Developing Categories, Part 3

stuff8I’ve already said that I wasn’t sure if ‘behaviour’ and ‘feedback, assessment & marking’ (FAM) should be separate categories, and some further analysis has convinced me that I need to drop them both.

One of the many useful features of Orange is the ‘concordance’ app, shown on the left in my workflow.  It allows for a sub-set of documents to be extracted based on a key word.  I chose to have a closer look at ‘marking’.  As you can see from the screenshot below, the app will show you your chosen word as it appears with a selected number of words either side.  The default is 5, which I stuck with.

stuff9

The white and blue bands represent individual documents, which can then be selected and viewed using the ‘corpus viewer’ app.  I browsed through several, deciding that they should best be classed as ‘professional concern’, ‘positioning’, ‘soapboxing’ or ‘reflective practice’.  I selected ‘assessment’ and ‘feedback’ as alternatives to ‘marking’, but a closer look at a few of them suggested the same.  I went back to the posts I’d originally classified as ‘FAM’ and reviewed them, and decided I could easily re-categorise them too.

Here’s an example of a post containing the key word ‘marking’:

Lesson 3 (previous post) had seen my Head of Department sit in with a year 7 group to look at ideas he could apply. His key observation- the need for a grounding in the terminology and symbols see first lesson which has been shared as a flipchart with the department. We move on apace to lesson 4 where pupils start to be involved in setting their own marking criteria linked to SOLO. Still no hexagons, a key aspect in the sequence of lessons now being blogged about by Paul Berry (see previous post). Linking activities between lessons has become very overt in this sequence of lessons. Our starter was a return to annotate the pics from last lesson. Most recall was at a uni-structural stage and some discussion ensued see Year 7 example below. The focus today was to be on marking information onto maps accurately. We have decided as a department to return to more traditional mapping skills as many of our pupils have a lack of sense of place. So we returned to the textbook (Foundations) and a copy of the main map was shared with the class. It limits the amount of information, and hopefully this will develop a stronger use of maps in future work. Before starting though we needed to determine a SOLO based marking criteria which allowed peer marking. The pupils in year 7 in particular had clear ideas already about this. We identified how they would mark and initial and day the marking as Sirdoes so it was clear who the peer marker was. The map task was time limited. I use a variety of flash based timers which I found online- the novelty value of how the timer will end can be a distraction at the end of a task but does promote pupil interest. I circulated the room giving prompts on how seas could include other terms e.g. Channel and ocean. The work rate was very encouraging. The peer marking was successful and invoked quite a lot of table based discussions. We started to identify the idea of feed forward feedback to allow improvement of future pieces of work. Lesson 4 with years 8 and 9 included a return to the SOLO symbols image sheet and sharing recall. Also a key facts based table quiz was used to promote teamwork and remind how we already know a range of facts. These quizzes provided a good opportunity to use the interactive nature of the board to match answers to locations. Writing to compare features in different locations became the focus for Years 8 and 9. We recapped the use of directions in Relational answers. Headings were provided and I circulated to support and/ or prompt as required. Now I need to identify opportunities to use HOT maps as recommended by others including Lucie Golton, John Sayers et al. from Twitters growing #SOLO community. Also the mighty hexagons and linking facts need to enter the arena. Please if commenting, which image size works better as lesson 3 or lesson 4?

This is clearly ‘reflective practice’, as the practitioner is clearly commenting on the successes of using the SOLO taxonomy model  with a variety of year groups.

If I have time, it may well be more appropriate to interrogate a particular category to visualise what sub-categories may emerge e.g.  I would expect ‘professional concern’ to encompass workload, marking, growth mindset, flipped  learning etc. , areas of concern that are ‘product’ as opposed to ‘process’.

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Developing Categories, Part 2

So, while I deploy my bespoke python code to scrape the contents of umpteen WordPress and Blogger blogs, I’ve continued trying to classify blogs from my sample according the the categories I outlined in my previous post.

I say ‘trying’ because it’s not as straightforward as it seems.  Some blogs clearly don’t fit into any of the categories, e.g. where a blogger has simply written about their holiday, or for one blogger written a series of posts explaining various aspects of science or physics.  I reckon that this a science teacher writing for the benefit of his or her students, but as the posts are sharing ‘knowledge’ rather than ‘resources’, I can’t classify them.  Fortunately the label propagation algorithm I will eventually be using will allow for new categories to be instigated (or the ‘boundaries’ for existing categories to be softened) so it shouldn’t be a problem.

