I post a yearly look at the Text Analytics industry — technologies and market developments — from the provider perspective. This year’s is Text Analytics 2014.
To gather background material for the article, and for my forth-coming report Text Analytics 2014: User Perspectives on Solutions and Providers (which should be out by late May), I interviewed a number of industry figures: Lexalytics CEO Jeff Catlin, Clarabridge CEO Sid Banerjee, Fiona McNeill of SAS, Daedalus co-founder José Carlos González, and Tom Anderson of Anderson Analytics and OdinText. (The links behind the names will take you to the individual Q&A articles.) This article is —
Text Analytics 2014: Q&A with Jeff Catlin, Lexalytics
1) How has the market for text technologies, and text-analytics-reliant solutions, changed in the past year? Any surprises?
I just did a blog post on the state of the industry. The basic position is that the market/industry looks nothing like it did at the beginning of 2013. Most of the traditional players are gone, or are focusing their businesses vertically. The entire post can be seen here… http://www.lexalytics.com/lexablog
2) How have perceptions and requirements surrounding sentiment analysis evolved? Where are sentiment capabilities heading, in your view?
This is a very interesting and important question… I believe it should be heading to a simpler place where broad applicability would ease its adoption. Lexalytics is pushing hard on this idea with the concept of “Juiciness”. If you ask people to explain sentiment they really struggle, but if you said tell me what a Juicy story is and you get surprisingly similar answers. We believe this is where sentiment should go because it’s business value is mostly on the edges (very positive and very negative), and that’s what Juiciness is. Others are going totally the other direction and pouring emotional states into the mix, which is both difficult to do, and even harder to get people to agree on. This is clearly one of the areas where many people think it’s heading, but we don’t see how broad adoption can happen with such a complex idea.
3) What new features or capabilities are top of your customers’ and prospects’ wish lists for 2014? And what new abilities or solutions can we expect to see from your company in the coming year?
The push is on two fronts: one is simply to keep improving the basic features and functions, like better sentiment scoring and better ad-hoc categorization, while the other is around a couple of new features, document morphology and user intention analysis. Morphology simply means understanding the type and structure of a document. This is very important if you’re processing big pdf or word docs where knowing that a table is in fact a table, or knowing that something is a section heading is important. Intention analysis on the other hand is the ability to profile the author of a piece of text to predict that they might be planning a purchase (intent to buy), or to sell, or in another domain there might be an intention to harm.
As a company, Lexalytics is tackling both the basic improvements and the new features with a major new release, Sallience 6.0 which will be landing sometime in the second half of the year. The core text processing and grammatic parsing of the content will improve significantly, which will in turn enhance all of our core features of the engine. Additionally, this improved grammatic understanding will allow us to be the key to detecting intention, which is the big new feature in Salience 6.0
4) Mobile’s growth is only accelerating, complicating the data picture, accompanied by a desire for faster, more accurate, and more useful, situational insights delivery. How are you keeping up?
You can look at mobile from two different angles. One, it’s simply a delivery mechanism for results, and two, it’s a generator of more user insights (how are people interacting with their applications?). The first aspect of this is the most interesting to us. As a delivery mechanism, it poses unique challenges for packing as much as possible into small, responsive real estate. This is where techniques like summarization and extraction of meaning makes for some really interesting applications.
You use an interesting phrase: “situational insights delivery” – which I would take to mean “what’s relevant to me right now.” Let’s take the simplest, most common application that is still highly relevant to “situations” – email. Wouldn’t it be nice to be able to scan and see, across all these emails that you don’t really want to have to parse on your iPhone, just what exactly you need to do? In Spring, 2013, we used some of our rich contextual grammar language to ship “imperative sentence” (aka “action item”) extraction. If it’s “blah, blah, blah, please send me the powerpoint from yesterday” – we’ll strip out the blah and give you the “please send me the powerpoint from yesterday.” This technology is in use with companies providing customer support services and applications, to help their reps get to the meat of what they need to do – right now.
This same thinking applies to any of a number of other sets of applications. One way of looking at the whole science of Text Analytics is extracting the essence of meaning and compacting it into a form that is the smallest possible representation of this information. “Who are the people that are involved?” ” Is this a product that I care about?” “Whoa, that’s really bad, I should get right on that.” And, with small screens, data that has been quickly compressed into units with high informational value is more useful than the original text stream.
5) Where does the greatest opportunity reside, for you as a solution provider? Internationalization? Algorithms, visualization, or other technical advances? In data integration and synthesis and expansion to new data sources? In providing the means for your customers to monetize data, or in monetizing data yourselves? In untapped business domains or in greater uptake in the domains you already serve?
As described in question 1, the industry is changing dramatically, and all vendors will have to change with it. We are progressing on 2 fronts. We are continuing to push to improve the core technology (Algorithms, Internationalization and big data) while looking for new vertical market opportunities. Our specific focus has been on mobile content applications and the backend framework to enable the rapid development and deployment of mobile content applications.
6) Do you have anything to add, regarding the 2014 outlook for text analytics and your company?
The walls are tumbling down. 2013 and 2014 are the enterprise data equivalent of the fall of the Berlin Wall, where data that was jealously guarded by individual groups is now available enterprise-wide. What this means, paradoxically, is that there is a lot more demand for small-group analysis. The data is corporate, but the results need to be local to help a sales team, or figure out where to go with the marketing of a single product. This is a really important driver for highly accessible text analytics that doesn’t look like text analytics – where it’s just a natural part of how you go about your day. We’re pushing partnerships and technology in 2014 that can help drive this once daunting technology to where it’s functionally invisible, just like search.
Thank you to Jeff! Click on the links that follow to read other Text Analytics 2014 Q&A responses: Clarabridge CEO Sid Banerjee, Fiona McNeill of SAS, Daedalus co-founder José Carlos González, and Tom Anderson of Anderson Analytics and OdinText. And click here for this year’s industry summary, Text Analytics 2014.
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