Last week I had the pleasure of talking at both BrightonPython and DataScienceLondon to about 150 people in total (Robin East wrote-up the DataScience night). The updated code is in github.

The goal is to disambiguate the word-sense of a token (e.g. “Apple”) in a tweet as being either the-brand-I-care-about (in this case – Apple Inc.) or anything-else (e.g. apple sauce, Shabby Apple clothing, apple juice etc). This is related to named entity recognition, I’m exploring simple techniques for disambiguation. In both talks people asked if this could classify an arbitrary tweet as being “about Apple Inc or not” and whilst this is possible, for this project I’m restricting myself to the (achievable, I think) goal of robust disambiguation within the 1 month timeline I’ve set myself.

Below are the slides from the longer of the two talks at BrightonPython:

As noted in the slides for week 1 of the project I built a trivial LogisticRegression classifier using the default CountVectorizer, applied a threshold and tested the resulting model on a held-out validation set. Now I have a few more weeks to build on the project before returning to consulting work.

Currently I use a JSON file of tweets filtered on the term ‘apple’, obtained using the free streaming API from Twitter using cURL. I then annotate the tweets as being in-class (apple-the-brand) or out-of-class (any other use of the term “apple”). I used the Chromium Language Detector to filter non-English tweets and also discard English tweets that I can’t disambiguate for this data set. In total I annotated 2014 tweets. This set contains many duplicates (e.g. retweets) which I’ll probably thin out later, possibly they over-represent the real frequency of important tokens.

Next I built a validation set using 100 in- and 100 out-of-class tweets at random and created a separate test/train set with 584 tweets of each class (a balanced set from the two classes but ignoring the issue of duplicates due to retweets inside each class).

To convert the tweets into a dense matrix for learning I used the CountVectorizer with all the defaults (simple tokenizer [which is not great for tweets], minimum document frequency=1, unigrams only).

Using the simplest possible approach that could work – I trained a LogisticRegression classifier with all its defaults on the dense matrix of 1168 inputs. I then apply this classifier to the held-out validation set using a confidence threshold (>92% for in-class, anything less is assumed to be out-of-class). It classifies 51 of the 100 in-class examples as in-class and makes no errors (100% precision, 51% recall). This threshold was chosen arbitrarily on the validation set rather than deriving it from the test/train set (poor hackery on my part), but it satisfied me that this basic approach was learning something useful from this first data set.

The strong (but not generalised at all!) result for the very basic LogisticRegression classifier will be due to token artefacts in the time period I chose (March 13th 2013 around 7pm for the 2014 tweets). Extracting the top features from LogisticRegression shows that it is identifying terms like “Tim”, “Cook”, “CEO” as significant features (along with other features that you’d expect to see like “iphone” and “sauce” and “juice”) – this is due to their prevalence in this small dataset (in this set examples like this are very frequent). Once a larger dataset is used this advantage will disappear.

I’ve added some TODO items to the README, maybe someone wants to tinker with the code? Building an interface to the open source DBPediaSpotlight (based on WikiPedia data using e.g. this python wrapper) would be a great start for validating progress, along with building some naive classifiers (a capital-letter-detecting one and a more complex heuristic-based one, to use as controls against the machine learning approach).

Looking at the data 6% of the out-of-class examples are retweets and 20% of the in-class examples are retweets. I suspect that the repeated strings are distorting each class so I think they need to be thinned out so we just have one unique example of each tweet.

Counting the number of capital letters in-class and out-of-class might be useful, in this set a count of <5 capital letters per tweet suggests an out-of-class example:

This histogram of tweet lengths for in-class and out-of-class tweets might also suggest that shorter tweets are more likely to be out-of-class (though the evidence is much weaker):

Next I need to:

• Update the docs so that a contributor can play with the code, this includes exporting a list of tweet-ids and class annotations so the data can be archived and recreated
• Spend some time looking at the most-important features (I want to properly understand the numbers so I know what is happening), I’ll probably also use a Decision Tree (and maybe RandomForests) to see what they identify (since they’re much easier to debug)
• Improve the tokenizer so that it respects some of the structure of tweets (preserving #hashtags and @users would be a start, along with URLs)
• Build a bigger data set that doesn’t exhibit the easily-fitted unigrams that appear in the current set

Longer term I’ve got a set of Homeland tweets (to disambiguate the TV show vs references to the US Department and various sayings related to the term) which I’d like to play with – I figure making some progress here opens the door to analysing media commentary in tweets.

