Visually clustering case law

I’ve been experimenting with a Python package called Yellowbrick, which provides a suite of visualisers built for gaining an insight into a dataset when working on machine learning problems.

One of my side projects at the moment is looking at ways in which an unseen case law judgment can be processed to determine its high level subject matter (e.g. Crime, Tort, Human Rights, Occupiers’ Liability etc) with text classification.

I did a quick experiment with Yellowbrick to visualise a small portion of the dataset I’m going to use to train a classifier. And here it is:

tSNE projection of 14 case law topic clusters using Yellowbrick

tSNE projection of 14 case law topic clusters using Yellowbrick

The chart above is called a TSNE (t-distributed stochastic neighbour embedding) projection. Each blob on the chart represents a judgment.

I was pretty pleased with this and several quick insights surfaced:

  • Crime may be a bit over represented in my training data, I’ll need to cut it back a bit

  • Some of the labels in the data may need a bit of merging, for example “false . imprisonment” can probably be handled by the “crime” data

  • There are a couple of interesting sub-clusters within the crime data (I’m guessing one of the clusters will be evidence and the other sentencing)

  • Human Rights as a topic sits right in the middle of the field between the crime cluster and the clusters of non-criminal topics

The code


from yellowbrick.text import TSNEVisualizer
from import set_palette
from sklearn.feature_extraction.text import TfidfVectorizer
import os

from sklearn.datasets.base import Bunch
from tqdm import tqdm
import matplotlib.pyplot as plt


Load the corpus

def load_corpus(path):
    Loads and wrangles the passed in text corpus by path.

    # Check if the data exists, otherwise download or raise
    if not os.path.exists(path):
        raise ValueError((
            "'{}' dataset has not been downloaded, "
            "use the module to fetch datasets"

    # Read the directories in the directory as the categories.
    categories = [
        cat for cat in os.listdir(path)
        if os.path.isdir(os.path.join(path, cat))

    files  = [] # holds the file names relative to the root
    data   = [] # holds the text read from the file
    target = [] # holds the string of the category

    # Load the data from the files in the corpus
    for cat in categories:
        for name in os.listdir(os.path.join(path, cat)):
            files.append(os.path.join(path, cat, name))

            with open(os.path.join(path, cat, name), 'r') as f:

    # Return the data bunch for use similar to the newsgroups example
    return Bunch(
    corpus = load_corpus('/Users/danielhoadley/Desktop/common_law_subset')

Vectorise and transform the data

tfidf  = TfidfVectorizer(use_idf=True)
docs   = tfidf.fit_transform(
labels =

Generate the visualisation

tsne = TSNEVisualizer(size=(1080, 720),title="Case law clusters"), labels)

Case law network graph

I recently posted a few images of a network graph I built with Neo4j depicting the connections between English cases. This article serves as a quick write up on how the graph database and the visualisations where produced.



The data driving the network graph was derived from a subset of XML versions of cases reported by the Incorporated Council of Law Reporting for England and Wales. I used a simple Python script to iterate over the files and capture (a) the citation (e.g. [2010] 1 WLR 1) associated with the file -- source; and (b) all of the citations to other cases within this file -- each outward citation from the source is the target. This was pulled into CSV format, like so:

[2015] 1 WLR 3238,[2015] AC 129
[2015] 1 WLR 3238,[2013] 1 WLR 366
[2015] 1 WLR 3238,[2011] 1 WLR 980

In the snippet of data above, [2015] 1 WLR 3238 can be seen to have CITED three cases, [2015] AC 129, [2013] 1 WLR 366 and [2011] 1 WLR 980. Moreover, [2015] AC 129 can be seen to have been CITED_BY [2015] 1 WLR 3238.

Importing the data into Neo4J

The data was imported into Neo4j with the following CYPHER query:

MERGE (c:Case {Name:toString(row.Source)})
MERGE (d:Case {Name:toString(row.Target)})
MERGE (c) -[:CITED]-> (d)
MERGE (d) -[:CITED_BY] -> (c)

The query above is a standard import query that created a node (:Case) for each unique citation in the source data and then constructed two relationships, :CITED and :CITED_BY between each node where these relationships existed.

View of the a small portion of the graph from the Neo4j browser

View of the a small portion of the graph from the Neo4j browser

Calculating the transitive importance of the cases in the graph

With the graph pretty much built, I wanted to get a sense of the most important case in the graph and the PageRank algorithm was used to achieve this:

CALL algo.pageRank('Case', 'CITED_BY',{write: true, writeProperty:'pagerank'})

This stored each case's PageRank as a property, pagerank, on the case node.

It was then possible to identify the ten most important cases in the network by running:

MATCH (c:Case) 
RETURN c.Name, c.pagerank 
ORDER BY c.pagerank DESC LIMIT 10

Which returned:

[2014] 3 WLR 535,15.561027
[2016] Bus LR 1337,13.3335
[2009] 3 WLR 369,11.5683645
[2000] 1 WLR 2068,11.149255000000002
[2009] 3 WLR 351,10.952590499999998
[1996] 1 WLR 1460,10.657869999999999
[2002] 2 WLR 578,9.848398000000001
[2000] 3 WLR 1855,9.2526755
[2005] 1 WLR 2668,8.36525
[2005] 3 WLR 1320,7.990162000000001

Visualising the graph

To render the graph in the browser, I used [neovis.js][1]. The code for the browser render:

        <style type="text/css">
            body {font-family: 'Gotham' !important}
            #viz {
                width: 900px;
                height: 700px;
        <script src=""></script>
        function draw() {
            var config = {
                container_id: "viz",
                server_url: "bolt://localhost:7687",
                server_user: "beans",
                server_password: "sausages",
                labels: {
                    "Case": {
                        caption: "Name",
                        size: "pagerank",
                relationships: {
                    "CITED_BY": {
                        caption: false,                           
                initial_cypher: "MATCH p=(:Case)-[:CITED]->(:Case) RETURN p LIMIT 5000"
            var viz = new NeoVis.default(config);
    <body onload="draw()">
        <div id="viz"></div>
Visualisation with neovis.js

Visualisation with neovis.js

To add colour to the various groups of cases in the graph, I used a hacky implementation of the label propogation community detection algorithm (I say hacky, because I didn't set any seed labels).

CALL algo.labelPropagation('Case', 'CITED_BY','OUTGOING',
  {iterations:10,partitionProperty:'partition', write:true})
YIELD nodes, iterations, loadMillis, computeMillis, writeMillis, write, partitionProperty;

The neovis.js could then by updated with a "community" attribute to generate different colours for each community of cases:

        <style type="text/css">
            body {font-family: 'Gotham' !important}
            #viz {
                width: 900px;
                height: 700px;
        <script src=""></script>
        function draw() {
            var config = {
                container_id: "viz",
                server_url: "bolt://localhost:7687",
                server_user: "sausages",
                server_password: "beans",
                labels: {
                    "Case": {
                        caption: "Name",
                        size: "pagerank",
                        community: "partition"
                relationships: {
                    "CITED_BY": {
                        caption: false,    
                initial_cypher: "MATCH p=(:Case)-[:CITED]->(:Case) RETURN p LIMIT 5000"
            var viz = new NeoVis.default(config);
    <body onload="draw()">
        <div id="viz"></div>

Part 3: Open Access To English Case Law (The Raw Data)

I started writing in the spring of this year about the state of open access to case law in the UK, with a particular focus on judgments given in the courts of England and Wales. 

