Encoding
Before we start with with text as data, a quick word on ENCODING. R needs to know how data is encoded to understand it properly. Some (non-English) letters are not supported through all encodings. Mostly, we will work with the standard UTF-8 encoding, which works well for English. In fact, we will get rid of all characters that do not fit into UTF-8.
scc$text <- iconv(scc$text, "ASCII", "UTF-8",sub='')
Keep in mind that when working with other languages, the encoding should be adjusted.
# When working with French texts, for instance, use the following encoding:
scc$text <- iconv(scc$text, from="UTF-8", to="LATIN1")
Creation of a Text Corpus and Text Pre-processing
In this lesson, we return to our Supreme Court of Canada data and convert the judgments' text into data. To do that we, download and activate the tm package (one of several R packages that allow turning text into data) and create a corpus object.
# Create a corpus from the text.
corpus <- VCorpus(VectorSource(scc$text))
We should then do some processing on our corpus. There is some variation in our text that, for most tasks, we are not interested in. For instance, if we are interested in understanding the content of our corpus, it will not matter for our analysis whether a word is capitalised or not.
At the same time, pre-processing text is not a value-neutral exercise and it may affect your results. You thus need to be mindful what you do.
There are two ways to think about pre-processing choices. First, you could run the same analysis with different pre-processing combinations to make sure that your results are robust and do not vary strongly due to your pre-processing decisions. Denny and Spirling provide some useful guidance on this. Second, you could approach pre-processing from a more theoretical perspective and justify your pre-processing choices on conceptual grounds: "is the variation in the text that I am interested in for this particular task meaningfully distorted by this pre-processing step?"
# Here, we focus on getting rid of variation that we don't consider conceptually meaningful for investigating content variation in our SCC texts.
# Removing punctuation.
corpus <- tm_map(corpus, removePunctuation)
# Lowercasing letters.
corpus <- tm_map(corpus, content_transformer(tolower))
# Eliminating trailing white space.
corpus <- tm_map(corpus, stripWhitespace)
# Removing numbers.
corpus <- tm_map(corpus, removeNumbers)
A more controversial aspect is the removal of stopwords. Stopwords include "and", "or", "over", "our" etc. Take a look.
##
[1] "i" "me" "my" "myself" "we" "our" "ours" "ourselves"
[9] "you" "your" "yours" "yourself" "yourselves" "he" "him" "his"
[17] "himself" "she" "her" "hers" "herself" "it" "its" "itself"
[25] "they" "them" "their" "theirs" "themselves" "what" "which" "who"
[33] "whom" "this" "that" "these" "those" "am" "is" "are"
[41] "was" "were" "be" "been" "being" "have" "has" "had"
[49] "having" "do" "does" "did" "doing" "would" "should" "could"
[57] "ought" "i'm" "you're" "he's" "she's" "it's" "we're" "they're"
[65] "i've" "you've" "we've" "they've" "i'd" "you'd" "he'd" "she'd"
[73] "we'd" "they'd" "i'll" "you'll" "he'll" "she'll" "we'll" "they'll"
[81] "isn't" "aren't" "wasn't" "weren't" "hasn't" "haven't" "hadn't" "doesn't"
[89] "don't" "didn't" "won't" "wouldn't" "shan't" "shouldn't" "can't" "cannot"
[97] "couldn't" "mustn't" "let's" "that's" "who's" "what's" "here's" "there's"
[105] "when's" "where's" "why's" "how's" "a" "an" "the" "and"
[113] "but" "if" "or" "because" "as" "until" "while" "of"
[121] "at" "by" "for" "with" "about" "against" "between" "into"
[129] "through" "during" "before" "after" "above" "below" "to" "from"
[137] "up" "down" "in" "out" "on" "off" "over" "under"
[145] "again" "further" "then" "once" "here" "there" "when" "where"
[153] "why" "how" "all" "any" "both" "each" "few" "more"
[161] "most" "other" "some" "such" "no" "nor" "not" "only"
[169] "own" "same" "so" "than" "too" "very"
In some legal contexts these words can matter a lot. Further, some words that we as lawyers may consider to be "noise" or unwanted stop words such as purely stylistic legal latin like "inter alia" , are not included in that stopword count. Hence as part of the exercise for this lesson, you will need to create a legal stopword list. For now, though, we just eliminate stopwords.
corpus <- tm_map(corpus, removeWords, stopwords("english"))
One other common pre-preprocessing step, stemming, whereby words are reduced to their root or stem, is something that must be done cautiously when applied to legal texts. The reason for that is because, in a legal context, words with the same stems can have very different meanings. For example, some stemmers group the word "arbitrary" and "arbitrator" to the same "arbitr" stem. As a result, stemming risks eliminating information that is useful to lawyers.
Creating a Term-document Matrix
Now that we have a pre-processed corpus, we can represent our text as data. More precisely, we can display it as a
term-document-matrix where each row is a term and each column is a document, i.e. one of our judgments.
# Create a term-document matrix.
tdm <- TermDocumentMatrix(corpus)
We can then proceed to the corpus analysis. For instance, we may want to find the most frequent words.
