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<!DOCTYPE html>
<html>
<head>
<title>Text Modeling</title>
<meta charset="utf-8">
<meta name="author" content="Julia Silge | Deming Conference | 4 Dec 2018" />
<link href="libs/remark-css/default.css" rel="stylesheet" />
<script src="https://use.fontawesome.com/5235085b15.js"></script>
<link rel="stylesheet" href="css/xaringan-themer.css" type="text/css" />
<link rel="stylesheet" href="css/footer_plus.css" type="text/css" />
</head>
<body>
<textarea id="source">
layout: true
<div class="my-footer"><span>bit.ly/silge-deming-2</span></div>
---
class: inverse, center, middle
background-image: url(figs/p_and_p_cover.png)
background-size: cover
# Text Modeling
<img src="figs/blue_jane.png" width="150px"/>
### USING TIDY PRINCIPLES
.large[Julia Silge | Deming Conference | 4 Dec 2018]
---
## Let's install some packages
```r
install.packages(c("tidyverse",
"tidytext",
"gutenbergr",
"stm",
"glmnet"))
```
---
class: right, middle
<img src="figs/blue_jane.png" width="150px"/>
# Find me at...
<a href="http://twitter.com/juliasilge"><i class="fa fa-twitter fa-fw"></i>&nbsp; @juliasilge</a><br>
<a href="http://github.com/juliasilge"><i class="fa fa-github fa-fw"></i>&nbsp; @juliasilge</a><br>
<a href="https://juliasilge.com"><i class="fa fa-link fa-fw"></i>&nbsp; juliasilge.com</a><br>
<a href="https://tidytextmining.com"><i class="fa fa-book fa-fw"></i>&nbsp; tidytextmining.com</a><br>
<a href="mailto:[email protected]"><i class="fa fa-paper-plane fa-fw"></i>&nbsp; [email protected]</a>
---
class: right, inverse, middle
background-image: url(figs/p_and_p_cover.png)
background-size: cover
# TIDYING AND CASTING
<h1 class="fa fa-check-circle fa-fw"></h1>
---
background-image: url(figs/tmwr_0601.png)
background-size: 900px
---
class: inverse
background-image: url(figs/p_and_p_cover.png)
background-size: cover
# Two powerful NLP techniques
--
- .large[Topic modeling]
--
- .large[Text classification]
---
class: inverse
background-image: url(figs/p_and_p_cover.png)
background-size: cover
# Topic modeling
- .large[Each DOCUMENT = mixture of topics]
--
- .large[Each TOPIC = mixture of words]
---
class: top
background-image: url(figs/top_tags-1.png)
background-size: 800px
---
class: center, middle, inverse
background-image: url(figs/p_and_p_cover.png)
background-size: cover
# GREAT LIBRARY HEIST 🕵️♂️
---
## **Downloading your text data**
```r
library(tidyverse)
library(gutenbergr)
titles <- c("Twenty Thousand Leagues under the Sea",
"The War of the Worlds",
"Pride and Prejudice",
"Great Expectations")
books <- gutenberg_works(title %in% titles) %>%
gutenberg_download(meta_fields = "title")
books
```
```
## # A tibble: 51,663 x 3
## gutenberg_id text title
## <int> <chr> <chr>
## 1 36 The War of the Worlds The War of the…
## 2 36 "" The War of the…
## 3 36 by H. G. Wells [1898] The War of the…
## 4 36 "" The War of the…
## 5 36 "" The War of the…
## 6 36 " But who shall dwell in these worlds… The War of the…
## 7 36 " inhabited? . . . Are we or they … The War of the…
## 8 36 " World? . . . And how are all thi… The War of the…
## 9 36 " KEPLER (quoted in The Anatomy … The War of the…
## 10 36 "" The War of the…
## # ... with 51,653 more rows
```
---