The gist of my assessment of the state of open access to judgments via the British open law apparatus is set out here, but boils down to:

  • Innovation in the open case law space in the UK is stuck in the mud
  • BAILII is lagging behind comparable projects taking place elsewhere in the common law world: CanLII and CaseText are excellent examples of what's possible.
  • Insufficient focus, if any, is being directed to improving open access to English case law.

In a subsequent article, I explored the value in providing open and free online access to the decisions of judges. I identified four bases upon which open access can be shown to be a worthwhile endeavour: (i) the promotion of the rule of law; (ii) equality of arms, particularly for self-represented litigants; (iii) legal dispute reduction; and (iv) transparency.

In the same article, I developed a rough and ready definition of what "open access to case law":

"Open access to case law" isn't a "thing", it's a goal. The goal, at least to my mind, boils down to providing access that is free at the point of delivery to the text of every judgment given in every case by every court of record (i.e. every court with the power to give judgments that have the potential to be binding on lower and co-ordinate courts) in the jurisdiction.

My overriding concern is that a significant number of judgments do not make their way to BAILII and are only accessible to paying subscribers of subscription databases, effectively creating a "have and have nots" scenario where comprehensive access to the decisions of judges depends on the ability to pay for it. The gaps in BAILII's coverage were discussed in this article.

In this article I go deeper into exploring how big the gaps are in BAILII's coverage when compared to the coverage of judgments provided by three subscription-based research platforms: JustisOne, LexisLibrary and WestlawUK. 


The aim of the study was gather data on the coverage provided by BAILII, JustisOne, LexisLibrary and WestlawUK of judgments given in the following courts between 2007 and 2017:

  • Administrative and Divisional Court
  • Chancery Division
  • Court of Appeal (Civil Division)
  • Court of Appeal (Criminal Division)
  • Commercial Court
  • Court of Protection
  • Family Court
  • Family Division
  • Patents Court
  • Queen's Bench Division
  • Technology and Construction Court


The way in which year-on-year counts of judgments given in a given court are handled by each of the four platforms varies from platform to platform. Accordingly, the following method was devised to extract the data from each platform:


BAILII provides an interface to browse its various databases. Within each database, it is possible to isolate a court and a year. The page for a given year of a given court sets out a list of the judgments for that year.

Each judgment appears in the underlying HTML as a list element (<li> ... </li>). For example,

<li><a href="/ew/cases/EWCA/Crim/2017/17.html">Abi-Khalil &amp; Anor, R v </a><a title="Link to BAILII version" href="/ew/cases/EWCA/Crim/2017/17.html">[2017] EWCA Crim 17</a> (13 January 2017)</li>

A count of the total number of each <li> ... </li> on each pages yields the total count of judgments.

Justisone, lexislibrary & westlawuk

The three subscriber platforms were approached differently. A list of search strategies based on the neutral citation for each court was constructed.

For example, to query judgments given in the Criminal Division of the Court of Appeal in 2017, the following query was constructed:

2017 ewca crim

A query for each court and each year was constructed and then submitted by the platform's "citation" search field. The total number of judgments yielded by the query was extracted by capturing the count of results from the platform's underlying HTML.

The Data

The data captured is available here in raw form. The code used to generate the visualisation in this article is available here as a Jupyter Notebook.

annual coverage by publisher

The following graph provides an overview of the annual coverage for all of the courts studied by publisher. The following points leap out of graph:

  • BAILII's coverage of judgments is far lower than that provided by the three subscription-based platforms, running on a rough average of between 2,500-3,000 judgments per year.
  • Save for a drop in LexisLibrary's favour in 2011, JustisOne consistently provides the most comprehensive coverage of judgments.
  • From 2012, Lexis has closely tracked JustisOne's coverage
  • There is a sharp and sudden proportional drop in coverage from 2014 across all four platforms.

The key takeaway from this graph is that a significant number of judgments never make it onto BAILII every year.


The following graph provides an alternative view of the same data. 


total coverage of court by publisher

This graph provides an overview of how each publisher fares in terms of coverage of the courts included in the study. By and large, there is a health degree of parity in coverage of the following courts across all four publishers:

  • Chancery Division
  • Commercial Court
  • Court of Protection
  • Family Court
  • Family Division
  • Technology and Construction Courts

However, BAILII is struggling to keep up with the levels of comprehensiveness provided by the commercial publishers in the Administrative Court, both divisions of the Court of Appeal and the Queen's Bench Division. 

The dearth in coverage of judgments from the Criminal Division on BAILII is especially startling, particularly given rise numbers of criminal defendants lacking representation at the sentencing stage. Intuitively (though I have not confirmed this), the deficit in BAILII's coverage of the Criminal Division will almost certainly be judgments following an appeal against sentence. 


(Interim) Conclusion

The data shows that BAILII is providing partial access to the overall corpus of judgments handed down in the courts studied. This, as I have previously been at pains to stress, is not down to any failing on BAILII's part. Rather, it is a symptom of how hopeless existing systems (such as they are) are at servicing BAILII with a comprehensive flow of cases to publish, particularly judgments given extempore. 

It also bears saying that the commercial publishers do not in any way obstruct BAILII from acquiring the material. A fuller discussion of the mechanics driving the problem will appear here soon.

Using Scikit-Learn to classify your own text data (the short version)

Last month I posted a lengthy article on how to use Scikit-Learn to build a cross-validated classification model on your own text data. The purpose of that article was to provide an entry point for new Scikit-Learn users who wanted to move away from using the built-in datasets (like twentynewsgroups) and focus on their own corpora.

I thought it might be useful to post a condensed version of the longer read for people who wanted to skip over the explanatory material and get started with the code.

As before, the objective of the code is as follows. We have a dataset consisting of multiple directories, each containing n text files. Each directory name acts as a descriptive category label for the files contained within (e.g. technology, finance, food). We're going to use this data to build a classifier capable of recieving new, unlabeled text data and assigning it to the best fitting category.