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

As noted a few days back I’m spending June working on a social-media focused brand disambiguator using Python, NLTK and scikit-learn. This project has grown out of frustrations using existing Named Entity Recognition tools (like OpenCalais and DBPediaSpotlight) to recognise brands in social media messages. These tools are generally trained to work on long-form clean text and tweets are anything but long or cleanly written!

The problem is this: in a short tweet (e.g. “Loving my apple, like how it werks with the iphon”) we have little context to differentiate the sense of the word “apple”. As a human we see the typos and deliberate spelling errors and know that this use of “apple” is for the brand, not for the fruit. Existing APIs don’t make this distinction, typically they want a lot more text with fewer text errors. I’m hypothesising that with a supervised learning system (using scikit-learn and NLTK) and hand tagged data I can outperform the existing APIs.

I started on Saturday (freshly back from honeymoon), a very small github repo is online. Currently I can ingest tweets from a JSON file (captured using curl), marking the ones with a brand and those with the same word but not-a-brand (in-class and out-of-class) in a SQLite db. I’ll benchmark my results against my hand-tagged Gold Standard to see how I do.

Currently I’m using my Python template to allow environment-variable controlled configurations, simple logging, argparse and unittests. I’ll also be using the twitter text python module that I’m now supporting to parse some structure out of the tweets.

I’ll be presenting my progress next week at Brighton Python, my goal is to have a useful MIT-licensed tool that is pre-trained with some obvious brands (e.g. Apple, Orange, Valve, Seat) and software names (e.g. Python, vine, Elite) by the end of this month, with instructions so anyone can train their own models. Assuming all goes well I can then plumb it into my planned annotate.io online service later.

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

Having returned from Chile last year, settled in to consulting in London, got married and now on honeymoon I’m planning on a change for June.

I’m taking the month off from clients to work on my own project, an open sourced brand disambiguator for social media. As an example this will detect that the following tweet mentions Apple-the-brand:
“I love my apple, though leopard can be a pain”
and that this tweet does not:
“Really enjoying this apple, very tasty”

I’ve used AlchemyAPI, OpenCalais, DBPedia Spotlight and others for client projects and it turns out that these APIs expect long-form text (e.g. Reuters articles) written with good English.

Tweets are short-form, messy, use colloquialisms, can be compressed (e.g. using contractions) and rely on local context (both local in time and social group). Linguistically a lot is expressed in 140 characters and it doesn’t look like”good English”.

A second problem with existing APIs is that they cannot be trained and often don’t know about European brands, products, people and places. I plan to build a classifier that learns whatever you need to classify.

Examples for disambiguation will include Apple vs apple (brand vs e.g. fruit/drink/pie), Seat vs seat (brand vs furniture), cold vs cold (illness vs temperature), ba (when used as an abbreviation for British Airways).

The goal of the June project will be to out-perform existing Named Entity Recognition APIs for well-specified brands on Tweets, developed openly with a liberal licence. The aim will be to solve new client problems that can’t be solved with existing APIs.

I’ll be using Python, NLTK, scikit-learn and Tweet data. I’m speaking on progress at BrightonPy and DataScienceLondon in June.