‘Soapboxing’, ‘professional concern’ and ‘positioning’ have also caused me to think carefully about my definitions.  ‘Soapboxing’ I’m counting as all posts that express an opinion in a strident, one-sided way,  with a strong feeling that the writer is venting frustration, and perhaps with a call to action.  These tend to be short posts, probably written because the blogger simply needs to get something off their chest and (possibly, presumably) get some support from others via the comments.  ‘Professional concern’, then, is a also post expressing a view or concern, but the language will be more measured.  Perhaps evidence from research or other bloggers will be cited, and the post will generally be longer.  The blogger may identify themselves as a teacher of some experience, or perhaps a head of department or other school leader.  As with ‘soapboxing’, a point of view will be expressed, but the call to action will be absent.

‘Positioning’ is a blog post that expresses a belief or method that the blogger holds to be valid above others, and expresses this as a series of statements.  Evidence to support the statements will be present, generally in the form of books or published research by educational theorists or other leading experts in the field of education.

Of course, having made some decisions regarding which blogs fit into these categories, I need to go back through some specific examples and try to identify some specific words or phrases that exemplify my decision.  And I fully expect other people to disagree with me, and be able to articulate excellent reasons why blog A is an example of ‘positioning’ rather than ‘professional concern’, but all I can say in response is that, while it’s possible to get a group of humans to agree around 75% of the time, it’s impossible to get them to agree 100%, and that’s but the joy and the curse of this kind of research.

Given more time, I’d choose some edu-people from Twitter and ask them to categorise a sample of blogs to verify (or otherwise) my decision, but as I don’t have that luxury the best I can do is make my definitions as clear as possible, and provide a range of examples as justification.

The other categories that aren’t proving straightforward are ‘feeedback, assessment and marking’ (‘FAM’) and ‘behaviour’.  I knew this might be the case, though, so I’m keeping an open mind about these.  I have seen examples of blogs discussing ‘behaviour’ that I’ve put into one of the three categories I’ve mentioned above, but that’s because the blogs don’t discuss ‘behaviour’ exclusively.

Anyway, I’ve categorised 284 (out of a total of 7,788) posts so far so I thought I’d have a bit of a look at the data.

stuff1

I used Orange again to get a bit more insight into my data.  Just looking at the top flow, after opening the corpus I selected the rows that had something entered in the ‘group’ column I created.

stuff2

Selecting rows.

stuff3

Creating classes.

 

 

 

 

 

 

 

 

I then created a class for each group name.  This additional information can be saved, and I’ve dragged the ‘save data’ icon onto the workspace, but I’ve chosen not to save it automatically for now.  If you do, and you give it a file name, every time you open Orange the file will be overwritten, which you may not want.  Then, I pre-processed the 284 blogs using the snowball stemmer, and decided I’d have a look at how just the sample might be clustered using k-means.

“Since it effectively provides a ‘suffix STRIPPER GRAMmar’, I had toyed with the idea of calling it ‘strippergram’, but good sense has prevailed, and so it is ‘Snowball’ named as a tribute to SNOBOL, the excellent string handling language of Messrs Farber, Griswold, Poage and Polonsky from the 1960s.”

Martin Porter

I’m not sure if I’ve explained k-means before, but here’s a nice link that explains it well.

“Clustering is a technique for finding similarity groups in a data, called clusters. It attempts to group individuals in a population together by similarity, but not driven by a specific purpose.”

The data points are generated from the words in the blogs.  These have been reduced to tokens by the stemmer, then a count is made of the number of times each word is used in a post.  The count is subsequently adjusted to take account of the length of the document so that a word used three times in a document of 50 words is not given undue weight compared with the same word used three times in a document of 500.  So, each document generates a score for each word used, with zero for a word not used that appears in another document or documents.  Mathematical things happen and the algorithm coverts each document into a data point in a graph like the ones in the link.  K-means then clusters the documents according to how similar they are.

I already know I have 8 classes, so that’s the number of clusters I’m looking for.  If I deploy the algorithm, I can see the result on a silhouette plot (the matching icon, top far right of the flow diagram above).  The closer to a score of ‘0’, the more likely it is that a blog post is on the border between two clusters.  When I select that the silhouette plot groups each post by cluster, it’s clear that ‘resources’ has a few blogs that are borderline.

