Just a little fun seasonal project I’ve made with the layout and design help of Gavin Wray.
It works very much like my other sentiment analysis tools, but with a sprinkling of Santa’s magic. Santa’s magic in this instance being that any tweets with the words ‘Christmas’ or ‘Xmas’ in them are weighted doubly—that is the scores are counted twice for the purpose of producing the mean score.
So, try the Christmas-o-meter and see how Christmassy you’re feeling.
(click through for big)
Last night I turned my sentiment analysis tool on two hashtags: #bcfc and #avfc, the most widely used tags to refer to Birmingham City and Aston Villa during their League Cup quarter final game. It was a chance to see if visualising to ‘competing’ tags around the same event would be a useful exercise.
Caveats that would apply to this:
There was a generally a downward trend throughout the match, tension? Bad football? It could have been both. The first two goals seemed to have a much bigger impact than the third—this I don’t quite understand, but it seems to be more about the tweets themselves than the tool.
I could see how a special subject-set of emotion words could be created for football, which could cope with more nuanced or unusual words. It’s something to consider.
The sentiment scores in a Google spreadsheet, csv files: #avfc tweets (657 of which were during the game), #bcfc tweets (370 during).
The obligatory Wordle:
As promised, I turned my Twitter sentiment analysis tool on the big TV/social web phenomenon that is the X-Factor. I started the script running at around 6:30pm and off again at 10:30pm — but the really interesting bit is during the show itself (thankfully watching the results stream in meant I didn’t have to watch the show itself).
It ran every minute and looked at the most recent 1,000 tweets tagged #xfactor.
The real reason for using the X-Factor is that I was aware just how violently the emotions can swing on Twitter when watching—and also it is a very defined timeline of events. The Valence (the happy-sad ratio, red line) had greater peaks and troughs in short times than any sentiment graphing project I’ve tried before.
The differences are far more prominent in the graph than any trends over the whole two and a half hours. Arousal (awake-ness, for want of a better word) was relatively constant, as was dominance (the feeling of control), although both jump up and down (within boundaries) along with Valence.
And who was ever-so unpopular around 8:50pm? This chap:
Next, I think I’ll try Question Time.
I’ve tried two experiments with the “is Birmingham happy” algorithm in the last few days, as they’re not based on place it makes more sense to use the popular term ‘sentiment analysis’ to refer to what it’s doing in this instance. As they were both reasonably short uses it was posible to update the reading often (and use a smaller number of tweets as the sample, giving more variation in the average scores) and give the sentiment graphs a live ‘wormal’ feeling, watching the ratings change over time.
First was on the Personal Democracy Forum EU conference in Barcelona, for the length of the two-day conference I monitored the hashtag #pdfeu every five minutes:
(click image for larger view)
The highest rating was 64.4% (at 12:45pm on Tuesday), the lowest 49.6% (Monday at 12:14pm during a short power failure). What was interesting to me was that the “arousal” rating seemed to work well as it stayed pretty steady during the power failure (or even leaped up a little) even as the happiness of the hashtag users dived. Post-lunch conference lulls and periods of excitement (the big spikes in day two, at least, corresponded with much applause) were mapped quite accurately.
The overall average was 57.29%. If you would like to explore or graph the data yourself, you can see in all in a Google Spreadsheet here.
Secondly I tried a much shorter and more mainstream application, David Cameron’s speech to the Conservative Party Conference:
The emotion tracking tool graphed here ran every 10 seconds during David Cameron’s speech to the CPC and analysed the last 100 tweets with the hashtag #cpc10 and the word “tories”. I chose two versions as I wasn’t sure that non-Conservative supporters would use the ‘official’ hashtag, I theorised that they would be likely to use the word ‘tories’. As it turned out I think that while there was a more even spread of pro and anti political types using the hashtag than I expected, but the ‘tories’ Tweeters were definitely more hostile. (See the data.) There was greater movement across the graph than on any other test I’ve run.
Conclusions? None so far, other than that I think this might be a very useful tool, and that more interesting data is created the more Tweets you have and the more you can afford (server-wise) to poll for results. I’m itching to try it on another big live event with conflicting opinions, that might mean training it on a reality TV event. Roll on the X-Factor.
Here are the pertiant bits of the flyer for a short course myself and Chris Unitt have devised and will be delivering as part of Birmingham’s Hello Business event. It’s aimed at those running communications for visitor attractions (museums, theatres, even theme parks) and will be focused on strategies to attract and retain customers.
If that sounds like something that would be of use to you email events@businesslinkwm.co.uk.
I’ve been running a, very rough, scrape of the Birmingham (UK) based interweb for ‘emotional wellbeing’ since April of 2008. Simply put a script running twice a day read in Tweets, news headlines and (originally) blog posts and compared the words within them to a table I’d drawn up of ‘emotion’ words and fairly arbitrary scores.
It was surprisingly interesting to watch: despite its roughness, the internal consistency let patterns emerge. It broadly followed weather and sports results, with some peaks and dips you could map to specific happenings, or news stories.