Probably for now I should focus on having no computer on my honeymoon…

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

Recently I’ve been grabbing Tweets some some natural language processing analysis (in Python using NetworkX and NLTK) – see this PyCon and PyData conversation analysis. Using the London dataset (visualised in the PyData post) I wondered if the geo-tagged tweets would give a good-looking map of London. It turns out that it does:

You can see the bright centre of London, the Thames is visible wiggling left-to-right through the centre. The black region to the left of the centre is Hyde Park. If you look around the edges you can even see the M25 motorway circling the city. This is about a week’s worth of geo-filtered Tweets from the Twitter 10% firehose. It is easier to locate using the following Stamen tiles:

Can you see Canary Wharf and the O2 arena to its east? How about Heathrow to the west edge of the map? And the string of reservoirs heading north north east from Tottenham?

Here’s a zoom around Victoria and London Bridge, we see a lot of Tweets around the railway stations, Oxford Street and Soho. I’m curious about all the dots in the Thames – presumably people Tweeting about their pleasure trips?

Here’s a zoom around the Shoreditch/Tech City area. I was surprised by the cluster of Tweets in the roundabout (Old Street tube station), there’s a cluster in Bonhill Street (where Google’s Campus is located – I work above there in Central Working). The cluster off of Old Street onto Rivington Street seems to be at the location of the new and fashionable outdoor eatery spot (with Burger Bear). Further to the east is a more pubby/restauranty area.

I’ve yet to analyse the content of these tweets (doing something like phrase extraction from the PyCon/PyData tweets onto this map would be great). As such I’m not sure what’s being discussed, probably a bunch of the banal along with chitchat between people (“I”m on my way”…). Hopefully some of it discusses the nearby environment.

I’m using Seth’s Python heatmap (inspired by his lovely visuals). In addition I’m using Stamen map tiles (via OpenStreetMap). I’m using curl to consume the Twitter firehose via a geo-defined area for London, saving the results to a JSON file which I consume later (shout if you’d like the code and I’ll put it in github) – here’s a tutorial.

During London Fashion Week I grabbed the tagged tweets (for “#lfw’ and those mentioning “london fashion week” in the London area), if you zoom on the official event map you’ll see that the primary Tweet locations correspond to the official venue sites.

What about Brighton? Down on the south coast (about 1 hour on the train south of London), it is where I’ve spent the last 10 years (before my recent move to London). You can see the coastline, also Sussex University’s campus (north east corner). Western Road (the thick line running west a little way back from the sea) is the main shopping street with plenty of bars.

It’ll make more sense with the Stamen tiles, Brighton Marina (south east corner) is clear along with the small streets in the centre of Brighton:

Zooming to the centre is very nice, the North Laines are obvious (to the north) and the pedestriansed area below (the “south laines”) is clear too. Further south we see the Brighton Pier reaching into the sea. To the north west on the edge of the map is another cluster inside Brighton Station:

Finally – what about all the geo-tagged Tweets for the UK (annoyingly I didn’t go far enough west to get Ireland)? I’m pleased to see that the entirety of the mainland is well defined, I’m guessing many of the tweets around the coastline are more from pretty visiting points.

How might this compare with a satellite photograph of the UK at night? Population centres are clearly visible but tourist spots are far less visible, the edge of the country is much less defined (via dailymail):

I’m guessing we can use these Tweets for:

• Understanding what people talk about in certain areas (e.g. Oxford Street at rush-hour?)
• Learning why foursquare checkings (below) aren’t in the same place as tweet locations (can we filter locations away by using foursquare data?)
• Seeing how people discuss the weather – is it correlated with local weather reports?
• Learning if people talk about their environment (e.g. too many cars, poor London tube climate control, bad air, too noisy, shops and signs, events)
• Seeing how shops, gigs and events are discussed – could we recommend places and events in real time based on their discussion?
• Figuring out how people discuss landmarks and tourist spots – maybe this helps with recommending good spots to visit?
• Looking at the trail people leave as they Tweet over time – can we figure out their commute and what they talk about before and after? Maybe this is a sort of survey process that happens using public data?