Stuff4

stuff5

 

 

 

 

 

 

‘FAM’ and ‘behaviour’ are more clearly demarcated.  If I let the algorithm choose the optimal number of clusters (Orange allows between 2 and 30), the result is 6, although 8 has a score of 0.708 which is reasonable (as you can see, the closer to 1 the score is, the higher the probability that the number suggested is the ‘best fit’ for the total number  of clusters within the data set).

stuff6 As you can see from the screenshot below, cluster 4 is made up of posts from nearly all the groups.  Remember, though, that this algorithm is taking absolutely no notice of my categories, or the actual words as words that convey meaning.  It’s just doing what it does  based on numbers, and providing me with a bit of an insight into my data.

stuff7

Developing Categories

AF

An initial estimate of the possible number of categories in the 25% sample my nine-thousand-odd list of blog posts, provided by the Affinity Propagation (AP) algorithm, suggested over 100 categories.   Based on the words used in the posts it chose to put into a cluster, this was actually reasonable although way more than I can process.  It was also obvious that some of the categories could have been combined:  maths- and science-based topics often appeared together, for example.

A different method provided by an algorithm in Orange (k-means, allowing the algorithm to find a ‘fit’ of between 2 and 30 clusters) suggested three or four clusters.  How is it possible for algorithms, using the same data, to come up with such widely differing suggestions for clusters?  Well, it’s maths.  No doubt a mathematician could explain to me (and to you) in detail how the results were obtained, but for me all the explanation I need is that when you start converting words to numbers and use the results to decide which sets of numbers have greater similarity, you get a result that, while useful, completely disregards the nuances of language.

An initial attempt by me to review the content of the categories suggested by AP, but I had to give up after a few hours’ work.  I identified a good number of potential categories, including the ones suggested by the literature (see below), but I soon realised that it was going to be difficult to attribute some posts to a specific category.  A well-labelled training set is really important, even if it’s a small training set.  So, back to the research that has already been published, describing he reasons why teachers and other edu-professionals blog, and a chat with my supervisor, who made the observation that I needed to think about ‘process’ as opposed to ‘product’.

Bit of a lightbulb moment, then.  I’m not trying to develop a searchable database of every topic covered – I’m trying to provide a summary of the most important aspects of teaching discussed in blogs over a period of time.   The categories arising from the literature are clearly grounded in process, and so these are the ones I’ll use.  If you click on this link, you’ll be able to see the full version of the Analytical Framework, a snippet of which is pictured above.

As well as the main categories (the ones in the blue boxes), I decided to add two more: ‘behaviour’ and ‘assessment /  feedback / marking’ simply because these, in my judgement, are important enough topics to warrant categories of their own.  However, I’m aware that they overlap with all the others, and so I may revise my decision in the light of results.  What I’ll have to do is provide clear definitions of each category, linked with the terms associated with the relevant posts.

What will be interesting is exploring each category.  The ‘concordance‘ widget in Orange allows for some of the key terms to be entered, and to see how  they’re used in posts.  This will add depth to the analysis, and may even lead to an additional category or two if it appears, for example, that ‘Ofsted’ dominated blogs within the ‘professional concern’ category for a considerable period of time, an additional category would be justified.  My intention is to divide my data into sets by year (starting at 2004), although it may be prudent to sub-divide later years as the total number of blog posts increases year on year.

Clustering Blog Posts: Part 3

No interesting visuals this time.  I’ve been spending my Saturday going back and hand-labelling what will become a training set of blog posts.

I should have done this before now, but I’ve been putting it off, mainly because it’s so tedious.  I have my sample of 2,316 blogs grouped into 136 clusters, and I’m going through them, entering the appropriate labels in the spreadsheet.  Some background reading has made it clear that a set of well-labelled data, even a small set, is extremely beneficial to a clustering algorithm.  The algorithm can choose to add new documents to the already established set, start a new set, or modify the parameters of the labelled set slightly to include the new document.  Whatever it decides, it ‘learns’ from the examples given, and the programmer can test the training set on another sample to refine the set before launching it on the entire corpus.

There has been some research into the kind of things teachers blog about.  The literature suggests the following categories:

  1. sharing resources;
  2. building a sense of connection;
  3. soapboxing;
  4. giving and receiving support;
  5. expressing professional concern;
  6. positioning.

Some of these are clearly useful – there are plenty of resource-themed blogs, although I instinctively want to label resource-sharing blogs with a reference to the subject.  ‘Soapboxing’ and ‘expressing professional concern’ appear relatively straightforward.  ‘Positioning’ refers to the  blogger ‘positioning themselves in the community i.e. as an ex-
pert practitioner or possessor of extensive subject knowledge’.  That may be more problematic, although I haven’t come across a post that looked as if it might fit into that category yet.  The ones  that are left – ‘support’ and ‘connection’ are very difficult, grounded as they are in the writers’ sense of feeling and emotion.  I’m not sure they’re appropriate as categories.

