's Text Mining with R: a tidy approach
• For 3/25 M: Install the gutenbergr package.
• Before 3/27 W: Introduce yourself in the Discussions area of Canvas. This is optional, but it could help with the next assignment:
• For 3/27 W: Find two or three partners for your final-project group.
• For 4/3 W: Submit an initial proposal for the final project.

2024.03.18.M

Topics: Final projects. Regular expressions.

Class:

• Slides on Canvas: Liquid co-occurrence in U.S. baby names (Martin, 2007, Ch. 3), exemplifying
  • Structure of a final project
  • Introduction to the tidyverse
  • Regular expressions for describing strings
  (This will likely take more than the whole period.)
• Go over the handout on final projects (on Canvas)

Assignment:

• For 3/20 W: Read the chapter on regular expressions in Wickham et al. 2023.
• Before 3/27 W: Introduce yourself in the Discussions area of Canvas. This is optional, but it could help with the next assignment:
• For 3/27 W: Find two or three partners for your final-project group.
• For 4/3 W: Submit an initial proposal for the final project.

2024.03.06.W

MIDTERM 1

• In this room, at this time, with computer
• Please see Midterm 1 Syllabus for details (Canvas > Modules > Course materials > "02.28.W-460MT1Syllabus2024Sp.pdf")

Assignment for 3/18 (M): Read Chapters 1--3 of Handling and Processing Strings in R, by Gaston Sanchez.

2024.03.04.M

Topics: Midterm review

Class:

• Go over questions about course content
• Readings?
• Labs?
• Any unanswered questions from the quizzes?

2024.02.28.W

Topics: Linear regression

Class:

• Linear associations
• The simple linear regression model (y = mx + b)
• Model fitting: ordinary least-squares regression
• Interpreting the fitted model

Announcenment: Midterm on Wednesday, March 6. A midterm syllabus will be unveiled on Canvas at the end of class.

2024.02.26.M

Topics: Tests of association between categorical variables

Class:

• Categori(c)al vs. scalar variables
• Association and independence
• Mosaic plots
• The chi-squared test of association
• Fisher's exact test
• Time permitting: Discuss Lab 4

Assignment:

• Reading: Navarro 2017
  • For 2/28 W: Chapter 15, through the end of Section 6
• For 2/28 W: Quiz 4, on Canvas

Announcement: Office hours on 2/27 Tues. will be later than usual (4:15--5:15 instead of 2-3) and by Zoom (link is on syllabus).

2024.02.21.W

Topics: Testing hypotheses about a population mean.

Class:

• General caveats about null-hypothesis significance testing (from last time's slides)
• Hypotheses about a population mean: t-tests
  • Assumptions
  • Worked example
  • In-class problem
  • Start on Lab 4.

Assignment for 2/26 M:

• Do Lab 4, comparison of reaction times in a synonymy-choice task

2024.02.19.M

Topics: Null-hypothesis significance testing

Class:

• Overview of NHST
• Logic of NHST
• Work through an example (binomial test)
• Do an in-class problem (chi-squared test for equal proportions)
• Discuss caveats

Assignment:

• Reading: Navarro 2017
  • For 2/21 W: Chapter 13, through the end of Section 3

2024.02.14.W

Topics: Confidence intervals

Class:

• Computing a z or t confidence interval for a sample from a normally-distributed population.
• Frequentist interpretation of confidence intervals
• Some misinterpretations of CIs.
• Factors determining the width of CIs. How much influence does the experimenter have?
• What if the population isn't normally-distributed?
• Other kinds of CIs.
• Uses of CIs

Assignment:

• No lab this week.
• Reading: Navarro 2017
  • For 2/19 M: Chapter 11, through the end of Section 7
  • For 2/21 W: Chapter 13, through the end of Section 3
• For 2/19 M: Quiz 3 (on Canvas -- will be up on Friday)

2024.02.07.W

Topics: Sampling theory.

Class:

• Statistical inference
• Sample statistics and the sampling distribution of the mean
• The Central Limit Theorem
• Start on Lab 3

Assignment for 2/14 W:

• Read Navarro 2017, Ch. 10.4 and 10.5. There is no associated quiz.
• Do Lab 3, sampling distributions and confidence intervals

Note: If the illustrations are not showing up for you in the Web-based version of Navarro 2017, a pdf can be found here.

2024.02.05.M

Topics: Frequentist vs. Bayesian statistics. Probability distributions. Samples.

Class:

• Inferential vs. descriptive statistics
• Frequentist vs. Bayesian inference
• Probability distributions. Examples:
  • Binomial
  • Normal
• Parameters and inference

Assignment: Read Navarro 2017, Ch. 10 through the end of 10.4. There is no associated quiz.

Note: If the illustrations are not showing up for you in the Web-based version of Navarro 2017, a pdf can be found here.

2024.01.31.W

Topics: Descriptive statistics: Variability

Class:

• Comments on Lab 1
  • Need to follow authors' account of what they did with the data. What observations did they exclude?
  • Use line breaks, indentation, mnemonic variable names, and in-line comments to make the code more human-readable (and hence more maintainable later on).
• Linear relationships and correlation (slides from last time)
• Start on Lab 2

Assignment for 2/5 M:

• Lab 2 (Canvas, Assignments)
• Read Navarro 2017, Ch. 9 through the end of 9.5. There is no associated quiz.

2024.01.29.M

Topics: Descriptive statistics

Class:

• Descriptive vs. inferential statistics
• Measures of central tendency
• Measures of variability
• Describing data grouping using formulas
• Collapsing within groups using aggregate ()
• Linear relationships and correlation

Assignment: Reading, from Navarro 2017: 5.1, 5.2, 5.4, 5.5, 5.7--5.10. Also: Quiz 2 (on Canvas).

2024.01.24.W

Topics: Data frames and basic visualization.

Class:

• Intro to data graphics in R (continued from last time; slides on Canvas)
• Just a few graphical parameters.
• Time permitting: A couple of useful data-summarization functions.
• Start on Lab 1.

Assignment: Lab 1

2024.01.22.M

Topics: Data frames. Start visualization.

Class:

• Data frames (continued from last time; slides on Canvas Modules)
• Intro to data visualization (slides; Canvas Modules)

Announcement: Office hours are T 2-3 and F 2:30-3:30

2024.01.17.W

Topics: R data types

Class:

• Comments on Lab 0
  • Functions are reusable code, so you can save effort by reusing them.
  • As we start seeing more complex code in our textbook, watch how it is formatted and commented to make it easier for humans to follow.
• Data types in R (slides, Canvas Modules).
• Time permitting: Start data frames (slides, Canvas Modules).

Assignment for 1/22 M:

• Reading, from Learning Statistics with R: Ch. 3.10 through end of Ch. 4
• Quiz 1 (Canvas Quizzes)

2024.01.10.W

Topics: Course organization. R and RStudio.

Class:

• Course organization
  • Syllabus
  • UNC Honor Code.
• Read script on preparedness in the classroom.
• Installing R and RStudio
• Start on Lab 0 (Canvas, Assignments).

Assignment for 1/17 W:

• Reading, from Learning Statistics with R (our free on-line textbook):
  • Ch. 1, Sections 1.1 and 1.2, on what statistics does for us
  • Ch. 3, Sections 3.1 through 3.9, about R
• Quiz 0 (Canvas, Quizzes)
• Lab 0 (Canvas, Assignments)