Text analysis: supervised classification

Lecture 27

Dr. Benjamin Soltoff

Cornell University
INFO 2950 - Spring 2024

May 2, 2024

Announcements

Presentations tomorrow
Team projects due Tuesday
Homework 08 and slip days

Supervised text classification

Supervised learning

Hand-code a small set of documents \(N =\) 1,000
Train a machine learning model on the hand-coded data
Evaluate the effectiveness of the machine learning model
Apply the final model to the remaining set of documents \(N =\) 1,000,000

`USCongress`

Rows: 4,449
Columns: 7
$ ID       <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ cong     <dbl> 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 1…
$ billnum  <dbl> 4499, 4500, 4501, 4502, 4503, 4504, 4505, 4506, 4507, 4508, 4…
$ h_or_sen <chr> "HR", "HR", "HR", "HR", "HR", "HR", "HR", "HR", "HR", "HR", "…
$ major    <dbl> 18, 18, 18, 18, 5, 21, 15, 18, 18, 18, 18, 16, 18, 12, 2, 3, …
$ text     <chr> "To suspend temporarily the duty on Fast Magenta 2 Stage.", "…
$ label    <fct> "Foreign trade", "Foreign trade", "Foreign trade", "Foreign t…

[1] "To suspend temporarily the duty on Fast Magenta 2 Stage."                                                                                                                                                                                
[2] "To suspend temporarily the duty on Fast Black 286 Stage."                                                                                                                                                                                
[3] "To suspend temporarily the duty on mixtures of Fluazinam."                                                                                                                                                                               
[4] "To reduce temporarily the duty on Prodiamine Technical."                                                                                                                                                                                 
[5] "To amend the Immigration and Nationality Act in regard to Caribbean-born immigrants."                                                                                                                                                    
[6] "To amend title 38, United States Code, to extend the eligibility for housing loans guaranteed by the Secretary of Veterans Affairs under the Native American Housing Loan Pilot Program to veterans who are married to Native Americans."

Split the data set

set.seed(123)

# convert response variable to factor
congress <- congress |>
  mutate(major = factor(x = major, levels = major, labels = label))

# split into training and testing sets
congress_split <- initial_split(data = congress, strata = major, prop = .8)
congress_split

<Training/Testing/Total>
<3558/891/4449>

congress_train <- training(congress_split)
congress_test <- testing(congress_split)

# generate cross-validation folds
congress_folds <- vfold_cv(data = congress_train, strata = major)

Class imbalance

Preprocessing the data frame

library(textrecipes)
congress_rec <- recipe(major ~ text, data = congress_train) |>
  step_tokenize(text) |>
  step_stopwords(text) |>
  step_tokenfilter(text, max_tokens = 500) |>
  step_tfidf(text) |>
  step_downsample(major)

Review the preprocessed data frame

prep(congress_rec) |> bake(new_data = NULL)

# A tibble: 540 × 501
   major  tfidf_text_1 tfidf_text_10 tfidf_text_11 tfidf_text_18 tfidf_text_1965
   <fct>         <dbl>         <dbl>         <dbl>         <dbl>           <dbl>
 1 Forei…            0             0             0             0               0
 2 Forei…            0             0             0             0               0
 3 Forei…            0             0             0             0               0
 4 Forei…            0             0             0             0               0
 5 Forei…            0             0             0             0               0
 6 Forei…            0             0             0             0               0
 7 Forei…            0             0             0             0               0
 8 Forei…            0             0             0             0               0
 9 Forei…            0             0             0             0               0
10 Forei…            0             0             0             0               0
# ℹ 530 more rows
# ℹ 495 more variables: tfidf_text_1974 <dbl>, tfidf_text_1986 <dbl>,
#   tfidf_text_2 <dbl>, tfidf_text_2000 <dbl>, tfidf_text_2001 <dbl>,
#   tfidf_text_2002 <dbl>, tfidf_text_2003 <dbl>, tfidf_text_23 <dbl>,
#   tfidf_text_3 <dbl>, tfidf_text_30 <dbl>, tfidf_text_38 <dbl>,
#   tfidf_text_4 <dbl>, tfidf_text_49 <dbl>, tfidf_text_5 <dbl>,
#   tfidf_text_abuse <dbl>, tfidf_text_access <dbl>, …

Establish a baseline

null_classification <- null_model() |>
  set_engine("parsnip") |>
  set_mode("classification")

null_cv <- workflow() |>
  add_recipe(congress_rec) |>
  add_model(null_classification) |>
  fit_resamples(
    congress_folds
  )

null_cv |>
  collect_metrics()

# A tibble: 2 × 6
  .metric  .estimator   mean     n std_err .config             
  <chr>    <chr>       <dbl> <int>   <dbl> <chr>               
1 accuracy multiclass 0.0899    10 0.00396 Preprocessor1_Model1
2 roc_auc  hand_till  0.5       10 0       Preprocessor1_Model1

Define the model

tree_spec <- decision_tree() |>
  set_mode("classification") |>
  set_engine("C5.0")

tree_spec

Decision Tree Model Specification (classification)

Computational engine: C5.0

Train the model

tree_wf <- workflow() |>
  add_recipe(congress_rec) |>
  add_model(tree_spec)

set.seed(123)

tree_cv <- fit_resamples(
  tree_wf,
  congress_folds,
  control = control_resamples(
    save_pred = TRUE,
    save_workflow = TRUE
  )
)

tree_cv_metrics <- collect_metrics(tree_cv)
tree_cv_predictions <- collect_predictions(tree_cv)
tree_cv_metrics

# A tibble: 3 × 6
  .metric     .estimator  mean     n std_err .config             
  <chr>       <chr>      <dbl> <int>   <dbl> <chr>               
1 accuracy    multiclass 0.432    10 0.00689 Preprocessor1_Model1
2 brier_class multiclass 0.401    10 0.00440 Preprocessor1_Model1
3 roc_auc     hand_till  0.766    10 0.00706 Preprocessor1_Model1

Confusion matrix

Feature importance

Application exercise

`ae-25`

Go to the course GitHub org and find your ae-25 (repo name will be suffixed with your GitHub name).
Clone the repo in RStudio Workbench, open the Quarto document in the repo, and follow along and complete the exercises.
Render, commit, and push your edits by the AE deadline – end of tomorrow

Recap

Supervised ML with text features does not have to be that different from ordinary ML
Need to quantify text into numeric variables through tokenization and other processes
Tokenization can produce a substantial number of features – unless computing power is unlimited, you will need to restrict the number of variables using some methodology
Use variable importance measures to assess how important specific tokens are to the predictive model

Do we really need this?