The other category that emerges from current research is ‘reflective practice’.   I’ve already come across several blog posts discussing SOLO taxonomy which could be categorised as just that – SOLO taxonomy – or ‘reflective practice’ or ‘positioning’ or ‘professional concern’.   My experience as a teacher (and here’s researcher bias again) wants to (and already has) labelled these posts as SOLO, because it fits better with my research questions, in the same way that I’m going to label some posts ‘mindset’ or ‘knowledge organiser’.  What I may do – because it’s easy at this stage – is to create two labels where there is some overlap with the existing framework suggested by the literature, which may be useful later.

It’s also worth mentioning that I’m basing my groups on the content of the blog posts.  An algorithm counts the number of times all the words in the corpus are used in each post (so many will be zero) and then adjusts the number according  to the length of the document in which it appears.  Thus, each word becomes a ‘score’ and it’s these that are used to decide which documents are most similar to one another.   Sometimes, it’s clear why the clustering algorithm has made the decision is has, other times it’s not, and this is why I’m having to go through the laborious process of hand-labelling.  Often, the blog post title makes the subject of the content clear, but not always.

Teachers and other Edu-professionals, Gods-damn them, like to be creative and cryptic when it comes to titling their blogs, and they often draw on metaphors to explain the points they’re trying to make, all of which expose algorithms that reduce language to  numbers as the miserable , soulless and devoid-of-any-real-intelligence things they are.  How very dare they.

 

Clustering Blog Posts: Part 2 (Word Frequency)

One of the most important things to do when working with a lot of data is to reduce the dimensionality of that data as far as possible.  When the data you are working with is text, this is done by reducing the number of words used in the corpus without compromising the meaning of the text.

One of the most fascinating things about language was discovered by G K Zipf in 1935¹: that the most frequently used words in (the English) language are actually few in number, and obey a ‘power law’.   The most frequently used word occurs twice as often as the next most frequently word, three times as often as the third, and so on.  Zipf’s law forms a curve like this:
Zipf-CurveThe distribution seems to apply to languages other than English, and it’s been tested many times, including using the text of, for example, novels.  It seems we humans are very happy to come up with a rich and varied lexography, but then rely on just a few to communicate with each other.  This makes perfect sense as far as I can see: I say I live on a boat, gets the essentials across (a thing that floats, a bit of an alternative lifestyle, how cool am I? etc.) because were I to say I live on a lifeboat, I then have to explain that it’s like one of the fully-enclosed ones you see hanging from the side of cruise ships, not the open Titanic-style ones most people would imagine.

“For language in particular, any such account of the Zipf’s law provides a psychological theory about what must be occurring in the minds of language users. Is there a multiplicative stochastic process at play? Communicative optimization? Preferential reuse of certain forms?” (Piantadosi, 2014)

A recent paper by Piantadosi² reviewed some of the research on word frequency distributions, and concluded that, although Zipf’s law holds broadly true, there are other models that provide a more reliable picture of word frequency which depend on the corpus selected.  Referring to a paper by another researcher, he writes “Baayen finds, with a quantitative model comparison, that which model is best depends on which corpus is examined. For instance, the log-normal model is best for the text The Hound of the Baskervilles, but the Yule–Simon model is best for Alice in Wonderland.”

I’m not a mathematician, but that broadly translates as ‘there are different ways of calculating word frequency, you pays your money you takes your choice”.  Piantadosi then goes on to explain the problem with Zipf’s law: it doesn’t take account of the fact that some words may occur more frequently that others purely by chance, giving the illusion of an underlying structure where none may exist.  He then goes on to suggests a way to overcome this problem, which is to use two independent corpora, or split a corpora in half and then test word frequency distribution in each. He then tests a range of models, and concludes that the “…distribution in language is only near-Zipfian.” and concludes “Therefore, comparisons between simple models will inevitably be between alternatives that are both “wrong.” “.

Semantics also has a strong influence on word frequency.  Piantadosi cites a study³ that compared 17 languages across six language families and concluded that simple words are used with greater frequency in all of them, and result in a near-Zipfian model.  More importantly for my project, he notes that other studies indicate that word frequencies are domain-dependent.   Piantadosi’s paper is long and presents a very thorough review of research relating to Zipf’s law, but the main point is that it does exist, even though why it should be so is still unclear.  The next question is should the most frequently used words from a particular domain also be removed?