## **Someone has torn your books apart!** 😭
```r
by_chapter <- books %>%
group_by(title) %>%
mutate(chapter = cumsum(str_detect(text,
regex("^chapter ",
ignore_case = TRUE)))) %>%
ungroup() %>%
filter(chapter > 0) %>%
unite(document, title, chapter)
by_chapter
```
```
## # A tibble: 51,602 x 3
## gutenberg_id text document
## <int> <chr> <chr>
## 1 36 CHAPTER ONE The War of the …
## 2 36 "" The War of the …
## 3 36 THE EVE OF THE WAR The War of the …
## 4 36 "" The War of the …
## 5 36 "" The War of the …
## 6 36 No one would have believed in the last y… The War of the …
## 7 36 century that this world was being watche… The War of the …
## 8 36 intelligences greater than man's and yet… The War of the …
## 9 36 men busied themselves about their variou… The War of the …
## 10 36 scrutinised and studied, perhaps almost … The War of the …
## # ... with 51,592 more rows
```
---
## **Can we put them back together?**
```r
library(tidytext)
word_counts <- by_chapter %>%
* unnest_tokens(word, text) %>%
anti_join(get_stopwords(source = "smart")) %>%
count(document, word, sort = TRUE)
word_counts
```
```
## # A tibble: 111,650 x 3
## document word n
## <chr> <chr> <int>
## 1 Great Expectations_57 joe 88
## 2 Great Expectations_7 joe 70
## 3 Pride and Prejudice_18 mr 66
## 4 Great Expectations_17 biddy 63
## 5 Great Expectations_27 joe 58
## 6 Great Expectations_38 estella 58
## 7 Great Expectations_2 joe 56
## 8 Great Expectations_23 pocket 53
## 9 Great Expectations_15 joe 50
## 10 Great Expectations_18 joe 50
## # ... with 111,640 more rows
```
---
## **Can we put them back together?**
```r
words_sparse <- word_counts %>%
* cast_sparse(document, word, n)
class(words_sparse)
```
```
## [1] "dgCMatrix"
## attr(,"package")
## [1] "Matrix"
```
---
## **Train a topic model**
Use a sparse matrix or a `quanteda::dfm` object as input
```r
library(stm)
topic_model <- stm(words_sparse, K = 4,
verbose = FALSE, init.type = "Spectral")
summary(topic_model)
```
```
## A topic model with 4 topics, 193 documents and a 18360 word dictionary.
```
```
## Topic 1 Top Words:
## Highest Prob: mr, elizabeth, mrs, darcy, bennet, miss, jane
## FREX: elizabeth, darcy, bennet, bingley, wickham, collins, lydia
## Lift: wickham, nephew, phillips, brighton, meryton, bourgh, mend
## Score: elizabeth, darcy, bennet, bingley, wickham, jane, lydia
## Topic 2 Top Words:
## Highest Prob: captain, nautilus, sea, nemo, ned, conseil, land
## FREX: nautilus, nemo, ned, conseil, canadian, ocean, seas
## Lift: vanikoro, indian, d'urville, reefs, scotia, shark's, solidification
## Score: nautilus, nemo, ned, conseil, canadian, ocean, captain
## Topic 3 Top Words:
## Highest Prob: mr, joe, miss, time, pip, looked, herbert
## FREX: joe, pip, herbert, wemmick, havisham, estella, biddy
## Lift: towel, giv, whimple, meantersay, jew, rot, barnard's
## Score: joe, wemmick, pip, jaggers, havisham, estella, herbert
## Topic 4 Top Words:
## Highest Prob: people, martians, man, time, black, men, night
## FREX: martians, martian, woking, mars, curate, pine, ulla
## Lift: martians, mars, curate, shepperton, henderson, hood, ripley
## Score: martians, martian, woking, cylinder, curate, ulla, pine
```
---
## **Exploring the output of topic modeling**
.large[Time for tidying!]
