Discussion of sentiment analysis, which is the extraction of indicators of a writer’s (or speaker’s) opinions and emotional reactions (e.g., about a product feature or brand). Related subjects include:
Time for a notes/links/comments post just for Text Technologies: Read more
|Categories: Blogosphere, Online media, Sentiment analysis, Social software and online media, Text mining||Leave a Comment|
Many people think text information is important to analyze, but even so data warehouses don’t seem to wind up holding very much of it.
|Categories: Attensity, Comprehensive or exhaustive extraction, Sentiment analysis, Text mining||5 Comments|
I’m not at Gartner’s Event Processing conference, but there seem to be some interesting posts and articles coming out of it. Seth Grimes has one on Reuters’ integration of text mining and event processing, including sentiment analysis. Well worth reading. Lots more detail than I’ve ever posted on similar applications.
|Categories: ClearForest/Reuters, Investment research and trading, Sentiment analysis, Text mining||4 Comments|
As reported on the Lexalytics blog, sentiment analysis specialist Lexalytics has merged with the text analytics division of Infonic to form Lexalytics Limited. The deal seems to have a screwy financial structure — which Seth Grimes made a valiant effort to decipher (I think from vacation, poor guy) — as is common when companies much too small to be public wind up trading publicly anyway.
Text mining tools are just WONDERFUL at detecting idiom, sarcasm, and figurative speech … Yeah, right. I asked Lexalytics CEO Jeff Catlin whether his tool could do that kind of thing, and he looked at me like I’d just grown a third ear.
Actually, he didn’t. But just like every other sentiment analysis vendor I encountered at the Text Analytics Summit or spoke to beforehand, he made it clear that his tool could only handle straightforward, literal expressions of opinion. Idiom, irony, sarcasm, metaphor, et al. are beyond the current reach of the technology.
Aren’t you just thrilled that I shared that earth-shattering news with you?
The usual TEMIS execs didn’t make the trip to the Text Analytics Summit this year. But cofounder Alessandro Zanasi did come, and I chatted with him for a bit. Alessandro is also author of a recent book on text mining, and pretty much a one-man Italian operation for France-based TEMIS. Despite his nominal 100:1 manpower disadvantage vs. Italian national-champion text anayltics vendor Expert System S.p.A., Alessandro proudly rattled off four different Italian government accounts he’d won vs. Expert System, all of them apparently in the government area.
Beyond that, Alessandro denies all the rumors that have grown out of TEMIS being hard to reach recently. He reports that pharma is still TEMIS’s big market, but stresses that this covers a range of apps, from research to Voice of the Market. I do get the sense that TEMIS’s sentiment extraction capabilities are less sophisticated than some of the other vendors’ — but the other vendors I’m thinking of are pretty focused on English, SPSS aside. If you need sentiment analysis in non-English languages — e.g., French or Italian — TEMIS should definitely be on your vendor shortlist.
|Categories: Application areas, Competitive intelligence, Expert System S.p.A., Sentiment analysis, TEMIS, Text Analytics Summit, Text mining||2 Comments|
I chatted with Lexalytics CEO Jeff Catlin at the Text Analytics Summit today. Lexalytics is a 14 person company, which represents a doubling over last year. Jeff thinks Lexalytics is on track this year to double again.
Lexalytics’ main business is OEMing sentiment extraction, e.g. to the many blog-analysis/reputation-management (i.e., Voice of the Market) companies that recently started up and in some cases have been bought by big market analysis firms. Lexalytics can and sometimes does extract the more basic stuff as well, but sentiment analysis is the heart of its business. A partial customer list can be found on the Lexalytics site. Lexalytics extracts in the English language only. Read more
|Categories: Competitive intelligence, Lexalytics, Sentiment analysis, Text Analytics Summit, Text mining, Text mining SaaS||1 Comment|
I was at the Text Analytics Summit yesterday. After the sessions and theoretically* before the drinks, there was a group of subject- or industry-specific “roundtables.” The three best-attended roundtables by far — each with at least 20% of the total roundtable attendees — were on “Voice of the Market”, “Competitive Intelligence”, and “Sentiment Analysis”. (Yes, those are in practice pretty close to being the same thing.) Thus, over half of the show attendees who voted with their feet on a particular subject area of interest picked one in the customer/marketing area. Read more
|Categories: Application areas, Competitive intelligence, Sentiment analysis, Text Analytics Summit, Text mining, Voice of the Customer||6 Comments|
According to Attensity CTO David Bean:
- Voice of the Customer/Market applications require less linguistic sophistication than other text mining applications.
- Hence, Voice of the Customer/Market apps are easier to get running than other text mining applications, which he conjectures is a big part of the reason for burgeoning sales.
I’m guessing most text mining vendors would agree with those views, although they might not agree with his elaborations, which include: Read more
|Categories: Application areas, Attensity, Competitive intelligence, Expert System S.p.A., Sentiment analysis, Text mining, Voice of the Customer||1 Comment|
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|