The code

import sklearn
import numpy as np
from glob import glob
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.linear_model import SGDClassifier
from sklearn import metrics
from sklearn.pipeline import Pipeline
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.externals import joblib

Get paths to labelled data

rawFolderPaths = glob("/Users/danielhoadley/PycharmProjects/trainer/!labelled_data_reportXML/*/")

print ('\nGathering labelled categories...\n')

categories = []

Extract the folder paths, reduce down to the label and append to the categories list

for i in rawFolderPaths:
    string1 = i.replace('/Users/danielhoadley/PycharmProjects/trainer/!labelled_data_reportXML/','')
    category = string1.strip('/')
    #print (category)

Load the data

print ('\nLoading the dataset...\n')
docs_to_train = sklearn.datasets.load_files("/Users/danielhoadley/PycharmProjects/trainer/!labelled_data_reportXML",description=None, categories=categories, load_content=True, encoding='utf-8', shuffle=True, random_state=42)

Split the dataset into training and testing sets

print ('\nBuilding out hold-out test sample...\n')
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.4)

Transform the training data into tfidf vectors

print ('\nTransforming the training data...\n')
count_vect = CountVectorizer(stop_words='english')
X_train_counts = count_vect.fit_transform(raw_documents=X_train)

tfidf_transformer = TfidfTransformer(use_idf=False)
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
print (X_train_tfidf.shape)

Transform the test data into tfidf vectors

print ('\nTransforming the test data...\n')
count_vect = CountVectorizer(stop_words='english')
X_test_counts = count_vect.fit_transform(raw_documents=X_test)

tfidf_transformer = TfidfTransformer(use_idf=False)
X_test_tfidf = tfidf_transformer.fit_transform(X_test_counts)
print (X_test_tfidf.shape)

print (X_test_tfidf)
print (y_train.shape)

docs_test = X_test

Construct the classifier pipeline using a SGDClassifier algorithm

print ('\nApplying the classifier...\n')
text_clf = Pipeline([('vect', CountVectorizer(stop_words='english')),
                     ('tfidf', TfidfTransformer(use_idf=True)),
                     ('clf', SGDClassifier(loss='hinge', penalty='l2',
                      alpha=1e-3, random_state=42, verbose=1)),

Fit the model to the training data, y_train)

Run the test data into the model

predicted = text_clf.predict(docs_test)

Calculate mean accuracy of predictions

print (np.mean(predicted == y_test))

Generate labelled performance metrics

print(metrics.classification_report(y_test, predicted,

Scraping news websites and looking for specific words and phrases

This afternoon, my colleague and Transparency Project member, Paul Magrath, told me he was interested finding out whether there's a way of systematically watching out for a set of pre-defined "trigger words" of interest to the Transparency Project in online articles published by a selection of news organisations with a nasty habit of misreporting family court proceedings. 

I thought "that's a perfect job for Python" and sat down to write a basic proof of concept for Paul to take a look at. 

The code, which is here, iterates through an RSS feed on the Daily Mail's online site, reads each article by requesting the article link for each item in the feed and checks it for a list of pre-defined triggers (currently devised around an article about Myleene Klass, of all people). The output is generated back to a CSV file for review. 

Here's the GitHub repo.

Using scikit-Learn on your own text data

Scikit-learn’s Working with Text Data provides a superb starting point for learning how to harness the power and ease of the sklearn framework for the construction of really powerful and accurate predictive models over text data. The only problem is that scikit-learn’s extensive documentation (and, be in no doubt, the documentation is phenomenal) doesn’t help much if you want to apply a cross-validated model on your own text data

At some point, you’re going to want to move away from experimenting with one of the built-in datasets (e.g. twentynewsgroups) and start doing data science on textual material you understand and care about. 

The purpose of this tutorial is to demonstrate the basic scaffold you need to build to apply the power of scikit-learn to your own text data. I’d recommend methodically working your way through the Working with Text Data tutorial before diving in here, but if you really want to get cracking, read on.

If you can't be bothered reading on and just want to see the code, it's in a repo on GitHub, here.


Before we start, let’s be clear about what we’re trying to do. We have a great big collection of text documents (ideally as plain text from the offing). Our documents are, to use the twentynewsgroups example, all news articles. The news articles have been grouped together, in directories, by their subject matter. We might have one subdirectory consisting of technology articles, called Technology. We might have another subdirectory consisting of articles about tennis, called Tennis

Our project directory might look like this (assume each subdirectory has 100 text documents inside):

news_articles \

The aim of the game is to use this data to train a classifier that is capable analysing a new, unlabelled article and determining which bucket to put it in (this is an article about food, this is an article about business, etc). 

What our code is going to do

We’re going to write some code, using scikit-learn, that does the following:

  • Loads our dataset of news articles and categorises those articles according to the name of the folder they live in (e.g. art, food, tennis)
  • Splits the dataset into two chunks: a chunk we’re going to use to train our classifier and another chunk that we’re going to use to test how good the classifier is
  • Converts the training data into a form the classifier can work with
  • Converts the test data into a form the classifier can work with
  • Builds a classifier 
  • Applies that classifier to our training data
  • Fires the test data into our trained classifier
  • Tells us how well the classifier did at predicting the right label (art, food, tennis etc) of the each document in the test dataset

1. Get the environment ready

The first job is to bring in everything we need from scikit-learn:

import sklearn
import numpy as np
from glob import glob
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.linear_model import SGDClassifier
from sklearn import metrics
from sklearn.pipeline import Pipeline 

That’s the stage set in terms of bringing in our dependencies.

2. Set our categories

The next job is to state the names of the categories (our folders of grouped news articles) in a list. These need to exactly match the names of the subdirectories acting as the categorical buckets in your project directory.

categories = [‘art’, ‘business’, ‘culture’, ‘design’, ‘food’, ‘technology’, ‘tennis’, ‘war’]

This approach of manually setting the folder names works well if you only have a few categories or you’re just using a small sample of a larger set of categories. However, if you’ve got lots of category folders, manually entering them as list items is going to be a bore and will make your code very, very ugly (I'll write a separate blog post on a better way of dealing with this, or look at the repo on GitHub, which incorporates the solution to this problem).

3. Load the data

We’re now ready to load our data in:

docs_to_train = sklearn.datasets.load_files(“/path/to/the/project/folder/“, 
    description=None, categories=categories, 
    load_content=True, encoding='utf-8', shuffle=True, random_state=42)

All we’re doing here is saying that our dataset, docs_to_train, consist of the files contained within all of the subdirectories to the path specified inside the .load_files function and that the categories are the categories set out in our categories list (see above). Forget about the other stuff in there for now.

4. Split the dataset we’ve just loaded into a training set and a test set

This is where the real work begins. We’re going to use the entire dataset, docs_to_train, to both train and test our classifier. For this reason, we’ve got to split the dataset into two chunks: one chunk for training and another chunk (that the classifier won’t get to look at in training) for testing. We’re going to “hold out” 40% of the the dataset for testing:

X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.4)

It’s really important to understand what this line of code is doing. 

First, we’re creating four new objects, X_train, X_test, y_train and y_test. The X objects are going to hold our data, the content of the text files. We’ve got one X object, X_train, and that will hold the text file data we’ll use to train the classifier. We have another X object, X_test, and that will hold the text file data we’ll use to test the classifier. The Xs are the data.

The we have the Ys. The Y objects hold the category names (art, culture, war etc). y_train will hold the category names that correspond to the text data in X_train. y_test will hold the category names category names that correspond to the text data in X_test. The y value are the targets. 

Finally, we’re using test_size=0.4 to say that out of all the data in docs_to_train we want 40% to be held out for the test data in X_test and y_test.

5. Transform the training data into a form the classifier can work with

Our classifier uses mathematics to determine whether Document X belongs in bucket A, B, or C. The classifier therefore expects numeric data rather than text data. This means we’ve got to take our text training data, stored in X_train, and transform it into a form our classifier can work with. 

count_vect = CountVectorizer(stop_words='english')

X_train_counts = count_vect.fit_transform(raw_documents=X_train)

These two lines are doing are a lot of heavy lifting and I would strongly urge you to go back to the Working with Text Data tutorial to fully understand what’s going on here.