It lead to a spin off focussing on Tweets from MPs, which I think influenced some of the developments that Tweetminster produced in the next year or so.
It was the patterns that lead me to keep putting off improving the algorithm, but recent Twitter API developments meant I had to do some work anyway and that (together with another project, of which more soon) gave me the impetus to give the project an overhaul. And here’s how it works now…
Twitter’s geolocation services are now much improved, so I can specify a point (the centre of Victoria Square in Birmingham) and a radius (10 miles) and get a reasonably accurate dump of Tweet data back—the algorithm calls for the most recent 1000.
Twitter is now the sole focus of data, in keeping with the ‘conversational pychogeography‘ aims of the project (in essence, words used without too much pre-meditation are more interesting than those written purely for publication). It also provides much more and more reactive data.
The words contained within these tweets are then compared to data from the University of Florida (The Affective Norms for English Words - PDF link). Within that data set each word covered (there are around a thousand in the set I’ve using) is given a score for Valence (sad to happy on a scale 0-10), Arousal (asleep to awake on a scale of 0-10) and Dominance (feeling lack of control to feeling in control on a scale of 0-10). The scores are then collated and a mean calculated. The overall emotional wellbeing score here is calculated as a mean of the three individual means, although the scores are revealed individually on the site.
I’m unsure if combining the results in this way is the best, which is why the site reveals the working — the Twitter feed just goes with one value for ease of understanding and adds a rating adjective too:
if ($brumemotion<100){$rating="fantastic";}if ($brumemotion<90){$rating="superb";}if ($brumemotion<80){$rating="good";}if ($brumemotion<70){$rating="okay";}if ($brumemotion<60){$rating="average";}if ($brumemotion<50){$rating="quiet";}if ($brumemotion<40){$rating="subdued";}if ($brumemotion<30){$rating="low";}if ($brumemotion<20){$rating="dreadful";}if ($brumemotion<10){$rating="awful";}
The Twitter feed produces results twice a day, and these scores are being saved to visualise more graphically, but the website updates every ten seconds (and will self-refresh if you stay on the site) and also displays a word cloud of the currently found ‘emotion words’:
I’ve been experimenting with more local results (here is a version running on just one Birmingham post code — B13) as well as live graphing. I also have a version that will analyse results for a hashtag—something we may use in conjunction with the Civico player to produce ‘wormals’ (graphs of sentiment) during conferences.
But for now, I’m happy to let the new algorithm bed in—wondering about the amount of data and frequency that will be required to see the most detail—and to see what patterns we can spot.
Feedback welcome. Go see for yourself or follow on Twitter.
With the Birmingham Popular Music Archive I’ve been inviting the public to contribute to an online database of music culture in Birmingham, by placing venues, artists, people or anything they feel relates to music on a map. You can see and add your memories to the map, here.
An editioning of the results so far was commissioned in the form of the Birmingham Music Map as part of ‘plug in’ an exhibition at mac curated by Simon Poulter. The exhibition has now finished, and the artwork (printed on toughened glass and around a metre wide by 1.4m high) is looking for a new home. I’m happy for it to be displayed in any public place as long as they will display an artist’s card next to it and look after it — it would be brilliant if it was mounted somewhere appropriate, but if you’re interested please drop me a line at jon@jonbounds.co.uk.
If you’ve not got the room, poster/print copies are available, screenprinted on gorgeous white archival paper at B1 (707 × 1000mm /27.8 × 39.4in) in a signed and numbered limited edition of 100 copies.
Buy Now (£25 plus £5 postage and packing — recorded delivery):
Price for framed copies on application. Please email for details.
If there’s one thing that fills the web more than cat pictures it’s ruminations on the state, past or future of newspapers and magazines. The truth is old models are failing and no-one really knows. Rupert Murdoch is trying paywalls, which is a possibility for publications with existing audiences and strong brands, but what can a start-up publication do?
In my own small way I’m experimenting — this week sees the launch of a—yes—paper-based magazine that Danny Smith and I have been working on for the best part of six months. This is what it looks like:
Things we’ve worked out so far:
As well as being exhausting and a great hobby, there’s been a fair few opportunities to try out different promotional web-tricks that I’m going to use again. Issue two shouldn’t take so long.
I’m very interested in the motivation behind uses of hashtags on Twitter — I have a feeling that they are more created than searched.
I would be very interested to see Twitter Search stats — to see how many people actually look at collections hashtagged content rather than just pump them out because it seems part of etiquette. This hypothesis brewed when I saw how hashtag use breaks down of during real big events (World Cup, election) — as people already know the context, I am thinking that they are used more as a shorthand for context than searched or monitored.
Without much hope of getting that valuable data, I have created a very short questionnaire to get some feeling for use of hashtags. Due to the responses being self-selecting I am assuming that the results will be biased towards experienced Twitter users, but we’ll see. That this may be compounded due to my network containing a lot of social media profesionals is also a worry, so I would appreciate if you would spread it as far as you can.
Blog RSSRSS feed of comments
twitter
flickr
Blog for The Birmingham PostEverything I Do