Business Objects-Inxight update
I’m at the Business Objects annual user conference, and had a couple of chances to talk with Inxight/text analytics folks. When I asked about areas of commercial application traction, answers were similar to those I got from Attensity and Clarabridge, but not quite the same. Specifically:
- Voice of the Customer is definitely tops.
- Some of the other applications Attensity and Clarabridge mentioned appear as well (e.g., antifraud).
- Business Objects also has a couple of customers looking at text mining as an aid to medical records, e.g. by helping to catch errors in tabular-field coding.
- There are some projects in actual investment research/analysis/trading, e.g. in correlating news announcements and stock price movements.
The Business Objects/Inxight folks also made a couple of interesting general technical points. Read more
| Categories: Application areas, BI integration, Business Objects and Inxight, Investment research and trading, Voice of the Customer | Leave a Comment |
SAP is acquiring Inxight
More precisely, SAP is acquiring Business Objects, and of course Business Objects already acquired Inxight.
This could be interesting …
| Categories: BI integration, Business Objects and Inxight, SAP, Text mining | Leave a Comment |
The Clarabridge approach to text mining
And for my sixth text mining post this weekend, here are some highlights of the Clarabridge technology story. (Sorry if it sounds clipped, but I’m a bit burned out …)
- Like Attensity, Clarabridge practices exhaustive extraction.* That is, they do linguistics against documents, extract all sorts of entities and relationships among the entities from each document, and dump the results into a relational database.
- Unlike Attensity, which uses a simple normalized relational schema, Clarabridge dumps the extracted data into a star schema. (The Clarabridge folks are from Microstrategy, which – surely not coincidentally – also favors star schemas.) Read more
| Categories: BI integration, Clarabridge, Comprehensive or exhaustive extraction, Ontologies, Text mining | 2 Comments |
Text mining applications as per Attensity and Clarabridge
Besides asking them technical questions, I surveyed Attensity and Clarabridge last week about text mining application trends, getting generously detailed answers from Michelle De Haaff of Attensity and Justin Langseth of Clarabridge. Perhaps the most important point to emerge was that it’s not just about particular apps. Enterprises are doing text mining POCs (Proofs of Concept) around specific apps, commonly in the CRM area, but immediately structuring the buying process in anticipation of a rollout across multiple departments in the enterprise.
Other highlights of what they said included: Read more
| Categories: Application areas, Attensity, Clarabridge, ClearForest/Reuters, Competitive intelligence, Factiva/Dow Jones, Investment research and trading, Text mining, Voice of the Customer | 3 Comments |
Nice new phrase — Voice of the Market
Michelle DeHaaff, Attensity’s VP of Marketing, just introduced me to a nice phrase — Voice of the Market, obviously related to Voice of the Customer. As Michelle put it:
We’ve also expanded into what we call Voice of the Market data – providing a combination of analysis on external and internal data
– this is how we’ve heard our customers put it:
*Customer feedback comes in many forms……when customers don’t know you are listening (blogs, public web forums) it is important to hear what they say.
*When customers purposely tell you something (via emails, in surveys, captured in customer service notes) it is not only important, but expected….
The first of those would be Voice of the Market, while the second would be Voice of the Customer.
| Categories: Application areas, Attensity, Competitive intelligence, Text mining, Voice of the Customer | 2 Comments |
When to use exhaustive extraction
I’ve been emailing and/or talking with both Clarabridge and Attensity this week. Since they’re the two big proponents of exhaustive extraction, I naturally asked whether there are any cases exhaustive extraction should not be used. In Clarabridge’s case, it turns out exhaustive extraction is the default, and no customer has ever turned this default off. However, their current high end is several million documents* per year. They suspect that in some current projects with much higher volumes the default may finally be turned off. Read more
David Bean of Attensity explains sentiment and other qualifiers
David Bean of Attensity is rightly one of the most popular explainers of text mining, for his clarity and personality alike. I shot a question to him about how Attensity’s exhaustive extraction strategy handled sentiment and so on. He responded with an email that contains the best overall explanation of sentiment analysis in text mining I’ve seen anywhere. Naturally, this is rolled into an Attensity-specific worldview and sales pitch — but so what? Read more
| Categories: Attensity, Comprehensive or exhaustive extraction, Sentiment analysis, Text mining, Voice of the Customer | 1 Comment |
Video Search Summit announces time travel
The First Conferences Ltd. folks who bring you the disappointing Text Analytics Summit are now also launching a “Video Search Summit”. It’s the “first annual” such, and is “inaugural.” On the other hand, their site has a page saying: Check out who has attended in the past – it’s an A – Z list of anyone who is anyone in Video Search! And it gives a list of same.
That’s pretty typical for First Conferences marketing. (And I hope they’ll edit that page after they read this …)
If the Video Search Summit is anything like the four Text Analytics Summits First Conferences has organized to date, it will be a great venue for technology vendor executives to chat with each other, untroubled by interruptions from customers* or prospects.
*Except for any they bring along themselves to participate in their talks.
| Categories: Text Analytics Summit | Leave a Comment |
A tip for submitting to DMOZ — make your site description clear
I just picked out a few of the many unreviewed sites in my DMOZ categories to evaluate, and listed most of those I reviewed.
How did I choose them to get screened? Mainly, I picked out ones with focused descriptions, titles, and so on, that just seemed likely to be listable based on that info (which is the essence of what I see on the page where all the various submitted sites are linked). I correctly guessed that I’d be able to quickly understand what I was seeing and judge whether to list the site or not, quickly write the official site description, and so on. Read more
| Categories: Categorization and filtering, Directories, ODP and DMOZ, Search engine optimization (SEO) | 2 Comments |
Predictive analytics vendors’ text mining sophistication
Steve Gallant of KXEN contacted me over the summer to show me KXEN’s new text mining capability. It was pretty basic bag-of-words stuff, which is still a lot better than nothing, and actually fits pretty well with KXEN’s general simplicity-centric strategy.
This inspired me to check whether there had been any big changes in text mining capabilities at SAS or SPSS. It turned out there hadn’t. SAS is also still on the bag-of-words level. SPSS, however, does do sentiment analysis (pretty obvious, considering their focus on surveys and the like) and negation.
Thanks go out to Mary Crissey and Olivier Jouve for getting back to me when I asked, along with apologies for taking a while to post what they told me.
| Categories: SAS, Sentiment analysis, SPSS, Text mining | Leave a Comment |