The first thing we’re doing is setting up a vectoriser, CountVectorizer(). This is a function that will count the number of times each word in the dataset occurs and project that count into a vector. 

Then, we take that vector and apply it to the training data stored in X_train. We store those occurrence vectors in X_train_counts.

Once that’s done we move on to the clever transformation bit. We’re going to take the occurrence counts, stored in X_train_counts, and transform them into a term frequency inverse document frequency value. 

tfidf_transformer = TfidfTransformer(use_idf=True)

X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)

Why are we doing this? Well, if you think about it, the documents in your training set will naturally vary in word length; some are going to be long, others are going to be short. Longer documents have more words in them and that’s going to result in a higher word count for each word. That’s going to skew your results. What we really want to do is get get a sense of the count of each word proportionate to the number of words in the document. Tf-idf (term frequency inverse document frequency achieves this). 

6. Transform the test data into a form the classifier can work with

Since we’ve gone to the trouble of splitting the dataset into a training set and a test set, we also need to transform our test data in exactly the same way as we just did with the training set. All we’re doing here is mirroring the transformation process we just applied to X_train onto X_test. 

count_vect = CountVectorizer(stop_words='english')
X_test_counts = count_vect.fit_transform(raw_documents=X_test)

tfidf_transformer = TfidfTransformer(use_idf=True)
X_test_tfidf = tfidf_transformer.fit_transform(X_test_counts)

7. Scikit-learn gives us a far better way to deal with these transformations: pipelines!

It was worth reading about the transformation process, because if you’re working with text data and trying to do science with it you really do need to at least see why and how that text is transformed into a numerical form a predictive classifier can deal with. 

However, scikit-learn actually gives us a far more efficient way (in terms of lines of code) to deal with the transformations — it’s called a pipeline. The pipeline is this example has three phases. The first creates the vectoriser — the machine used to turn our text into numbers — a count of occurrences. The second phase deals with transforming the crude vectorisation handled in the first into a frequency-based representation of the data — the term frequency inverse document frequency. Finally, and most excitingly, the third phase of the pipeline sets up the classifier — the machine that’s going to train the model. 

Here’s the pipeline code:

text_clf = Pipeline([('vect', CountVectorizer(stop_words='english')),
    ('tfidf', TfidfTransformer(use_idf=True)),
    ('clf', SGDClassifier(loss='hinge', penalty='l2', alpha=1e-3, random_state=42, 

For now, don’t worry about the parameters set out in the classifier, just appreciate the structure and content of the pipeline. 

8. Deploy the pipeline and train the model

Now it’s time to train our model by applying the pipeline we’ve just built to our training data. All we’re doing here is taking our training data (X_train) and the corresponding training labels (y_train) and passing them into the fit function that comes built into the pipeline., y_train)

Depending on how big your dataset is, this could take a few minutes or a bit longer. 

As a sidenote, on this point, you might have noticed that I set the verbose parameter in the classifier as 1. This is purely so I can see that the classifier is running and that the script isn't hanging because I’m chewing through memory. 

9. Test the model we’ve just trained using the test data

We’ve now trained our model on the training data. It’s time to see how well trained the model really is by letting it loose on our test data. What we’re going to do is take the test data, X_test, let the model evaluate it based on what it learned from being fed the training data (X_train) and the training labels (y_train) and see what categories the model predicts the test data belongs to. 

predicted = text_clf.predict(X_test)

We can measure the model’s accuracy by taking the mean of the classifier’s predictive accuracy like so:

print (np.mean(predicted == y_test))

Better yet, we can use scikit-learn’s built in metrics library to give us some detailed performance statistics by category (i.e. how well did the classifier at predicting that an article about “design” is an article about “design”.

print(metrics.classification_report(y_test, predicted, 

The metrics will provide you with a precision score for each category and an overall average of the performance of the model between 0 and 1. 

The closer that average score gets to 1, the better the model will perform. Mind you, if your model is averaging a score of 1 on the nose, something has gone wrong!   

A (Brief) Excursion into Topic Modelling with Mallet

For the past three months or so, I've been experimenting with a range of topic models across a range of technologies, including R, Python, C++ and Java. 

I've recently been spending time with MALLET (a Java-based suite of NLP tools) and I'm really impressed with how easy this implementation is to get working. 

There is a truly excellent walkthrough courtesy of the Programming Historian right here, which makes everything perfectly clear if you're coming towards this without much experience. 

As is usual, I tested MALLET with a reasonably large corpus of judgments from the Criminal Division of the Court of Appeal, which I had organised as .txt files in a directory on my machine.

The following steps provide a basic outline of how I got everything going:

Download MALLET

Speaks for itself. You can download MALLET here. Unzip the .tar file to a directory of your choosing.

Import the data

The first thing we need to do is import the data into MALLET. To do this, go to the directory in which you unpacked the MALLET .tar file at the command line and then run the following command:

bin/mallet import-dir --input path/to/the/your/data --output topic-input.mallet \
  --keep-sequence --remove-stopwords

This runs MALLET, points it to the directory holding your data, creates the input file you'll use in the next step (topic-input.mallet) and removes uninteresting words (like a, of, the, for, etc)

Build the topic model

The steps above shouldn't have taken you much more than 5-10 minutes. This bit is the fun part - building the topic model. 

At the command line, run:

bin/mallet train-topics --input topic-input.mallet --num-topics 50 --output-state topic-state.gz --output-doc-topics doc-topics.txt --output-topic-keys topic_keys.txt

This passes in the input file generated in the step above, sets the number of topics to generate at 50 and then specifies a range of outputs. 

The most interesting outputs generated are:

  • topic-keys.txt, which sets out the topics and the key terms within those topics
  • doc-topics, which sets out the main topic allocations for each document in the dataset.

A first run with Hierarchical Dirichlet Process

I've been experimenting with Latent Dirichlet Allocation for a while in R, but was looking for a topic model algorithm that did not require the number of topics (k) to be defined apriori the application of the algorithm to the text data I wanted to work with. 

As a relative newcomer to topic modelling, I hadn't even heard of David Blei or Chong Wang, both of whom I now know to be pioneers in modern topic modelling. Quite by chance, I stumbled on Wang and Blei's implementation of Hierarchical Direchlet Processing in C++, a topic model where the data determine the number of topics. 

Getting HDP to work required quite a bit of wrangling and I couldn't find any walkthroughs suitable for novices like me, so I thought it would be worth noting up how I managed to get it to work with a small sample set of text data. 

What follows is far from a perfect (or even good) representation of how to apply HDP to text data, but the steps that follow did work for me. Here goes...

Sample Data

It goes without saying that the very first step is to assemble a corpus of text data against which we'll apply the HDP algorithm. In my case, as is usual, I used ten English judgments (all of which are recent decisions from the Criminal Division of the Court of Appeal) in .txt format. Save these into a folder.

Getting the Sample Data ready for HDP

Before we even go near Wang & Blei's algorithm, we need to prepare the sample data in a particular way. 

