7 Week 7: A Primer on Supervised Learning
Slides
- 7 Supervised Learning (link to slides)
7.1 Setup
As always, we first load the packages that we’ll be using:
# devtools::install_github("conjugateprior/austin")
library(austin) # just for those sweet wordscores
library(tidyverse) # for wrangling data
library(tidylog) # to know what we are wrangling
library(tidytext) # for 'tidy' manipulation of text data
library(quanteda) # tokenization power house
library(quanteda.textmodels)
library(wesanderson) # to prettify7.2 Wordscores
Laver et al. (2003) propose a supervised scaling technique called Wordscores. We learned the intuition in this week’s lecture. We will now replicate Table 1 from Laver and Benoit (2003) using the austin package. The package includes sample data that we will use:
data(lbg)Let’s keep only the reference documents:
ref <- getdocs(lbg, 1:5)
ref## docs
## words R1 R2 R3 R4 R5
## A 2 0 0 0 0
## B 3 0 0 0 0
## C 10 0 0 0 0
## D 22 0 0 0 0
## E 45 0 0 0 0
## F 78 2 0 0 0
## G 115 3 0 0 0
## H 146 10 0 0 0
## I 158 22 0 0 0
## J 146 45 0 0 0
## K 115 78 2 0 0
## L 78 115 3 0 0
## M 45 146 10 0 0
## N 22 158 22 0 0
## O 10 146 45 0 0
## P 3 115 78 2 0
## Q 2 78 115 3 0
## R 0 45 146 10 0
## S 0 22 158 22 0
## T 0 10 146 45 0
## U 0 3 115 78 2
## V 0 2 78 115 3
## W 0 0 45 146 10
## X 0 0 22 158 22
## Y 0 0 10 146 45
## Z 0 0 3 115 78
## ZA 0 0 2 78 115
## ZB 0 0 0 45 146
## ZC 0 0 0 22 158
## ZD 0 0 0 10 146
## ZE 0 0 0 3 115
## ZF 0 0 0 2 78
## ZG 0 0 0 0 45
## ZH 0 0 0 0 22
## ZI 0 0 0 0 10
## ZJ 0 0 0 0 3
## ZK 0 0 0 0 2This is the same matrix as in Figure 1, where we have a count of each word (in this case, letters) by reference document (i.e., documents that have already been labeled). We can assign scores (A_scores) to each reference text to place them on an ideological scale (or whatever scale we want). We then estimate Wordscores for each word.
# We do this in the order of the reference texts:
A_score <- c(-1.5,-0.75,0,0.75,1.5)
ws <- classic.wordscores(ref, scores=A_score)
ws$pi## Score
## A -1.5000000
## B -1.5000000
## C -1.5000000
## D -1.5000000
## E -1.5000000
## F -1.4812500
## G -1.4809322
## H -1.4519231
## I -1.4083333
## J -1.3232984
## K -1.1846154
## L -1.0369898
## M -0.8805970
## N -0.7500000
## O -0.6194030
## P -0.4507576
## Q -0.2992424
## R -0.1305970
## S 0.0000000
## T 0.1305970
## U 0.2992424
## V 0.4507576
## W 0.6194030
## X 0.7500000
## Y 0.8805970
## Z 1.0369898
## ZA 1.1846154
## ZB 1.3232984
## ZC 1.4083333
## ZD 1.4519231
## ZE 1.4809322
## ZF 1.4812500
## ZG 1.5000000
## ZH 1.5000000
## ZI 1.5000000
## ZJ 1.5000000
## ZK 1.5000000Now we get the virgin text and predict the textscore by estimating the average of the weighted wordscores for the virgin document:
vir <- getdocs(lbg, 'V1')
vir## docs
## words V1
## A 0
## B 0
## C 0
## D 0
## E 0
## F 0
## G 0
## H 2
## I 3
## J 10
## K 22
## L 45
## M 78
## N 115
## O 146
## P 158
## Q 146
## R 115
## S 78
## T 45
## U 22
## V 10
## W 3
## X 2
## Y 0
## Z 0
## ZA 0
## ZB 0
## ZC 0
## ZD 0
## ZE 0
## ZF 0
## ZG 0
## ZH 0
## ZI 0
## ZJ 0
## ZK 0
# predict textscores for the virgin documents
predict(ws, newdata=vir)## 37 of 37 words (100%) are scorable
##
## Score Std. Err. Rescaled Lower Upper
## V1 -0.448 0.0119 -0.448 -0.459 -0.437Cool.