Here are some other geo-based visualisations I’ve recently seen:

• Nice video of Oyster London Underground checkins from 2012 (write-up)
• FourSquare’s 500,000 check-in visualisation (Jan blog post) for the world, zoom on London to see how the map is different to the tweet data I have above
• Another FourSquare check-in visualisation just for London filtered by location-type
• Language-tagged geo-tweets for New York
• Language-tagged geo-tweets for London
• Language-tagged geo-tweets for Europe (uses the Chromium compact language detector)

If you want help with this sort of work then note that I run my own AI consultancy, analysing and visualising social media like Twitter is an active topic for me at present (and will be more so via my planned API at annotate.io).

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

Minesh B. Amin (MBASciences) and I (Mor Consulting Ltd) taught Applied Parallel Computing over 3 hours at PyCon 2013. PyCon this year was a heck of a lot of fun, I did the fun run (mentioned below), received one of the free 2500 RaspberryPis that were given away, met an awful lot of interesting people and ran two birds-of-a-feather sessions (parallel computing for our tutorial, another on natural language processing).

I held posting this entry until the video was ready (it came out yesterday). All the code and slides are in the github repo. Currently (but not indefinitely) there’s a VirtualBox image with everything (Redis, Disco etc) pre-installed.

After the conference, partly as a result of the BoF NLP session I created a Twitter graph “Concept Map” based on #pycon tweets, then another for #pydata. They neatly summarise many of the topics of conversation.

Here’s our room of 60+ students, slides and video are below:

The video runs for 2 hours 40:

Here’s a list of our slides:

1. Intro to Parallelism (Minesh)
2. Lessons Learned (Ian)
3. List of Tasks with Mandelbrot set (Ian)
4. Map/Reduce with Disco (Ian)
5. Hyperparameter optimisation with grid and random search (Minesh)

These are each of the slide decks:

 

I also had fun in the 5k fun run (coming around 77th of 150 runners), we raised $7k or so for cancer research and the John Hunter Memorial Fund.

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

Below I’ve visualised tweets for #PyData conference and the cities of London and Brighton – this builds on my ‘concept cloud‘ from a few days ago at the #PyCon conference. Props to Maksim for his Social Media Analysis tutorial for inspiration.

Update – Maksim’s Analying Social Networks tutorial video is online.

For the earlier #PyCon 2013 analysis I visualised #hashtags and @usernames from #pycon tagged tweets during the conference. I’ve built upon this to add some natural language processing for ‘noun phrase extraction’ which I detail below – this helps me to pull out phrases that are descriptive but haven’t been tagged. It also helps us to see which people are connected with which subjects. For the PyCon analysis I collected 22k tweets, after removing retweets I was left with 7,853 for analysis.

#PyData (PyData Santa Clara 2013)

PyData 2013 is a much smaller conference than PyCon (PyCon had 2,500 people and 20% female attendance, PyData had around 400 with 10% female attendance). Being smaller it had far fewer tweets – after removing retweets I had just 225 tweets to analyse. Cripes! This is clearly not big data. The other problem was that people weren’t using many #hashtags, they were referring to topics using natural language. For example:

“Peter Norvig was giving a talk at PyData in Santa Clara, CA on the topic of innovation in education.” (source)

Clearly some natural language processing was required. I took two approaches:

• Extract capitalised sub-phrases (e.g. “Peter Norvig”, “Santa Clara”) of one or more words
• Use NLTK’s bigram collocation analyser (to find lowercased phrases such as “ipython notebook”, “machine learning”)

Starting at the bottom of the plot we see three types of colour:

• white is for #hashtags
• light blue is for @usernames
• dark green is for phrases (extracted using natural language processing)

We see a cluster of references around @fperez_org (Fernando Perez of IPython), one cluster is around @swcarpentry (the scientist-friendly software carpentry movement), the other is around IPython and the IPython Notebook (@minrk of IPython/parallel is linked too). I like the connection to Julia – Fernando discussed during his keynote that Julia now interoperates with Python.

The day before we had Peter Norvig (Director of research at Google) giving a keynote on the use of Python in education at Udacity including a discussion of how machine learning could be used to identify the mistakes that new coders make so we could make friendlier error messages to help students correct their code. See the clustering around this at the top of the graph.