```r
chapter_topics <- tidy(topic_model, matrix = "beta")
chapter_topics
```
```
## # A tibble: 73,440 x 3
## topic term beta
## <int> <chr> <dbl>
## 1 1 joe 8.69e-104
## 2 2 joe 3.03e-139
## 3 3 joe 1.21e- 2
## 4 4 joe 3.28e- 19
## 5 1 mr 1.90e- 2
## 6 2 mr 1.91e- 4
## 7 3 mr 1.22e- 2
## 8 4 mr 1.15e- 45
## 9 1 biddy 3.21e- 80
## 10 2 biddy 3.84e-149
## # ... with 73,430 more rows
```
---
## **Exploring the output of topic modeling**
```r
top_terms <- chapter_topics %>%
group_by(topic) %>%
top_n(10, beta) %>%
ungroup() %>%
arrange(topic, -beta)
top_terms
```
```
## # A tibble: 40 x 3
## topic term beta
## <int> <chr> <dbl>
## 1 1 mr 0.0190
## 2 1 elizabeth 0.0141
## 3 1 mrs 0.00886
## 4 1 darcy 0.00881
## 5 1 bennet 0.00694
## 6 1 miss 0.00674
## 7 1 jane 0.00652
## 8 1 bingley 0.00607
## 9 1 time 0.00493
## 10 1 good 0.00480
## # ... with 30 more rows
```
---
## **Exploring the output of topic modeling**
```r
top_terms %>%
mutate(term = reorder(term, beta)) %>%
ggplot(aes(term, beta, fill = factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
coord_flip()
```
---
<!-- -->
---
## **How are documents classified?**
```r
chapters_gamma <- tidy(topic_model, matrix = "gamma",
document_names = rownames(words_sparse))
chapters_gamma
```
```
## # A tibble: 772 x 3
## document topic gamma
## <chr> <int> <dbl>
## 1 Great Expectations_57 1 0.000792
## 2 Great Expectations_7 1 0.00340
## 3 Pride and Prejudice_18 1 1.000
## 4 Great Expectations_17 1 0.0480
## 5 Great Expectations_27 1 0.000367
## 6 Great Expectations_38 1 0.00110
## 7 Great Expectations_2 1 0.000531
## 8 Great Expectations_23 1 0.432
## 9 Great Expectations_15 1 0.000565
## 10 Great Expectations_18 1 0.000277
## # ... with 762 more rows
```
---
## **How are documents classified?**
```r
chapters_parsed <- chapters_gamma %>%
separate(document, c("title", "chapter"),
sep = "_", convert = TRUE)
chapters_parsed
```
```
## # A tibble: 772 x 4
## title chapter topic gamma
## <chr> <int> <int> <dbl>
## 1 Great Expectations 57 1 0.000792
## 2 Great Expectations 7 1 0.00340
## 3 Pride and Prejudice 18 1 1.000
## 4 Great Expectations 17 1 0.0480
## 5 Great Expectations 27 1 0.000367
## 6 Great Expectations 38 1 0.00110
## 7 Great Expectations 2 1 0.000531
## 8 Great Expectations 23 1 0.432
## 9 Great Expectations 15 1 0.000565
## 10 Great Expectations 18 1 0.000277
## # ... with 762 more rows
```
---
## **How are documents classified?**
```r
chapters_parsed %>%
mutate(title = reorder(title, gamma * topic)) %>%
ggplot(aes(factor(topic), gamma)) +
geom_boxplot() +
facet_wrap(~ title)
```
---
<!-- -->
---
class: center, middle, inverse
background-image: url(figs/p_and_p_cover.png)
background-size: cover
# GOING FARTHER 🚀
---
## Tidying model output
### Which words in each document are assigned to which topics?
- .large[`augment()`]
- .large[Add information to each observation in the original data]
---
background-image: url(figs/stm_video.png)
background-size: 850px
---
## **Using stm**
- .large[Document-level covariates]
```r
topic_model <- stm(words_sparse, K = 0, init.type = "Spectral",
prevalence = ~s(Year),
data = covariates,
verbose = FALSE)
```
- .large[Use functions for `semanticCoherence()`, `checkResiduals()`, `exclusivity()`, and more!]
- .large[Check out http://www.structuraltopicmodel.com/]
- .large[See [my recent blog post](https://juliasilge.com/blog/evaluating-stm/) for how to choose `K`, the number of topics]
---
background-image: url(figs/model_diagnostic-1.png)