The algorithm requires the data to be in LDA-C format, which looks like this:

 [M] [term_1]:[count] [term_2]:[count] ...[term_N]:[count]
where [M] is the number of unique terms in the document, and the [count] associated with each term is how many times that term appeared in the document.

This presents the first problem, because our data appear as words in .txt format files. Fortunately, there's an excellent Python program called text2ldac that comes to our rescue. Text2ldac takes the data in .txt format and outputs the files we need, in the form in which we need them.

Clone text2ldac from the git repo here

Once you've pulled down text2ldac, you're ready to take your text files and process them. To do this, go to the command line and run the following command (make adjustments to the example that follows to suit your own directories and filenames:

$ text2ldac danielhoadley$ python --stopwords stopwords.txt /Users/danielhoadley/Documents/Topic_Model/text2ldac/input

All that's happening here is we're running, using the --stopwords flag to pass in our stopwords (which, in my case, are in a file named stopword.txt) and then passing in the directory that contains our .txt files.

This will output three files: a .dat file (e.g. input.dat) which is the all import LDA-C formatted input for the HDP algorithm, a .vocab file, which contains all of the words in the corpus (one word per line) and a .dmap file, which lists the input .txt documents. 

Time to run HDP

Now that we have our data in the format required by the HDP algorithm, we're ready to apply the algorithm. 

For convenience, I recommend copying the three files generated by text2ldac into the folder you're going to run HDP from, but you can leave them wherever you like. 

Go to the folder containing the HDP program files and run the following command (again, adjust to your own folder and filenames:

$ ./hdp --algorithm train --data /Users/danielhoadley/Documents/Topic_Model/hdp/hdp/Second_run/input.dat --directory train_dir

Let's unpack this a bit: 

1. ./hdp invokes the HDP program

2. The --algorithm flag sets the algorithm to be applied, namely train

3. The path that follows the algorithm flag points to the .dat file produced by text2ldac

4. --directory train_dir is telling HDP to place the output files in a directory called train_dir

You'll know you've successfully executed the program if the prompt begins printing something that looks like this:

Program starts with following parameters:
algorithm:= train
data_path:= /Users/danielhoadley/Documents/Topic_Model/hdp/hdp/Second_run/input.dat
directory:= trainer
max_iter= 1000
save_lag= 100
init_topics = 0
random_seed = 1488746763
gamma_a = 1.00
gamma_b = 1.00
alpha_a = 1.00
alpha_b = 1.00
eta = 0.50
#restricted_scans = 5
split-merge = no
sampling hyperparam = no

reading data from /Users/danielhoadley/Documents/Topic_Model/hdp/hdp/Second_run/input.dat

number of docs: 9
number of terms : 5795
number of total words : 35865

starting with 7 topics 

iter = 00000, #topics = 0008, #tables = 0076, gamma = 1.00000, alpha = 1.00000, likelihood = -305223.54210
iter = 00001, #topics = 0008, #tables = 0079, gamma = 1.00000, alpha = 1.00000, likelihood = -301582.68017
iter = 00002, #topics = 0008, #tables = 0079, gamma = 1.00000, alpha = 1.00000, likelihood = -300273.98808

I'm not going to go into all of these parameters here, but the main one to note is the max_iter, which sets the number of time the algorithm walks over the test data. 

Note also that the algorithm has decided by itself how many topics it's going to work with (in the above example, 7)

The algorithm will produce a bunch of .dat files. The one we're really interested in should have a name like mode-topics.dat and not like this 00300-topics.dat (which was produced on the 300th iteration of the algorithm's walk). 

Printing the topics determined by HDP

Wang & Blei very helpfully provided a R script, print.topics.r, which you can use to turn the results of the algorithm into a human-readable form. This is helpful because the output generated by the algorithm will look like this:

00001 00001 00007 00007 00007
00011 00000 00000 00000 00000

The key thing at this stage is to remember two key files you'll need as input for the R script: the mode-topics.dat file (or similar name) generated by HDP and the .vocab file generated by text2ldac. 

Go back to the command line and navigate to the folder that contains print.topics.r. First, you'll need to make the R script executable, so run:

$ sudo chmod +x print.topics.R

Then run

$ ./print.topics.r /Users/danielhoadley/Documents/Topic_Model/hdp/hdp/trainer/mode-topics.dat /Users/danielhoadley/Documents/Topic_Model/hdp/hdp/Second_run/input.vocab topics.dat 4

1. ./print.topics.r runs the R script

2. The first argument is the path to the mode-topics.dat file produced by the HDP algorithm

3. The second argument is the path to the .vocab file produced by text2ldac

4. The third argument is the name of the file you want to output the human-readable result to, e.g. topics.dat

5. Finally, the fourth argument, which isn't mandatory, is the number of terms per topic you wish to output - the default is 5.

The output

If everything has worked as it ought to have done, you'll get an output like this if you look at the topic.dat file in RStudio:

The output is by no means perfect the first time around. Better results will probably depend on hitting the source data with a raft of stop words and tweaking HDP's many parameters, but it's a good start.





Sentiment in Case Law

Created with Sketch.

For the past few months, I've been exploring various methods of unlocking interesting data from case law. This post discusses the ways in which data science techniques can be used to analyse the sentiment of the text of judgments.

The focus in this post is mainly technical and describes the steps I've taken using a statistical programming language, called R, to extract an "emotional" profile of three cases.

I have yet to arrive at any firm hypothesis of how this sort of technique can be used to draw conclusions that would necessarily be of use in court or during case preparation, but I hope some of the initial results canvassed below are interest to a few. 

TidyText is an incredibly effective and approachable package in R for text mining that I stumbled across when flicking through some of the Studio::Conf 2017 materials a few days ago. 

There's loads of information available about the TidyText package, along with its underlying philosophy, but this post focuses on an implementation of one small aspect of the package's power: the ability to analyse and plot the sentiment of words in documents.


I'm using a small dataset for this walkthrough that consists of three court judgments: two judgments of the UK Supreme Court and one from its predecessor, the Judicial Committee of the House of Lords:

The subject matter of the dataset isn't really that important. My purpose here is to use the tools TidyText makes available to chart the emotional attributes of the words used in these judgments over the course of each document. 