As I mentioned before, research has already established that it’s worth removing (at least as far as English is concerned) a selection of words.  Once that’s done, which are the most frequently used words in my data?  I used Orange is to split my data in half and generate three word clouds based on the same parameters, and observe the result.  Of course I’m not measuring the distribution of words, I’m just doing a basic word count and then displaying the results, but it’s a start.  First, here’s my workflow:

WFD1

I’ve shuffled (randomised) my corpus, taken a training sample of 25%, and then split this again into two equal samples.  Each of these has been pre-processed using the following parameters:

WFD2

Pre-processing parameters. I used the lemmatiser this time.

The stop word set is the extended set of ‘standard’ stop words used by Scikit that I referred to in my previous post, plus a few extra things to try and get rid of some of the rubbish that appears.

The word clouds for the full set, and each separate sample, look like this:

WC1

Complete data set (25% sample, 2316 rows)

WC2

50% of sample (1188 rows)

WC3

Remaining 50% of sample

The graph below plots the frequency with which the top 500 words occur.

WFDGraph

So, I can conclude that based on word counts, each of my samples is similar to each other, and to the total (sampled) corpus.  This is good.

So, should I remove the most frequently used words, and if so, how many?  Taking the most frequently used words across each set, and calculating the average for each word, gives me a list as follows:

table1

And if I take them out, the word cloud (based on the entire 25% set) looks like this:

WCouldLemSWset3

Which leads me to think I should take ‘learning’ and ‘teaching’ out as well.  It’s also interesting that the word ‘pupil’ has cropped up here – I wonder how many teachers still talk about pupils rather than students?  Of course, this data set contains blogs that may be a few years old, and/or be written by bloggers who prefer the term.  Who knows?  In fact, Orange can tell me.  The ‘concordance’ widget, when connected to the bag of words, tells me that ‘pupil’ is used in 64 rows (blogs) and will show me a snippet of the sentence.

concordance1

It’s actually used a total of 121 times, and looking at the context I’m not convinced it adds value in terms of helping me with my ultimate goal, which is clustering blog posts by topic, so it’s probably worth mentioning here that the words used the least often are going to be the most numerically relevant when it comes to grouping blogs by topic.

WCouldLemSWset4

Could I take out some more?  This is a big question.  I don’t want to remove so many words that the data becomes difficult to cluster.  Think of this as searching the blog posts using key words, much as you would when you search Google.  Where, as a teacher, you might want to search ‘curriculum’, you might be more interesting in results that discuss ‘teaching (the) curriculum’  rather than those that cover ‘designing (the) curriculum’.  If ‘teaching’ has already been removed, how will you find what you’re looking for?  Alternatively, does it matter so long as search returns everything that contains the word ‘curriculum’?  You may be more interested in searching for ‘curriculum’ differentiating by key stage.  For my purposes, I think I’d be happy with a cluster labelled ‘curriculum’ that covered all aspects of the topic.  I’ll be able to judge when I see some actual clusters emerge, and have the chance to examine them more closely.  Which, incidentally, the concordance widget tells me is used in 93 blogs, and appears 147 times.  That’s more than ‘pupil’, but because of my specialised domain knowledge I judge to be more important to the corpus.

Which is also a good example of researcher bias.

  1. Zipf, G. K.; The Psychology of Language; 1966; The M.I.T. Press.
  2. Piantadosi, S.; Zipf’s word frequency law in natural language: A critical review and future Directions; Journal of National Institutes of Health; 2014; volume 21, October issue, pages 1112-1130.
  3. Calude, A., Pagel, M.;  How do we use language? Shared patterns in the frequency of word use across 17 world languages; 2011; Journal of Philosophical Transactions of the Royal Society of London. Series B, Biological sciences; volume 366, issue 1567, pages 1101-7.

Clustering Blog Posts: Part 1

Today, while my laptop was employed scraping Edu-blog posts from 2011, I decided to play around with Orange.  This is one of the suite of tools offered by Anaconda which I use for all my programming needs.

The lovely thing about Orange is that it allows you to build a visual workflow,  while all the actual work in the form of the lines and lines of code is done behind the scenes.  You have to select some parameters, which can be tricky, but all the heavy lifting is done for you.

