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 José Carlos González, Daedalus
1) How has the market for text technologies, and text-analytics-reliant solutions, changed in the past year? Any surprises?
Over the past year, there has been a lot of buzz around text analytics. We have seen a sharp increase of interest around the topic, along with some caution and distrust by people from markets where a combination of sampling and manual processing has been the rule until now.
We have perceived two (not surprising) phenomena:
– The blooming of companies addressing specific vertical markets incorporating basic text processing capabilities. Most of the time, text analytics functionality is achieved through integration of general-purpose open source, simple pattern matching or pure statistical solutions. Such solutions can be built rapidly from large resources (corpora) available for free, which has lowered entry barriers for newcomers at the cost of poor adaptation to the task and low accuracy.
– Providers have strengthened the effort carried out to create or educate the markets. For instance, non-negligible investments have been made to make the technology easily integrable and demonstrable. However, the accuracy of text analytics tools depends to some extent on the typology of text (language, genre, source) and on the purpose and interpretation of the client. General-purpose and do-it-yourself approaches may lead to deceive user expectations due to wrong parametrization or goals outside the scope of particular tools.
2) Do you have a 2013 user story, from a customer, that really illustrates what text analytics is all about?
One of our most challenging projects in 2013 was about real-time analysis of social media content for a second screen application, where text analytics has had multiple roles: First, providing capabilities to focus on aspects of the social conversation about TV programs (actors, characters, anchor, sponsors, brands, etc.), analyzing at the same time the sentiment expressed on them.
Second, recommending and delivering content extracted automatically from external sources. These sources can be general (Wikipedia), specialized (TV magazines and websites), personal or organizational (web, Facebook or LinkedIn pages of people or organizations), and popular content shared by users in social media.
Third, providing clues for contextual and intent-based advertising. Fourth, profiling user interest and (inferred) demographic features for targeted and personalized advertising (beyond contextual). The project, which we fully carried out for a telecom company owning a DTTV license with multiple TV channels, involved real-time text analytics in a big data environment, plus all the visualization and user interface design.
This user case is shown to demonstrate the versatility of text analytics to fulfill multiple roles in a single project.
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?
Our goal for 2014 is to cover progressively the specific needs of our clients by helping them to develop solutions in vertical markets, freeing them from the complexity of language processing technology. This involves developing our API offering in Textalytics (http://textalytics.com), our Meaning as a Service product.
The first new API to be delivered in 2014 is specialized in semantic publishing. A second one planned in our road map will be for Voice of Customer Analysis.
As personalization is also a need perceived across markets, we are also integrating a user management API, allowing our clients to edit and manage by themselves specialized dictionaries, classification taxonomies and models.
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?
As explained above, one of our main working topics in 2013 was about real-time analysis of social media streams for different purposes, integrated in smartphone and tablet apps. In particular, we have developed a second screen app for the TV industry. We perceive that mobile apps will continue acting as a major force, designing new scenarios and driving further opportunities for text analytics technologies.
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?
Having experience in many different industries (media, telecom, defense, banking, market intelligence, online marketing, etc.) and in many different languages, our greater challenge is internationalization. The Textalytics brand implements our strategy for a global, multi-industry offering. Textalytics represents a new semantic/NLP API concept in the sense that it goes well beyond the basic horizontal functionality that is being offered in the market: we also offer pre-packaged, optimized functionality for several industries and applications and the possibility for the customer to tailor the system with their dictionaries and models. The customer benefits are a much higher productivity with low risks and costs.
6) Do you have anything to add, regarding the 2014 outlook for text analytics and your company?
In 2013 we have developed a good amount of paid pilots and proof-of-concept prototypes for clients from different areas. Clients start to understand the real potential, limitations and place of text analytics technologies. In 2014, clients are able to see the value already deployed in solutions addressing scenarios very similar to their own, which will foster a more rapid adoption of text analytics solutions.
Thank you to José! Click on the links that follow to read other Text Analytics 2014 Q&A responses: Lexalytics CEO Jeff Catlin, Clarabridge CEO Sid Banerjee, Fiona McNeill of SAS, and Tom Anderson of Anderson Analytics and OdinText. And click here for this year’s industry summary, Text Analytics 2014.
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