First off, we need to get our environment ready and set our working directory:

# Environment

# Data - setwd to the folder that contains the data you want to work with

Next, we're going to get the text of the files (in my case, the three judgments) into a data frame:

case_words <- data_frame(file = paste0(c("evans.txt", "miller.txt", "donoghue.txt"))) %>%
mutate(text = map(file, read_lines))

This gives us a tibble with a single variable equal to the name of the file. We now need to unnest that tibble so that we have the document, line numbers and words as columns.

case_words <- case_words %>%
unnest() %>%
mutate(line_number = 1:n(),
 file = str_sub(basename(file), 1))
case_words$file <- forcats::fct_relevel(case_words$file, c("evans", "miller", "donoghue"))

We get a tibble that looks like this:

# A tibble: 96,318 × 3
file line_numberword
<fctr> <int> <chr>
1evans.txt 1lord
2evans.txt 1 neuberger
3evans.txt 1with
4evans.txt 1whom
5evans.txt 1lord
6evans.txt 1kerr
7evans.txt 1 and
8evans.txt 1lord
9evans.txt 1reed
10 evans.txt 1 agree
# ... with 96,308 more rows

You can check the state of your table at this point by running,


The last thing we need to do before we're ready to begin computing the sentiment of the data is to tokenise the words in our tibble:

case_words <- case_words %>%
unnest_tokens(word, text) 


We are ready to start analysing the sentiment of the data. TidyText is armed with three different sentiment dictionaries, afinn, nrc and Bing. The first thing we're going to do is get a birds eye view of the different sentiment profiles of each judgment using the nrc dictionary and plot the results using ggplot:

case_words %>%
inner_join(get_sentiments("nrc")) %>%
group_by(index = line_number %/% 20, file, sentiment) %>%
summarize(n = n()) %>%
ggplot(aes(x = index, y = n, fill = file)) + 
geom_bar(stat = "identity", alpha = 0.7) + 
facet_wrap(~ sentiment, ncol = 3)

A bird-eye view of ten emotional profiles of each judgment

The x-axis of each graph represents the position within each document from beginning to end, the y-axis quantifies the intensity of the sentiment under analysis. 

We can get a closer look at the emotional fluctuation by plotting an analysis using the afinn and Bing dictionaries:

case_words %>% 
left_join(get_sentiments("bing")) %>%
left_join(get_sentiments("afinn")) %>%
group_by(index = line_number %/% 20, file) %>%
summarize(afinn = mean(score, na.rm = TRUE), 
bing = sum(sentiment == "positive", na.rm = TRUE) - sum(sentiment == "negative", na.rm = TRUE)) %>%
gather(lexicon, lexicon_score, afinn, bing) %>% 
ggplot(aes(x = index, y = lexicon_score, colour = file)) +
geom_smooth(stat = "identity") + 
facet_wrap(~ lexicon, scale = "free_y") +
scale_x_continuous("Location in judgment", breaks = NULL) +
scale_y_continuous("Lexicon Score")

Sentiment curves using afinn and bing sentiment dictionaries


The Bing analysis (pictured right), appears to provide a slightly more stable view. Instances moving above the zero-line indicate positive emotion, instances moving below the zero line indicate negative emotion.

For example, if we take the judgment in R (Miller), we can see that the first half of the judgment is broadly positive, but then dips suddenly around the middle of the judgment. The line indicates that the second half of the judgment text is slightly more negative than the first half, but rises to it's peak of positivity just before the end of the text.

The text of the judgment in Donoghue is considerably more negative. The curve sharply dips as the judgment opens, turns more positive towards the middle of the document, takes a negative turn once more and resolves to a more positive state towards the conclusion. Statute Scraper

The following Python script can be used to scrape the full text of Public General Acts from


The script takes a list of URLs to individual pieces of legislation from a text file. The script processes each URL one by one. The text file needs to look something like this, with each target URL on a new line:


The Python script is simple enough. 

  • First, it opens url.txt and reads the target URLs line by line
  • For each target URL, the title of the legislation is captured (this is used to name the output files)
  • Each url is cycled through sequentially and the contents of the relevant part of the HTML markup is scraped
  • The scraped material is written to a text file and an prettified HTML file.

# Environment

import requests
import time
import io
from bs4 import BeautifulSoup
from urllib import urlopen

# Get the text file with the URLs to be scraped and scrape the target section in each page

print "\n\nScraping URLs in urls.txt...\n\n"

with open('urls.txt') as inf:
    # Get each url on each line in urls.txt
    urls = (line.strip() for line in inf)
    for url in urls:
        site = urlopen(url)
        soup = BeautifulSoup(site, "lxml")
        # Scrape the name of the legislation in each target url for use when saving the output to a file
        for legName in soup.find_all("h1", {"class": "pageTitle"}):
            actTitle = legName.text
                print 'Scraping ' + actTitle + ' ...\n'
        # Scrape stuff in <div id="viewLegContents"></div>
        for item in soup.find_all("div", {"id": "viewLegContents"}):
                # Write what we've scraped, with UTF-8 encoding, as text to a new text file - one file per url
                with (actTitle + '.txt', 'w', encoding='utf-8') as g:
                    # Write what we've scraped to an html file - one file per url
                    with open (actTitle + '.html', 'w') as g:

print "\n\nDone! Files created.\n"

Respect the source of the data you're scraping

The people behind have done everyone a big favour in making their information so accessible. The least we can do is to be respectful of their servers when performing scraping tasks like this. If you plan on running this, I'd strongly urge you to break your url.txt input into small chunks and, if you go on to reuse the data, remember to acknowledge the source of that data.

Rapid Keyword Extraction of Donoghue v Stevenson

Sometimes it would be really handy to be able to quickly and accurately extract keywords from a large corpus of documents. It is quite easy to foresee such a use-case arising in legal publishing, for example. 

RAKE (Rapid Keyword Extraction), is a Python natural language processing module that goes a long way in dealing with this use-case. 

I was interested in putting RAKE to the test and thought I'd pit the algorithm against what is perhaps to most well known piece of case law in the common law world: Donoghue v Stevenson (of snail and ginger beer fame). 

What follows is the basic "working out" of the code and the results of the first pass. For anyone interested in replicating this experiment or doing some keyword extraction of their own, see this excellent tutorial - you'll see that my own code follows it closely.


import rake 
import operator


rake_object = rake.Rake("smartstoplist.txt", 5, 5, 7)

This line of code does the following:

  • Creates a RAKE object that extracts keywords where (i) each word has at least 5 characters; (ii) each phrase must have at least 5 words; and (iii) each keyword must appear in the text at least 7 times
  • Hits the text file with a list of stop words to remove textual noise


Now we open the text file (in this test, I've saved the judgment in Donoghue as a text file) and save it in a variable:

judgment = open("dono.txt","r") 
text =


Now we're ready to run RAKE over the text to get the keywords:

keywords = 
print (keywords)


The following keywords (along with their scores) were returned:

[('give rise', 4.300000000000001), ('common law', 4.184313725490196), ('duty owed', 4.154061624649859), ('ordinary care', 4.115278543849972), ('reasonable care', 4.093482554312047), ('skivington lr 5', 4.050000000000001), ('lake & elliot', 4.0), ('pender 11 qb', 3.966666666666667), ('present case', 3.7993197278911564), ('defective', 1.7619047619047619), ('present', 1.7380952380952381), ('principles', 1.7333333333333334), ('dangerous', 1.6491228070175439), ('exercise', 1.588235294117647), ('cases', 1.5875), ('bottles', 1.5833333333333333), ('liability', 1.5789473684210527), ('relationship', 1.5555555555555556), ('court', 1.5365853658536586), ('supplying', 1.5), ('appears', 1.4761904761904763), ('principle', 1.4736842105263157), ('allowed', 1.4545454545454546), ('party', 1.4375), ('nature', 1.4210526315789473), ('warranty', 1.4166666666666667), ('goods', 1.4090909090909092), ('thing', 1.4090909090909092), ('articles', 1.4), ('condition', 1.4), ('appellant', 1.3953488372093024), ('injured', 1.3863636363636365), ('alleged', 1.375), ('bought', 1.3636363636363635), ('stated', 1.3636363636363635), ('examination', 1.3636363636363635), ('opportunity', 1.3636363636363635), ('appeal', 1.3333333333333333), ('support', 1.3333333333333333), ('defect', 1.3333333333333333), ('decided', 1.3333333333333333), ('relation', 1.3333333333333333), ('bottle', 1.3225806451612903), ('matter', 1.3125), ('authorities', 1.3125), ('injury', 1.3076923076923077), ('carelessness', 1.3076923076923077), ('judgment', 1.3055555555555556), ('proposition', 1.3043478260869565), ('recover', 1.3), ('referred', 1.3), ('circumstances', 1.2972972972972974), ('supplied', 1.2857142857142858), ('found', 1.2857142857142858), ('based', 1.2777777777777777), ('defendant', 1.2666666666666666), ('liable', 1.263157894736842), ('article', 1.26), ('manufactured', 1.25), ('lordships', 1.25), ('danger', 1.25), ('means', 1.25), ('poison', 1.25), ('inspection', 1.2307692307692308), ('purchaser', 1.2272727272727273), ('george', 1.2272727272727273), ('person', 1.2222222222222223), ('courts', 1.2222222222222223), ('house', 1.2105263157894737), ('plaintiff', 1.2096774193548387), ('chattel', 1.2), ('decision', 1.1935483870967742), ('entitled', 1.1818181818181819), ('authority', 1.1666666666666667), ('vendor', 1.1666666666666667), ('dicta', 1.1666666666666667), ('premises', 1.1538461538461537), ('repair', 1.1538461538461537), ('question', 1.1515151515151516), ('pursuer', 1.1428571428571428), ('manufacturer', 1.1384615384615384), ('facts', 1.1333333333333333), ('persons', 1.1333333333333333), ('subject', 1.125), ('class', 1.125), ('scotland', 1.125), ('evidence', 1.125), ('manufacturers', 1.125), ('defender', 1.125), ('contents', 1.1176470588235294), ('words', 1.1), ('longmeid', 1.1), ('holliday 6', 1.1), ('exist', 1.1), ('consequence', 1.1), ('negligence', 1.0985915492957747), ('contract', 1.0918367346938775), ('difficult', 1.0833333333333333), ('proved', 1.0833333333333333), ('respect', 1.0833333333333333), ('respondent', 1.08), ('consumer', 1.0789473684210527), ('proof', 1.0714285714285714), ('regard', 1.0714285714285714), ('manufacture', 1.0666666666666667), ('knowledge', 1.0666666666666667), ('england', 1.0588235294117647), ('langridge', 1.0555555555555556), ('action', 1.0476190476190477), ('opinion', 1.0357142857142858), ('lords', 1.0), ('ginger', 1.0), ('retailer', 1.0), ('result', 1.0), ('neglect', 1.0), ('division', 1.0), ('ground', 1.0), ('fraud', 1.0), ('judgments', 1.0), ('parke', 1.0), ('levy 2', 1.0), ('winterbottom', 1.0), ('wright 10', 1.0), ('stranger', 1.0), ('coach', 1.0), ('reason', 1.0), ('blacker', 1.0), ('breach', 1.0), ('skill', 1.0), ('parties', 1.0), ('brett', 1.0), ('heaven', 1.0), ('point', 1.0), ('treated', 1.0), ('property', 1.0), ('purpose', 1.0), ('thought', 1.0), ('existence', 1.0), ('pointed', 1.0), ('argument', 1.0), ('defendants', 1.0), ('hamilton', 1.0), ('contention', 1.0), ('mullen', 1.0), ('barr &', 1.0), ('defenders', 1.0), ('members', 1.0), ('remote', 1.0), ('bridge', 1.0)]

I was fairly chuffed with these results given it was the first attempt. The key seems to be getting the right balance of parameters when setting the object up. But, it's good to see terms like duty owed and reasonable care appearing at the top of the results. 

It definitely needs some fine tuning and probably an expansion of the stop list, but it's a good start.


Algorithmically Topic Modelling Judgments

Like many others that work in the information/publishing sector, I have developed a keen interest in learning how to make use of machine learning and text mining technology to enhance the information I work on (in my working context, the information is case law). 

Over the Christmas break I started to experiment with a statistical programming language called R, which has a decent suite of text mining functionality available right out of the box.  

I wanted to put R to use to tackle a simple and practical question: is it possible to accurately classify a judgment algorithmically with relative ease?

In order to test R against this particular use-case, I constructed a simple experiment.

Build sample corpus of data

To run the experiment I needed a small batch of sample judgments. I selected nine recent judgments from BAILII: three from the Criminal Division of the Court of Appeal; three from the Family Court; and three from the Commercial Court. 

Success Factors

For R to be successful in the experiment, it would need to algorithmically classify the nine cases to the correct topic, i.e. the three criminal cases should be grouped together, as should the commercial cases, etc. 


I’ll write up more detailed notes on the method and code used in this experiment, but essentially the following steps would be taken:

  1. Load the nine judgments into R as text files
  2. Pre-process the text files to remove unwanted material (like punctuation, numbers and standard stop words).
  3. Analyse the most frequently occurring terms in the corpus of judgments and remove additional stop words that would be common across the entire dataset.
  4. Apply a topic modelling algorithm (the Latent Dirichlet allocation (LDA) model) to the corpus of judgments to algorithmically allocate each of the judgments to one of three topics (criminal, family or commercial).
  5. Match up the judgments to a topic
  6. Produce a matrix of the key terms governing allocation into a topic
  7. Produce a matrix detailing the respective probability for each case allocation to a topic.


The LDA algorithm did a decent job of allocating judgments to one of the three topics. First off, we can take a look at the key terms the model has used to allocate each case to a topic. The first column in the chart below, for example, sets out the terms viewed as most relevant to allocate a judgment to the family topic. 

Family Commercial Criminal
1 children claus court
2 order claim evid
3 court polici appel
4 evid vote appeal
5 child period case
6 made manag convict

Now let's look at the actual allocation of judgment to topics:

a.txt Fam
adamantine.txt Comm
arc.txt Comm
canary.txt Comm
f.txt Fam
n.txt Fam
r_v_amjad.txt Crim
r_v_burke.txt Crim
r_v_garland.txt Crim

Each of the nine judgments has been allocated to the correct topic. We can also have a deeper look at the probability for each allocation.

Family Commercial Criminal
a.txt 0.66 0.13 0.21
adamantine 0.05 0.92 0.04
arc.txt 0.06 0.90 0.04
canary.txt 0.05 0.91 0.04
f.txt 0.82 0.03 0.15
n.txt 0.86 0.03 0.11
r_v_amjad.txt 0.11 0.08 0.82
r_v_burke.txt 0.14 0.10 0.76
r_v_garland.txt 0.12 0.02 0.85


This experiment shows that it is possible to use a language like R to accurately fit a topic model onto the text of judgments. However, there are two obvious limitations associated with this particular implementation of text classification.