This was my workflow today, although this was about halfway through so by the time I’d finished, there were a few more things there.  Still, it’s enough to show you how it works.

workflow

My corpus is a sample of 9,262 blog posts gathered last year.  Originally, there were over 11,000 posts but they’ve been whittled down by virtue of having no content, having content that had been broken up across several rows in the spreadsheet, or being duplicates.  I also deleted a few that simply weren’t appropriate, usually because they were written by educational consultants, as means to sell something other tangible such as books or software, or political in some way such as blogs written for one of the teaching unions.  What I’ve tried to do is identify blog URLs that contain posts by individuals, preferably but not exclusively teachers, with a professional interest in education and writing from an individual point of view.  This hasn’t been easy, and I’m certain that when I have the full set of data (which will contain many tens of thousands of blog posts) some less than ideal ones will have crept in, but that’s one of the many drawbacks of dealing with BIG DATA:  it’s simply too big to audit.

You may recall that the point of all this is to classify as much as the Edu-blogosphere as I possibly can –  to see what Edu-professionals talk about, and to see if the topics they discuss change over time.  Is there any correlation between, for example, Michael Gove being appointed Secretary of State for Education and posts discussing his influence?  We’ll see.  First of all, I have to try and cluster the posts into groups according to content.  I’ve been doing this already, and developed a methodology.  However, while I’m still gathering data, and labelling a set of ‘training data’ (of which more in a future blog post) I’ve been experimenting with a different set of tools.

So, here’s my first step using Orange.  Open the corpus, shuffle (randomise) the rows, and take a sample of 25% for further analysis, which equates to 2316 documents or rows.

corpus

Open the document, select the data you need, e.g. ‘Content’.  The other features can still be accessed.

 

 

Step1

The ‘corpus viewer’ icons along the top of the workflow shown above mean that I can see the corpus at each stage of the process.  This is a glimpse of the second one, after shuffling.  Double-clicking any of the icons brings up a view of the output, as well as the various options available for selection.

viewer2

The next few steps are will give me an insight into the data, and already there are a series of decisions to make.  I’m only interested in  pre-processing the content of each blog post.  The text has to be broken up into separate words, or ‘tokens’, punctuation removed, plus any URLs that are embedded in the text.  In addition, other characters such as /?&* or whatever are also stripped out.  So far, so straightforward as this screen grab shows:

 

preprocess1

Words can also be stemmed or lemmatised (see above).

Stemming usually refers to a crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time, and often includes the removal of derivational affixes.  Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma . If confronted with the token saw, stemming might return just s, whereas lemmatization would attempt to return either see or saw depending on whether the use of the token was as a verb or a noun. ”  The Stanford Natural Language Processing Group

If I lammatise the text as is, I get this lovely word cloud. BTW, it’s just a coincidence that ‘example’ is in red.WCloudLemmatiser

As you would expect from blogs written by teachers, words like ‘student’ and ‘teacher’ feature heavily.  If I use the snowball stemmer (which is basically an efficient algorithm for stemming, explained here) then the word cloud looks like this:

WCloudBespokeStopWords

Both of these word clouds are also generated from the corpus after a series of words know as ‘stop words’ are removed.  These words are the ones we often use most frequently, but add little value to a text; words such as ‘the’, ‘is’, ‘at’ or ‘which’.  There is no agreed standard, although most algorithms use the list provided by the Natural Language Toolkit (NLTK).  I’ve chosen to use the list provided by Scikit-Learn, a handy module providing lots of useful algorithms.  Their list is slightly longer.  The use of stop words is well researched and recommended to reduce the number of unique tokens (words) in the data, otherwise referred to by computer scientists as dimensionality reduction.   I also added some other nonesense that I noticed when I was preprocessing this data earlier in the year- phrases like ‘twitterfacebooklike’ – so in the end I created my own list combining the ‘standard’ words and the *rap, and copied them into a text file.  This is referred to as ‘NY17stopwordsOrange.txt’ in the screenshot below.

stopwords

My next big question, though, is what happens if I add to the list some of the words that are most frequently used in my data set – words like ‘teach’, ‘student’, ‘pupil’, ‘year’?  So I added these words to the list: student, school, teacher, work, year, use, pupil, time, teach, learn, use.  This is the result, using the stemmer:

WCloudStopWordsSet2

There is research to suggest that creating a bespoke list of stop words that is domain-specific is worth doing as a step before going on to try and classify a set of documents.  It’s the least-used words in the corpus that are arguably the most interesting and valuable.  I’ll explore this some more in the next post, along with the following steps in the workflow.