background-position: 50% 50%
background-size: 950px
---
# Stemming?
.large[Advice from [Schofield & Mimno](https://mimno.infosci.cornell.edu/papers/schofield_tacl_2016.pdf)]
.large["Comparing Apples to Apple: The Effects of Stemmers on Topic Models"]
---
class: right, middle
<h1 class="fa fa-quote-left fa-fw"></h1>
<h2> Despite their frequent use in topic modeling, we find that stemmers produce no meaningful improvement in likelihood and coherence and in fact can degrade topic stability. </h2>
<h1 class="fa fa-quote-right fa-fw"></h1>
---
class: right, middle, inverse
background-image: url(figs/p_and_p_cover.png)
background-size: cover
# TEXT CLASSIFICATION
<h1 class="fa fa-balance-scale fa-fw"></h1>
---
## **Downloading your text data**
```r
library(tidyverse)
library(gutenbergr)
titles <- c("The War of the Worlds",
"Pride and Prejudice")
books <- gutenberg_works(title %in% titles) %>%
gutenberg_download(meta_fields = "title") %>%
mutate(document = row_number())
books
```
```
## # A tibble: 19,504 x 4
## gutenberg_id text title document
## <int> <chr> <chr> <int>
## 1 36 The War of the Worlds The War of t… 1
## 2 36 "" The War of t… 2
## 3 36 by H. G. Wells [1898] The War of t… 3
## 4 36 "" The War of t… 4
## 5 36 "" The War of t… 5
## 6 36 " But who shall dwell in these… The War of t… 6
## 7 36 " inhabited? . . . Are we o… The War of t… 7
## 8 36 " World? . . . And how are … The War of t… 8
## 9 36 " KEPLER (quoted in The A… The War of t… 9
## 10 36 "" The War of t… 10
## # ... with 19,494 more rows
```
---
## **Making a tidy dataset**
.large[Use this kind of data structure for EDA! 💅]
```r
library(tidytext)
tidy_books <- books %>%
* unnest_tokens(word, text) %>%
group_by(word) %>%
filter(n() > 10) %>%
ungroup
tidy_books
```
```
## # A tibble: 159,707 x 4
## gutenberg_id title document word
## <int> <chr> <int> <chr>
## 1 36 The War of the Worlds 1 the
## 2 36 The War of the Worlds 1 war
## 3 36 The War of the Worlds 1 of
## 4 36 The War of the Worlds 1 the
## 5 36 The War of the Worlds 3 by
## 6 36 The War of the Worlds 6 but
## 7 36 The War of the Worlds 6 who
## 8 36 The War of the Worlds 6 shall
## 9 36 The War of the Worlds 6 in
## 10 36 The War of the Worlds 6 these
## # ... with 159,697 more rows
```
---
## **Cast to a sparse matrix**
.large[And build a dataframe with a response variable]
```r
sparse_words <- tidy_books %>%
count(document, word, sort = TRUE) %>%
* cast_sparse(document, word, n)
books_joined <- data_frame(document = as.integer(rownames(sparse_words))) %>%
left_join(books %>%
select(document, title))
```
---
## **Train a glmnet model**
```r
library(glmnet)
library(doMC)
registerDoMC(cores = 8)
is_jane <- books_joined$title == "Pride and Prejudice"
model <- cv.glmnet(sparse_words, is_jane, family = "binomial",
parallel = TRUE, keep = TRUE)
```
---
## **Tidying our model**
.large[Tidy, then filter to choose some lambda from glmnet output]
```r
library(broom)
coefs <- model$glmnet.fit %>%
tidy() %>%
filter(lambda == model$lambda.1se)
Intercept <- coefs %>%
filter(term == "(Intercept)") %>%
pull(estimate)
```
---
## **Tidying our model**
```r
classifications <- tidy_books %>%
inner_join(coefs, by = c("word" = "term")) %>%
group_by(document) %>%
summarize(Score = sum(estimate)) %>%
mutate(Probability = plogis(Intercept + Score))
classifications
```
```
## # A tibble: 16,007 x 3
## document Score Probability
## <int> <dbl> <dbl>
## 1 1 -2.41 0.101
## 2 3 0.216 0.610
## 3 6 1.86 0.890
## 4 7 -1.05 0.305
## 5 8 -1.29 0.258
## 6 9 -0.535 0.424
## 7 13 -0.252 0.495
## 8 15 -5.66 0.00435
## 9 19 0.393 0.651
## 10 21 -2.41 0.101
## # ... with 15,997 more rows
```
---
## **Understanding our model**
```r
coefs %>%
group_by(estimate > 0) %>%
top_n(10, abs(estimate)) %>%
ungroup %>%
ggplot(aes(fct_reorder(term, estimate), estimate, fill = estimate > 0)) +
geom_col(show.legend = FALSE) +
coord_flip()
```
---
<!-- -->
---
## **ROC**
```r
comment_classes <- classifications %>%
left_join(books %>%
select(title, document), by = "document") %>%
mutate(Correct = case_when(title == "Pride and Prejudice" ~ TRUE,
TRUE ~ FALSE))
roc <- comment_classes %>%
arrange(desc(Probability)) %>%
mutate(TPR = cumsum(Correct) / sum(Correct),
FPR = cumsum(!Correct) / sum(!Correct),
FDR = cummean(!Correct))
```
---
## **ROC**
```r
roc %>%
arrange(Probability)
```
```
## # A tibble: 16,007 x 8
## document Score Probability title Correct TPR FPR FDR
## <int> <dbl> <dbl> <chr> <lgl> <dbl> <dbl> <dbl>
## 1 3963 -14.8 0.000000479 The War of the Wo… FALSE 1 1 0.337
## 2 4127 -13.3 0.00000204 The War of the Wo… FALSE 1 1.000 0.337
## 3 6431 -13.0 0.00000293 The War of the Wo… FALSE 1 1.000 0.337
## 4 2863 -12.7 0.00000374 The War of the Wo… FALSE 1 0.999 0.336
## 5 2570 -12.7 0.00000401 The War of the Wo… FALSE 1 0.999 0.336
## 6 2628 -12.4 0.00000513 The War of the Wo… FALSE 1 0.999 0.336
## 7 4177 -12.1 0.00000670 The War of the Wo… FALSE 1 0.999 0.336
## 8 6042 -12.1 0.00000700 The War of the Wo… FALSE 1 0.999 0.336
## 9 2704 -12.1 0.00000725 The War of the Wo… FALSE 1 0.999 0.336
## 10 1698 -11.9 0.00000849 The War of the Wo… FALSE 1 0.998 0.336
## # ... with 15,997 more rows
```
---
<!-- -->
---
## **AUC for model**
```r
roc %>%
summarise(AUC = sum(diff(FPR) * na.omit(lead(TPR) + TPR)) / 2)
```
```
## # A tibble: 1 x 1
## AUC
## <dbl>
## 1 0.991
```
---
## **Misclassifications**
Let's talk about misclassifications. Which documents here were incorrectly predicted to be written by Jane Austen?