Later the same day Henrik (@brinkar) spoke on Wise.io‘s Random Forest classifier. Their approach was efficient enough to demo live on a RaspberryPi. The connection from Peter to Henrik goes via #venturebeat who covered wise.io’s new software release during the conference.

Connecting IPython and Wise.io is @ogrisel (Olivier Grisel) of scikit-learn. He gave an impressive (and given the variability of conference wifi – slightly ballsy) live demo of scaling a machine learning system via IPython Parallel on EC2.

In the middle we see @teoliphant (Travis Oliphant) joined to Continuum (his company). Off to the right I get to blow my own trumpet – the phrases “awesome python” and “network analysis” connect to “russel brand” which is how one wag described my lightning talk. I got a chance to demo the earlier version of this at the end of @katychuang‘s talk on networkx.

London (geo-tagged tweets)

For the last month I’ve been grabbing tweets in the London geo area for another project. I had to raise my filtering levels to bring the network down to a sane (and easily visualised) number of nodes. After removing ReTweets I have 497,771 tweets from just a subset of my data. Some obvious clusters can be seen:

• #weather and #rain and (presumably a rather wet) “St Albans” (a very British discussion)
• The “O2 Arena” near the centre with “Justin Beiber” and #believetour, linked with #amazing, #excited, #nowplaying
• @onedirection must have been playing (connected with band members @louis_tomlinson and @real_liam_payne amongst others)
• To the top-right we have a football cluster with “Manchester United”, “Champions League”, #cpfc, #realmadrid and “Old Trafford”
• The usual tourist spots like “Tower Bridge”, “Covent Garden”, “Hyde Park”, “Big Ben”, “Trafalgar Square” are  discussed with #happy #sun #loveit, linked just off of here is “London Heathrow Airport” and “New York”

Brighton (geo-tagged tweets)

This is my favourite, analysed using 40,379 tweets after removing ReTweets. The nature of the two cities (Brighton is 50 miles south of London on the coast, it is a university town with a young & party-friendly population) is quite apparent:

• Top left there is discussion around “One Direction”, #justinbeiber and #seo (a particular Brighton tech thing)
• Just south of @justinbieber is a single chain of not-safe-for-work ranting (another particular Brighton thing)
• If you jump to the bottom right you’ll see #underwear, #lingerie, #teenagers – not as dodgy as you might expect, Sweetling were doing a social media bra campaign
• #hove is joined with #sunny #morning and nearby places #lewes #shoreham
• #brightonbeach and “Brighton Pier” connect with #birds (Seagulls – a bane!) and #sun
• #friends, #memories#, #happy, #goodtimes, #marina, #fun, #girls cluster around the centre (Brighton does like a party)
• Off down to the bottom left is a some sort of political discussion (what were they doing in Brighton?)

Reproducing this

All the code is in github at twitter_networkx_concept_map including the one line cURL command to capture the data. An example .gephi file is included for visualisation in Gephi. The built-in networkx viewer (optionally using GraphViz) works reasonably well but isn’t interactive. Maksim’s tutorial and utils class were jolly useful (utils is in my repo), I’m also using twitter-text-python for parsing @usernames, #hashtags and URLs from the tweets.

If you want some custom work around this, give me a shout via Mor Consulting.

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

For my client (AdaptiveLab) I recently gave an internal talk on the state of the art of Natural Language Processing around Social Media (specifically Twitter and Facebook), having spent a few days digesting recent research papers. The area is fascinating (I want to do some work here via my Annotate.io) as the text is so much dirtier than in long form entries such as we might find with Reuters and BBC News.

The Powerpoint below is just the outline, I also gave some brief demos using NLTK (great Python NLP library).

