qss.Rmd

---
title: "Replication of Chapter 5 of _Quantitative Social Science: An Introduction_"
author: Stefan Müller and Kenneth Benoit
output: 
  html_document:
    toc: true
---

```{r, echo = FALSE}
knitr::opts_chunk$set(collapse = FALSE, 
                      comment = "##")
```

    
```{r, message = FALSE}
library("quanteda")
```


In this vignette we show how the **quanteda** package can be used to replicate the text analysis part (Chapter 5.1) from Kosuke Imai's book [*Quantitative Social Science: An Introduction*](http://qss.princeton.press) (Princeton: Princeton University Press, 2017).

## Download the Corpus

To get the textual data, you need to install and load the **qss** package first that comes with the book. 

```{r eval = FALSE}
devtools::install_github("kosukeimai/qss-package", build_vignettes = TRUE)
```

## Section 5.1.1: The Disputed Authorship of ‘The Federalist Papers’

First, we use the **readtext** package to import the Federalist Papers as a data frame and create a **quanteda** corpus. 

```{r}
library("readtext")
# use readtext package to import all documents as a dataframe
corpus_texts <- readtext(system.file("extdata/federalist/", package = "qss"))

# create docvar with number of paper
corpus_texts$paper_number <- paste("No.", seq_len(nrow(corpus_texts)), sep = " ")

# transform to a quanteda corpus object
corpus_raw <- corpus(corpus_texts, text_field = "text", docid_field = "paper_number")

# create docvar with authorship (used in Section  5.1.4)
docvars(corpus_raw, "paper_numeric") <- seq_len(ndoc(corpus_raw))

# create docvar with authorship (used in Section  5.1.4)
docvars(corpus_raw, "author") <- factor(NA, levels = c("madison", "hamilton"))
docvars(corpus_raw, "author")[c(1, 6:9, 11:13, 15:17, 21:36, 59:61, 65:85)] <- "hamilton"
docvars(corpus_raw, "author")[c(10, 14, 37:48, 58)] <- "madison"
```

```{r}
# inspect Paper No. 10 (output suppressed)
texts(corpus_raw)[10] %>% 
    stringi::stri_sub(1, 240) %>% 
    cat()
```


## Section 5.1.2: Document-Term Matrix

Next, we transform the corpus to a document-feature matrix. `dfm_prep` (used in sections 5.1.4 and 5.1.5) is a dfm in which numbers and punctuation have been removed, and in which terms have been converted to lowercase. In `dfm_papers`, the words have also been stemmed and a standard set of stopwords removed.

```{r}
# transform corpus to a document-feature matrix
dfm_prep <- tokens(corpus_raw, remove_numbers = TRUE, remove_punct = TRUE) %>%
  dfm(tolower = TRUE)

# remove stop words and stem words
dfm_papers <- dfm_prep %>%
  dfm_remove(stopwords("en")) %>%
  dfm_wordstem("en")

# inspect
dfm_papers

# sort into alphabetical order of features, to match book example
dfm_papers <- dfm_papers[, order(featnames(dfm_papers))]

# inspect some documents in the dfm
head(dfm_papers, nf = 8)
```

The **tm** package considers features such as "1st" to be numbers, whereas **quanteda** does not.  We can remove these easily using a wildcard removal:
```{r}
dfm_papers <- dfm_remove(dfm_papers, "[0-9]", valuetype = "regex", verbose = TRUE)
head(dfm_papers, nf = 8)
```


## Section 5.1.3: Topic Discovery

We can use the `textplot_wordcloud()` function to plot word clouds of the most frequent words in Papers 12 and 24.

```{r warning=FALSE, fig.width = 8, fig.height = 8}
set.seed(100)
library("quanteda.textplots")
textplot_wordcloud(dfm_papers[c("No. 12", "No. 24"), ], 
                   max.words = 50, comparison = TRUE)
```

Since **quanteda** cannot do stem completion, we will skip that part.