```r
roc %>%
* filter(Probability > .8, !Correct) %>%
sample_n(10) %>%
inner_join(books %>%
select(document, text)) %>%
select(Probability, text)
```
```
## # A tibble: 10 x 2
## Probability text
## <dbl> <chr>
## 1 0.977 should be prepared. It seems to me that it should be poss…
## 2 0.842 certain further details which, although they were not all …
## 3 0.903 the innkeeper, she would, I think, have urged me to stay in
## 4 0.915 particular. Micro-organisms, which cause so much disease …
## 5 0.860 "\"There won't be any more blessed concerts for a million …
## 6 0.885 She turned without a word--they were both panting--and the…
## 7 0.826 decorum were necessarily different from ours; and not only…
## 8 0.894 her.
## 9 0.911 "\"Half a mile, you say?\" said he."
## 10 0.963 I cannot but regret, now that I am concluding my story, ho…
```
---
## **Misclassifications**
Let's talk about misclassifications. Which documents here were incorrectly predicted to *not* be written by Jane Austen?
```r
roc %>%
* filter(Probability < .2, Correct) %>%
sample_n(10) %>%
inner_join(books %>%
select(document, text)) %>%
select(Probability, text)
```
```
## # A tibble: 10 x 2
## Probability text
## <dbl> <chr>
## 1 0.184 is so violent, that it would be the death of half the good…
## 2 0.164 I was never more annoyed! The insipidity, and yet the nois…
## 3 0.177 glancing over it, said, in a colder voice:
## 4 0.140 occasional appearance of some trout in the water, and talk…
## 5 0.155 "as I sit by the fire.\""
## 6 0.159 road, the house standing in it, the green pales, and the l…
## 7 0.195 half-a-mile, and then found themselves at the top of a con…
## 8 0.0206 window that he wore a blue coat, and rode a black horse.
## 9 0.191 struck with the action of doing a very gallant thing, and …
## 10 0.113 the happiest of men.
```
---
background-image: url(figs/tmwr_0601.png)
background-position: 50% 70%
background-size: 750px
## **Workflow for text mining/modeling**
---
background-image: url(figs/lizzieskipping.gif)
background-position: 50% 55%
background-size: 750px
# **Go explore real-world text!**
---
class: left, middle
<img src="figs/blue_jane.png" width="150px"/>
# Thanks!
<a href="http://twitter.com/juliasilge"><i class="fa fa-twitter fa-fw"></i>&nbsp; @juliasilge</a><br>
<a href="http://github.com/juliasilge"><i class="fa fa-github fa-fw"></i>&nbsp; @juliasilge</a><br>
<a href="https://juliasilge.com"><i class="fa fa-link fa-fw"></i>&nbsp; juliasilge.com</a><br>
<a href="https://tidytextmining.com"><i class="fa fa-book fa-fw"></i>&nbsp; tidytextmining.com</a><br>
<a href="mailto:[email protected]"><i class="fa fa-paper-plane fa-fw"></i>&nbsp; [email protected]</a>
Slides created with [**remark.js**](http://remarkjs.com/) and the R package [**xaringan**](https://github.com/yihui/xaringan)
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