Text mining
Analysis of text mining companies, technology, and trends. Related subjects include:
QL2 - web text extraction and more
Here are some highlights of the QL2 story, per exec Mike McDermott.
- QL2’s main business is scraping price and other product offering data from the web for high-speed competitive analysis. For example, of their 250ish customers overall, over 90 are airlines. Online retailers are another big chunk of their customer base.
- QL2 also commonly partners with text mining companies in applications such as Voice of the Market or competitive intelligence. E.g., QL2 has been brought into a few deals each by Attensity, Clarabridge, and especially Temis.
- QL2 goes well beyond basic crawling. Notably, the system fills in forms with parameters. And of course it monitors pages for changes.
- QL2’s scripting language is, Mike tells me, very SQL-like. Hence the “QL” in the name.
- QL2 rolls its own filters, rather than using INSO or whoever. (Actually, what are the main file-reading filter choices these days? I’ve lost track.) Indeed, Mike fondly believes QL2 does a better job with PDFs than Adobe does.
- QL2 doesn’t want to be thought of as web-only. Rather, Mike likes my formulation of “text data ETL, web or otherwise.” That said, he freely admits QL2’s strength is in Extract rather than in Transform or Load.
| Categories: Application areas, Competitive intelligence, QL2, Text mining | Leave a Comment |
Clarabridge does SaaS, sees Inxight
I just had a quick chat with text mining vendor Clarabridge’s CEO Sid Banerjee. Naturally, I asked the standard “So who are you seeing in the marketplace the most?” question. Attensity is unsurprisingly #1. What’s new, however, is that Inxight – heretofore not a text mining presence vs. commercially-focused Clarabridge – has begun to show up a bit this quarter, via the Business Objects sales force. Sid was of course dismissive of their current level of technological readiness and integration – but at least BOBJ/Inxight is showing up now.
The most interesting point was text mining SaaS (Software as a Service). When Clarabridge first put out its “We offer SaaS now!” announcement, I yawned. But Sid tells me that about half of Clarabridge’s deals now are actually SaaS. The way the SaaS technology works is pretty simple. The customer gathers together text into a staging database – typically daily or weekly – and it gets sucked into a Clarabridge-managed Clarabridge installation in some high-end SaaS data center. If there’s a desire to join the results of the text analysis with some tabular data from the client’s data warehouse, the needed columns get sent over as well. And then Clarabridge does its thing.
| Categories: BI integration, Clarabridge, Comprehensive or exhaustive extraction, IBM and UIMA, Software as a Service (SaaS), Text mining, Text mining SaaS | 1 Comment |
What TEMIS is seeing in the marketplace
CEO Eric Bregand of Temis recently checked in by email with an update on text mining market activity. Highlights of Eric’s views include:
- Yep, Voice Of The Customer is hot, in “many markets”; Eric specifically mentioned banking, car, energy, food, and retail. He further sees IBM backing VotC as text’s “killer app.” (Note: Temis has a history of partnering with IBM, most notably via its unusually strong commitment to UIMA.)
- Specifically, THE hot topics in the European market these days are competitive intelligence and sentiment analysis. (Note: I’ve always thought Temis got serious about competitive analysis a little earlier than most other text mining vendors did.)
- Life sciences is an ever growing focus for Temis.
- I confused him a bit with how I phrased my question about custom publishing and Temis’ Mark Logic partnership. But he did express favorable views of the market, specifically in the area of integrating text mining and native XML database management, and even volunteered that nStein appears to be doing well.
| Categories: Application areas, Competitive intelligence, Custom publishing, IBM and UIMA, Investment research and trading, Mark Logic, TEMIS, Text mining, Voice of the Customer, nStein | 1 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.)
| Categories: BI integration, Clarabridge, Comprehensive or exhaustive extraction, Ontologies, Text mining | 1 Comment |
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:
| 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 | Leave a Comment |
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, SPSS, Sentiment analysis, Text mining | Leave a Comment |