Next, we identify clusters of similar essay based on term frequency-inverse document frequency (*tf-idf*) and apply the $k$-means algorithm to the weighted dfm. 

```{r}
# tf-idf calculation
dfm_papers_tfidf <- dfm_tfidf(dfm_papers, base = 2)

# 10 most important words for Paper No. 12
topfeatures(dfm_papers_tfidf[12, ], n = 10)

# 10 most important words for Paper No. 24
topfeatures(dfm_papers_tfidf[24, ], n = 10)
```

We can match the clustering as follows:

```{r}
k <- 4  # number of clusters

# subset The Federalist papers written by Hamilton

dfm_papers_tfidf_hamilton <- dfm_subset(dfm_papers_tfidf, author == "hamilton")

# run k-means
km_out <- stats::kmeans(dfm_papers_tfidf_hamilton, centers = k)

km_out$iter # check the convergence; number of iterations may vary

colnames(km_out$centers) <- featnames(dfm_papers_tfidf_hamilton)

for (i in 1:k) { # loop for each cluster
  cat("CLUSTER", i, "\n")
  cat("Top 10 words:\n") # 10 most important terms at the centroid
  print(head(sort(km_out$centers[i, ], decreasing = TRUE), n = 10))
  cat("\n")
  cat("Federalist Papers classified: \n") # extract essays classified
  print(docnames(dfm_papers_tfidf_hamilton)[km_out$cluster == i])
  cat("\n")
}

```

## Section 5.1.4: Authorship Prediction

In a next step, we want to predict authorship for the Federalist Papers whose authorship is unknown. As the topics of the Papers differs remarkably, Imai focuses on 10 articles, prepositions and conjunctions to predict authorship.

```{r}
# term frequency per 1000 words
tfm <- dfm_weight(dfm_prep, "prop") * 1000

# select words of interest
words <- c("although", "always", "commonly", "consequently",
           "considerable", "enough", "there", "upon", "while", "whilst")
tfm <- dfm_select(tfm, words, valuetype = "fixed")

# average among Hamilton/Madison essays
tfm_ave <- dfm_group(dfm_subset(tfm, !is.na(author)), groups = author) /
  as.numeric(table(docvars(tfm, "author")))

# bind docvars from corpus and tfm to a data frame
author_data <- data.frame(docvars(corpus_raw), convert(tfm, to = "data.frame"))

# create numeric variable that takes value 1 for Hamilton's essays,
# -1 for Madison's essays and NA for the essays with unknown authorship
author_data$author_numeric <- ifelse(author_data$author == "hamilton", 1, 
                                     ifelse(author_data$author == "madison", -1, NA))

# use only known authors for training set
author_data_known <- na.omit(author_data)

hm_fit <- lm(author_numeric ~ upon + there + consequently + whilst,
             data = author_data_known)
hm_fit

hm_fitted <- fitted(hm_fit) # fitted values
sd(hm_fitted)
```

## Section 5.1.5: Cross-Validation

Finally, we assess how well the model fits the data by classifying each essay based on its fitted value.

```{r}
# proportion of correctly classified essays by Hamilton
mean(hm_fitted[author_data_known$author == "hamilton"] > 0)

# proportion of correctly classified essays by Madison
mean(hm_fitted[author_data_known$author == "madison"] < 0)

n <- nrow(author_data_known)
hm_classify <- rep(NA, n) # a container vector with missing values

for (i in 1:n) {
  # fit the model to the data after removing the ith observation
  sub_fit <- lm(author_numeric ~ upon + there + consequently + whilst,
                data = author_data_known[-i, ]) # exclude ith row
  # predict the authorship for the ith observation
  hm_classify[i] <- predict(sub_fit, newdata = author_data_known[i, ])
}

# proportion of correctly classified essays by Hamilton
mean(hm_classify[author_data_known$author == "hamilton"] > 0)

# proportion of correctly classified essays by Madison
mean(hm_classify[author_data_known$author == "madison"] < 0)

disputed <- c(49, 50:57, 62, 63) # 11 essays with disputed authorship
tf_disputed <- dfm_subset(tfm, is.na(author)) %>% 
    convert(to = "data.frame")

author_data$prediction <- predict(hm_fit, newdata = author_data)

author_data$prediction # predicted values
```

Finally, we plot the fitted values for each Federalist paper with the **ggplot2** package.

```{r, fig.width = 8, fig.height = 6}
author_data$author_plot <- ifelse(is.na(author_data$author), "unknown", as.character(author_data$author))

library(ggplot2)
ggplot(data = author_data, aes(x = paper_numeric, 
                               y = prediction, 
                               shape = author_plot, 
                               colour = author_plot)) +
    geom_point(size = 2) +
    geom_hline(yintercept = 0, linetype = "dotted") + 
    labs(x = "Federalist Papers", y = "Predicted values") +
    theme_minimal() + 
    theme(legend.title=element_blank())
```