 

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

The field of “data science” covers a lot of areas, it feels like there’s a continuum of layers that can be considered and lumping them all as “data science” is perhaps less helpful than it could be. Maybe by sharing my list you can help me with further insight. In terms of unlocking value in the underlying data I see the least to most valuable being:

• Storing data
• Making it searchable/accessible
• Augmenting it to fashion new data and insights
• Understanding what drives the trends in the data
• Predicting the future

Storing a “large” amount of data has always been feasible (data warehouses of the 90s don’t sound all that different to our current Big Data processing needs). If you’re dealing with daily Terabyte dumps from telecomms, astro arrays or LHCs then storing it might not be economical but it feels that more companies can easily store more data this decade than in previous decades.

Making the data instantly accessible is harder, this used to be the domain of commercial software and now we have the likes of postgres, mongodb and solr which scale rather well (though there will always be room for higher-spec solutions that deal with things like fsync down to the platter level reliably regardless of power supply and modeling less usual data structures like graphs efficiently). Since CPUs are cheap building a cluster of commodity high-spec machines is no longer a heavy task.

Augmenting our data can makes it more valuable. By example – applying sentiment analysis to a public tweet stream and adding private demographic information gives YouGov’s SoMA (disclosure – I’m working on this via AdaptiveLab) an edge in the brand-analysis game. Once you start joining datasets you have to start dealing with the thorny problems – how do we deal with missing data? If the tools only work with some languages (e.g. English), how do we deal with other languages (e.g. the variants of Spanish) to offer a similarly good product? How do we accurately disambiguate a mention of “apple” between a fruit and a company?

Modeling textual data is somewhat mainstream (witness the availability of Sentiment, NER and categorisation tools). Doing the same for photographs (e.g. Instagram photos) is in the quite-hard domain (have you ever seen a food-identifier classifier for photos that actually works?). We rarely see any augmentations for video. For audio we have song identification and speech recognition, I don’t recall coming across dog-bark/aeroplane/giggling classifiers (which you might find in YouTube videos). Graph network analysis tools are at an interesting stage, we’re only just witnessing them scale to large data amounts of data on commodity PCs and tieing this data to social networks or geographic networks still feels like the domain of commercial tools.

Understanding the trends and communicating them – combining different views on the data to understand what’s really occurring is hard, it still seems to involve a fair bit of art and experience. Visualisations seems to take us a long way to intuitively understanding what’s happening. I’ve started to play with a few for tweets, social graphs and email (unpublished as yet). Visualising many dimensions in 2 or 3D plots is rather tricky, doubly so when your data set contains >millions of points.

Predicting the future – in ecommerce this would be the pinacle – understanding the underlying trends well enough to be able to predict future outcomes from hypothesised actions. Here we need mathematical models that are strong enough to stand up to some rigorous testing (financial prediction is obviously an example, another would be inventory planning). This requires serious model building and thought and is solidly the realm of the statistician.

Currently we just talk about “data science” and often we should be specifying more clearing which sub-domain we’re involved with. Personally I sit somewhere in the middle of this stack, with a goal to move towards the statistical end. I’m not sure one how to define the names for these layers, I’d welcome insight.

This is probably too simple a way of thinking about the field – if you have thoughts I’d be most happy to receive them.

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

I’ll start with a quote via “Why workers are losing the war against the machines” taken from A Farewell to Alms by economist Gregory Clark:

“There was a type of employee at the beginning of the Industrial Revolution whose job and livelihood largely vanished in the early twentieth century. This was the horse. The population of working horses actually peaked in England long after the Industrial Revolution, in 1901, when 3.25 million were at work. … There was always a wage at which all these horses could have remained employed. But that wage was so low that it did not pay for their feed. “

Now I’m back in London I’m watching the prevalence of couriers and delivery people bringing a constant stream of packages through the busy streets. I’m betting this will be automated in the near future. Couple self-driving cars and a physical-packet-delivery-platform that looks a bit like the Internet protocol and then you’ve got (I think) a bit of a game changer.

Self-driving cars have the potential to be legal in cities (they’re legal in a few US states at present, accepting longer legal battles to come). They’ll drive safely and predictably, they’re unlikely to react erratically (e.g. no pulling out in busy streets for a foolish maneuver and hitting a cyclist), they don’t need a lunch break and they could pick-up and drop-off from depots a long way from traditional storage facilities (as nobody has to commute to the facility).

