LexaBlog: Our Sentiment about Text Analytics and Social Media
Using text analytics in your organization
I’m often asked how someone would use our text analytics and sentiment software within their organization. Most inquiries come because a person knows that analytics on unstructured text is important. But they aren’t quite sure how to work it into their business in order to get meaningful results. Here are a few scenarios to show you how our software is used by our customers:
Text Analytics - Enterprise Search
Lexalytics Customer Case: Endeca
Situation: You have streams of information coming into your company and it all looks the same. Sometimes you know what to search on, but other times you need to find the hidden information in all that unstructured content. How do you improve your enterprise search capabilities to get even better results
Solution: Integrating text analytics into the enterprise search application will allow you to find information you didn’t even know existed because it can extract entities like people, places, companies, products and relationships - and that allows you to access information without necessarily knowing what question to ask.
Reputation Management - Market Intelligence
Lexalytics Customer Case: Cymfony
Situation: You know people are talking about your company, your products and brands, yet you don’t have the time or resources to read a million blogs a day. How do you discover the sentiment contained within all the information out in the blogosphere?
Solution: Lexalytics has spent years refining and improving the sentiment analysis software used by many of the vendors offering reputation management, today. By analyzing sentiment at the entity level, and not just the document level, we are providing more accurate results from your data.
Classification - Taxonomy
Lexalytics Customer Case: SmartBrief
Situation: You know you have streams of content flowing into your company, but you can’t find an easy way to manage all the various buckets of information. How do you slice and dice the content without taking months to set up a new taxonomy?
Solution: The key to our classification solution is the ability to easily edit the taxonomy to your business needs. We don’t believe one method of classification is the end all, be all, of a solution so we allow users to select from a variety of methods
As John Harvey at KMWorld recently pointed out, there may be several reasons why text analytics is important to your company, but I wanted to share the three that we see the most in our business.
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Comments
Mike, this is a good idea.
We do recognize that the questions are tailored to a particular vendor’s feature set, even if responses are fair. For instance, your questions 1 & 2 are not relevant for OpenCalais, which is delivered as a Web service.
I am, however, skeptical of self ratings. For instance, Lexalytics doesn’t run on Unix or MacOS, right? So I’d give you two stars. A rating point is foreign-language support, but Lex has none so you get no stars, and Lex is less open than open-source GATE or RapidMiner or open-code LingPipe so I’d reduce you to two stars. But this is nibbling at the edges on my part. It’s a nice effort.
I do have three significant questions.
1) Your point 4 is “Flexibility of entity extraction” but what you show is annotation, not extraction. What are Lex’s extraction capabilities, either into XML possibly including RDF triples or into a database or some other format?
2) Do we not need to look at accuracy, performance, and throughput?
3) Ecosystem: Some vendors have alliances with vendors whose applications consume text-analytics output, for instance, BI or e-discovery or search tools. Some vendors offer visualization or data mining capabilities. All this matters for some users.
These are just off-hand thoughts. Good start.
Thanks for the feedback Seth, I actually agree with most of what you say especially around the star ratings, with hindsight no stars for language support is perfectly correct. I’m not sure that I would agree with you on the Open Calais issue though, surely it is of utmost importance to know if you can install the software on your own stack or have to use a webservice with all the latency issues that involves.
Answering your specific questions
1) The screenshot is of a tool that we are releasing alongside our 4.1 release that will enable you to annotate your own data with what you consider entities and then make a trained model that will exist alongside our existing ones, giving you complete flexibility in what you recognize as an entity, and thus what you can extract. To your other point, this write up focuses solely on Salience, which of course is an SDK so entities are returned as structures / objects in whatever lanaguage you are using. It is a simple task to turn those into XML, insert them into a DB etc.
2) Yes we do, though as discussed in other places accuracy is very hard to judge, hopefully one day someone will produce a base line test set that we can all run against.
With regards to performance I wrote something about that a few weeks ago http://blog.lexalytics.com/2009/02/17/every-little-bit-helps/
3) Yep it does, our own partnership with the Enterprise Search vendors or various VoC companies for example help bring text analytics into a more mainstream market. Not sure how you would rate that on a meme however, number of partnerships?