# We can for example sort our matrix by the word frequency in our corpus.
frequent_words <- sort(rowSums(tdm), decreasing=TRUE)
# Here are our most frequent words.
##
court canlii para act scc scr canada rights
1881 1812 1488 1415 1270 1246 1217 1216
Visualizing Word Frequency
One of the advantages of R is that results are easy to visualize. For example, we can visualize how often a word is used through wordclouds. For that, we need to download and activate the wordcloud package.
To make the wordcloud easily readable we reduce the information we want to display.
# It is easier to work with wordcloud if we tranform our file into a dataframe.
frequent_words <- as.data.frame(as.table(frequent_words))
colnames(frequent_words) <- c("word", "freq")
# In order not to overload the word cloud, we limit our analysis to the 50 most frequent words in the corpus.
most_frequent_words <- head(frequent_words,50)
Finally, we can proceed to the visualization.
# You can adjust the color to your liking.
wordcloud(most_frequent_words$word,most_frequent_words$freq, colors = brewer.pal(8, "Dark2"))
##
Working with Bigrams
Above we worked with single words --
unigrams. But we can do the same analysis with sequences of words --
ngrams. Here we use a sequence of 2 words --
bigrams.
# This is our bigram function. R allows you to write your own functions. This is what we do here. We can then apply our function within the script similar to R's in-built functions.
bigrams <- function(x) unlist(lapply(ngrams(words(x), 2), paste, collapse = " "), use.names = FALSE)
We now create another term-document matrix with one small change: Each row is now a pair of words.
# Creating a term-document matrix with bigrams.
tdm <- TermDocumentMatrix(corpus, control = list(tokenize = bigrams))
Again, we can use our Term-Document matrix to identify the most frequent pairs of words.
# Sort TDM find the 12 most frequent bigrams.
head(sort(rowSums(tdm), decreasing=TRUE), 12)
##
r v scc scr scc canlii canlii scr court appeal canlii scc
729 631 606 603 597 587
attorney general de facto constitution act jury roll trial judge british columbia
491 398 296 291 280 261
Dictionary Approach I: Term Mapping
Dictionary methods relate an outside list of terms to our corpus. This can be useful for a range of tasks. We may want to automatically check for the presence of absence of certain terms as part of a content analysis. Or we may want to select specific documents where specific signalling terms from our dictionary appear.
In this lesson, we use Dictionary methods for two tasks. First, we want to know whether appeals to the Supreme Court of Canada were successful or not. To do so, we will map signalling terms to automate this assignment. Second, we use a sentiment dictionary to determine whether the judges use a positive or a negative tone when in their judgements.
Here, we tackle the first of these tasks - classifying the outcome of decisions. For that, we can come up with two signalling terms that denote each outcome. Of course, rather than using one signalling term, we could use several.
success_formular <- "should be allowed"
reject_formular <- "should be dismissed"
We then check whether that word sequence is present and append it to our cases as success or reject.
success <- str_count(scc$text, success_formular)
reject <- str_count(scc$text, reject_formular)
Since each of these terms is potentially repeated, we substitute occurrences with "yes" and the absence of the term with "no".
success <- gsub("1|2","yes",success)
success <- gsub("0","no",success)
reject <- gsub("1|2","yes",reject)
reject <- gsub("0","no",reject)
Finally, we add the success and failure column to our dataset.
Take a look at the dataset using the View(scc) command. You will notice that some SCC cases are classified as both success and reject. How can that be? If you open the SCC judgment [2013] 1 S.C.R. 61.txt you will see that the Supreme Court allowed part of the appeal and dismissed another part. So our assignment of success and reject turned out to be correct. In general, it is prudent double check your results for accuracy.
Because legal texts often follow specific drafting rules and convention, such rule-based term mapping can work surprisingly well to map the contents of legal documents. But simple word mapping rules do not work for all tasks. Where no uniform signalling terms exist, researchers are better off to resort to more flexible machine-learning approaches that we will discuss in the section on Classification and Prediction.
Dictionary Approach II: Sentiment Analysis
Another dictionary approach uses outside word lists to investigate the sentiment of text. Essentially, that means that we count words with positive and negative connotation in a document to assess the sentiment or tonality of a document.
For example, political scientists have used sentiment analysis to investigate whether judges change their tone when dealing with particularly sensitive cases. We repeat a similar analysis here. The same approach, but with a different dictionary, could be used to look at other characteristics of legal texts, such as their prescriptivity, flexibility or use of legalese or outdated terms.
Let's start by downloading and activating the SentimentAnalysis package and by taking a look at its inbuilt sentiment dictionaries.
library("SentimentAnalysis")
# The package comes with existing word lists of positive and negative words.
head(DictionaryGI$positive)
##
[1] "abide" "ability" "able" "abound" "absolve" "absorbent"
head(DictionaryGI$negative)
##
[1] "abandon" "abandonment" "abate" "abdicate" "abhor" "abject"
Next, we want check how many of these positive and negative words exist in each text of our corpus. Do judges tend to use more positive or more negative language when framing their decisions.
# We count how many of positive and negative words exist in each text .
tdm <- TermDocumentMatrix(corpus)
text_sentiment <- analyzeSentiment(tdm)
When positive words outnumber negative ones, we classify a text as positive and vice versa. This is of course only an approximation but may nevertheless be helpful to group texts.
table(convertToBinaryResponse(text_sentiment)$SentimentGI)
## negative positive
0 25
As it turns out, all judgments use more positive words than negative words.