Consider one of these vehicles arriving outside your office and phoning you to give you a secret ID number. You come out to the street, key in the number, a panel pops open and there’s your package. Internally the packages are retrieved in a similar way to automated warehouses. Since the system is always calling home to report its status it could notify all upcoming delivery recipients of its expected ETA. You could probably buy an upgrade to reserve your delivery slot (giving delivery companies a new revenue stream?).

If they’re controlled via a derivative of the Internet Protocol then we have a decentralised physical-packet-routing system. If the cars can ‘mate’, perhaps by backing on to each other, they can trade packages so the packages travel further without human intervention. Maybe you end up with an open market for atoms-distribution, assuming compatible protocols exist amongst the courier companies.

I’ve followed John Robb’s recent discussion of DroneNet (more) – it is the same idea (props – I’m tagging on his/others’ thinking) applied to low cost drones. I think drones will follow later as they’re constrained by weight and flight restrictions and so they are far less useful in the city at present.

At the end of the day I think that humans will be pushed out of the physical package delivery game (be it via drones or via delivery cars). Trying to understand the speed at which humans will be removed from traditional working disciplines in specialist area continues to baffle me.

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

Whilst working on data sciencey problems for AdaptiveLab I’m becoming more involved in simple visualisations for proof-of-concepts for clients. This ties in nicely with my PyCon Parallel Computing tutorial with Minesh. I’ve been prototyping a Disco map/reduce tutorial (part 2 for PyCon) using tweets collected during the life of SocialTies during 2011-2012.

Using 11,645,331 tweets on 1 machine running through Disco with a modified word_count example it is easy to filter to keep tweets with a certain word (“loving” in this case) and to plot a word cloud (thanks Andreas!) of the remaining tweets:

Tweet analysis often shows a self-referential nature – here we see “i’m” as one of the most popular words. It is nice to see “:)” making an appearance. Brands mentioned include “Google”, “iPhone”, “iPad”. We also see “thanks”, “love”, “nice” and “watching” along with “London” and “music”. Annoyingly I’m not cleaning the words so we see “it!”, “it.”, “(via” (with erroneous brackets) and the like which clutter the results a bit.

Next I’ve applied “hating” as the filter to the same set:

One of the most mentioned words is “people” which is a bit of a shame, along with “i’m”. Thankfully we see some “love” and “loving” there. “apple” appears more frequently than “twitter” or “google”. Lots of related negative words also appear e.g. “stupid”, “hate”, “shit”, “fuck”, “bitch”.

Interestingly few of the terms shown include Twitter users or hashtags.

Finally I tried the same using “apple” on an earlier smaller set (859,157 tweets):

Unsurprisingly we see “store”, “iphone”, “ipad”  “steve”. Hashtags include “#wwdc”, “#apple” and “#ipad”. The Twitter accounts shown are errors due to string-matching on “apple” except for @techcrunch.

I find it interesting to see competitor brands being mentioned in the same tweets (e.g. “google”, “microsoft”, “android”, “samsung”, “amazon”, “nokia”), although the firms are obviously related to “apple”.

An improvement would be to remove words from the chart that match the original pattern (hence removing words like “apple” and “#apple” but keeping everything else). Removing near-duplicate terms (e.g. “apple”, “apples”, “apple’”) and performing common string clean-ups (removing punctuation) which also help.

It would also be good to change the colour channels – perhaps using red for commonly-negative words and green for commonly-positive words, with the rest in a neutral colour. Maybe we could also colour the neutral words differently if they’re commonly associated with the key word (e.g. brands of the key word).

Getting started with Disco was easy enough. The installation takes a few hours (the Disco project instructions assume a certain familiarity with networked systems), after that editing the examples is straightforward. Visualising using Andreas’ code was very straight-forward. The source will be posted around the time of my PyCon tutorial in March.

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Ian applies Data Science as an AI/Data Scientist for companies in Mor Consulting, founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.