Meta Analysis

New piece in a new publication: My trans-analytic Voyage: Text Analytics on Both Sides of the Atlantic contrasts my observations at analytics conferences in the US and Europe.

The Chicago Web, Game and Social Media Analytics Meetup has been around several years and has developed a substantial membership. Now, the group has a new home. Thoughtworks, a global IT consultancy based in Chicago, will host meetings at their headquarters at 200 East Randolph. Last week, I presented “Crossing the Language Chasm: Extracting Information from Foreign-Language Text” for the group at the new location, and it was a pleasure. The space is roomy, comfortable and a great match for this use. The meeting was well attended, and I expect that the new space will help to build attendance.

If you didn’t get to attend the presentation, you can read the original article on Smart Data Collective:

Crossing the Language Chasm: Extracting Information from Foreign-Language Text

Text Analytics Summit Europe took place April 23-24, and I had the opportunity to speak there. My presentation, “Cross-lingual Text Analytics: A New Frontier in Linguistic Technology”, was based on my article of the same title that appeared in Multilingual magazine earlier this year. In that talk, I explained the meaning of “cross-lingual” text analytics, the process and why translating text to feed into English-language text analytics tools is undesirable.

The London group was much more motivated to talk about languages other than English than any audience I’ve encountered in the US! There were several other speakers discussing issues related to non-English text analytics, including some case studies. And the discussion during breaks and such was very different from the US. Americans need to smell the coffee and realize that if we don’t rise up and get into customer engagement and text analytics for languages other than English, we’ll be losing business to international competitors who will get there first. Believe me, they have a huge head start!

Back from a long road trip and recovered from jetlag, I must now get back to writing! Just finished a piece for Language Technology News http://langtechnews.hivefire.com/, will post a link when that’s available. In the past few weeks I have given three presentations on text analytics – in San Francisco, London and Chicago – and I’ve heard many other interesting speakers, so I have some new stories to tell over the next couple of weeks.

Next up – I’ll be giving the keynote presentation at Text Analytics Summit Boston in June! http://www.textanalyticsnews.com/text-mining-conference/ You can read the conference agenda here: http://www.textanalyticsnews.com/text-mining-conference/conference-agenda.php. Hope to see you there!

Social Media Analytics Summit, April 17-18, San Francisco
Capitalize on Multi-lingual Social Media Analytics

European Text Analytics Summit, April 23-24, London
Cross-lingual Text Analytics: A New Frontier in Linguistic Technology

Chicago Web, Game and Social Media Analytics Group, May 2, Chicago Free!
Crossing the Language Chasm: Extracting Information from Foreign-Language Text

Predictive Analytics World, June 25-26, Chicago
Cross-Language Text Analytics: Overcoming Language Barriers

If you’re involved, or interested in, text analytics, you should read Text/Content Analytics 2011: User Perspectives on Solutions and Providers, a market study written by Seth Grimes of Alta Plana. Information like this is hard to come by, and if you can find it at all, it’s often expensive, but this reports is free.

My big takeaway from the report – most users of text analytics today are not yielding positive ROI. That’s shameful! The lesson for me as one who works in the industry is that I have a responsibility to help promote profitable use of text analytics – that includes encouraging users to plan for profit, measure, test and learn to use text analytics tools well.

New article on Smart Data Collective now, Planning for ROI in Text Analytics.

I’ve been working on a glossary for text analytics terms, still have some work to do on that. Let me know if there are particular terms you’d like to see included.

In the meanwhile, here’s one from the folks at Clarabridge.

ESRI is the 800 pound gorilla in the world of maps. Back when I worked for an equally weighty analytics software vendor, I was deeply jealous that a competitor had a partnership with ESRI, while my company partnered with – how shall I put this – a lesser mapping vendor. Our maps stunk, while ESRI’s maps were functional and cool.

Now, through my text analytics work with LinguaSys, I have the good fortune to partner with ESRI to bring text analytics and mapping together. ESRI’s Mansour Raad has been creating functional and cool geographic visualizations of sentiment, and he’s bringing them to the world. Next week he’ll be speaking at ESRI Developer Summit, inspiring fellow developers with a new example that we created – a visual study of sentiment toward the US Transportation Security Administration (TSA).

So often sentiment analysis ends up as a simple pie or bar chart – what percent like my brand, what percent hate it? That type of analysis doesn’t give the client any basis for action. Instead, imagine putting open-ended comments – from surveys, social media, service requests, and other sources – on a map. And enhancing the data with analytics – not limited to simple positive/negative, but with subtleties such as whether the writer is using indicators of disapproval or emotion, obscenities or even references to Nazism. Imagine using color, interactive behavior and other indicators to visualize and identify meaningful patterns in the data. It’s pretty, but it’s more than pretty. Done right, this kind of visualization enables decision makers to derive actionable information.

Read more about Mansour Raad’s work in his post, Enter the Fifth Dimension; Sentiment Map Rendering

In a recent post called Why Nobody Is Actually Analyzing Unstructured Data, Bill Franks explained an important point. Statisticians, data miners, analysts of any quantitative stripe, don’t analyze unstructured data. Oh we may do text analytics, video or audio analytics, but when we do, there is always a step that converts the unstructured text, video or audio to some sort of structured data first. Once that’s done, we can use our ordinary analytics toolkit on the newborn structured data.

It’s worth reading the article. Bill makes his point with a terrific example of how fingerprint data gets the structure needed for comparisons.

My working definition of text analytics is now “the process of converting text into some simpler form of data, such as a category or score.” It’s true that there are usually many more steps needed to produce an actionable analysis, but once the text is simplified, the rest isn’t so much text analytics as it is just analytics.