First, the number of topics needs to be defined a priori. That's absolutely fine where you have an idea of the number of topics in advance of the modelling (as in the case of this experiment), but if you don't know how many topics there are in the corpus, you'll probably have to run the model more the once and experiment with the number of topics. 

The second issue is one of scalability. This experiment used a small corpus (only nine judgments). The larger and more varied the corpus, the more heavy lifting is required to stage the data well. 

However, regardless of these limitations, the fantastic thing about this modelling approach is the ease with which it can be deployed. 

Visualising subsequent judicial consideration: Part Two

Last week I posted a piece that explored ways in which the subsequent judicial treatment of a case could be graphically represented in a way that helps the viewer gain a bird-eye view of the basic profile of treatment a case has received. 

My first stab at a new model was to plot subsequent consideration on a radar chart, like this:

Authority Profile D3.js Radar Chart

The data in the radar is based on material subsequent considerations of Chan Wing-Siu v The Queen [1985] AC 168, which up until the beginning of this year was the leading authority on joint criminal enterprise. The problem was that whilst the radar model succeeded in drawing an easy to digest view of Chan Wing-Siu's authority profile, it had the potential to mislead because it did not take account of the importance of the most recent type of consideration the case had received. 

In the case of Chan Wing-Siu, it had enjoyed predominantly positive treatment from the mid-1980s straight through to 2016. This, when plotted on the radar chart, gave the impression that Chan Wing-Siu was still good law. However, we know that as a result of the Supreme Court's decision in R v Jogee that this is no longer the case. 

Plotting the consideration profile over time

The radar chart cannot be used to provide a sufficiently reliable account of a case's current status in law because the importance of recent events are not easily reflected in that graphic model. The answer, therefore, is to render the graphical view of the consideration profile over time

The following chart (a static version of a d3.js timeline chart), uses the same data as the radar chart above, but arranges that data chronologically. Each consideration type (e.g. applied, explained, not followed etc) is given a lane on the chart. Each case that has subsequently considered Chan Wing-Siu is then dropped into its corresponding lane (e.g. R v Barr distinguished Chan Wing-Sium, hence it sits in the "Distinguished" lane on the chart.).

Artboard Created with Sketch. Considered Followed Approved Distinguished Not followed Disapproved Applied Adopted Explained Not applied Doubted Departed from R v Barr (1986) R v Slack (1989) R v Hyde (1989) Hui Chi-ming v The Queen (1989) R v Roberts (Kelvin) (1993) R v Stewart (Heather) (1995) R v Powell (Anthony) (1997) R v Powell (Anthony) (1997) R v Rahman (2009) R v Jogee (2016)

The timeline chart is effective at demonstrating the clear run Chan Wing-Siu enjoyed until it was kicked into touch by the Supreme Court in R v Jogee.  There may be a case for collapsing the empty lanes to focus the graphic on the active consideration types, but all in all, I think the timeline does the job well.

To my knowledge, none of the legal databases (certainly in the UK) adopt this approach to mapping subsequent judicial consideration. If, I'm wrong about that, please let me know in the comments section below. 

If, on the other hand, I'm right that this method hasn't been employed before, then this is one of those head-slapping moments. 

Visualising subsequent judicial consideration: Part One

Visualisation of legal information is gathering momentum, albeit in baby steps. Without a doubt, Justis have been responsible for the pushing the envelope further than anyone else in this respect (see their Precedent Map in JustCite and text heat mapping in JustisOne).

Text visualisation tools almost certainly have a bigger role to play in the provision of online legal information. The challenge is to make sure that they are genuinely relevant and that the trends they describe are accurate. 

One facet of legal information that is ripe for useful visualisation techniques is the mapping of how a case has been treated by subsequent cases. The Justis precedent map runs along these lines: the case under analysis appears as the central node in a sort of clock dial. The nodes in the left hemisphere of the dial are the cases that have been considered by the case at the central node. The nodes in the right hemisphere of the dial are that cases that have themselves considered the case at the central node. Colour-coding is also used to denote the class of consideration, e.g. positive, negative etc. 

There is a potential use-case in which a user might wish to view the "authoritative profile" of a particular decision in a way that is not neatly satisfied by the Justis precedent map or any other tool I'm aware of. Say, for example, I wanted to form a bird's eye view of a particular case's authority profile in order to help me make an assessment as to whether it is good authority for some line of argument I'm seeking to run. What I want to be able to see, at a glance, is the rough shape of how that case has been treated by later cases. With this in mind, I tried to develop an extremely rough and ready visualisation that could assist in this situation. 

Plotting instances of subsequent consideration on a radar chart

The solution may or may not be something that runs along the lines of the radar graph below. 

The graph works by plotting points on a radar chart that correspond to the frequency over time a case has received a particular class of subsequent consideration. The classes of consideration (e.g. applied, distinguished etc) that run around the chart area are based on the granular consideration matrix developed by The Incorporated Council of Law Reporting for England and Wales. The right hemisphere consists of "positive" treatment classes, the left hemisphere consists of "negative" treatment classes. 

The larger the area of shading in the right hemisphere, the more positively the case has subsequently been treated. Conversely, the greater the area of shading in the left hemisphere, the more negatively the case has been subsequently treated. 

Authority Profile D3.js Radar Chart

The data in the radar chart above is actually based on material subsequent judicial considerations on Chan Wing-Siu v The Queen [1985] AC 168. Fourteen cases materially considered Chan Wing-Siu (that is to say, fourteen cases engaged with the Chan Wing-Siu beyond giving it mere mention). 

As the chart shows, the vast bulk of subsequent consideration has been positive, with 64% of the subsequent cases applying Chan Wing-Siu. The chart does provide an attractive visual profile of the ways in which Chan Wing-Siu has been subsequently considered, but the picture it paints of the future authoritative value of the case is where the chart falls down. 

Problems with the radar model

There are, I suppose, are a number of ways in which this data model falls down. The major problem is that is does not account for the importance of the most recent class of consideration. As we know, Chan Wing-Siu is no longer good law for the test to be applied to assess whether a defendant had sufficient intent to establish murder or grievous bodily harm in the context of a joint enterprise. The Supreme Court disapproved of the Chan Wing-Siu test earlier this year in R v Jogee [2016] UKSC 8[2016] 2 WLR 681 earlier this. The radar model is therefore misleading, because that single instance of negative consideration was sufficient to destroy the Chan Wing-Siu case's future authoritative value.  

Addressing this problem with the model

One simple way of addressing this problem with the radar model might simply be to assign a signal to the most recent class of consideration. That way, the user is placed on notice about the most recent class of consideration the case under analysis received and is able to adjust their assessment of the case accordingly. This seems a little clunky.

The better way, perhaps, is to admit that whilst the radar model is effective insofar as it is able to graphically demonstrate the basic profile of the sorts of subsequent consideration a case has received, a more effective method might be provide an additional graphical representation of consideration over time. I'll set out a time-based graphical model in a later post. 

Building the radar model

The radar model featured in this blog post was written using the d3.js library. The code borrows heavily from the radar chart function written by Nadieh Bremer ( and the original code (free of my hacking) can be found at on the excellent website here