In the last few months, sentiment has become the next big thing in enterprise content processing. Manning & Napier, an investment firm, funded a number of projects for its search and content processing system that could determine what the computer scientists call "polarity" and I call the positive and negative aspect of a document.
A human can read a document and make a comment like, "This customer is really annoyed at our warranty program," or "We need to get this letter over to marketing because our customer is raving about our new product." Computers, as it turns out, can do a reasonably good job of determining the sentiment of a document or processing a large number of documents and providing a report that says, "Sixty-three percent of the messages about our service are positive."
Social media's impact
Sentiment analysis is one of the facets of text analytics that can discern the softer or intentional components of a report, an e-mail or other communication. What has boosted interest in tapping into the sentiment of a document? My view is: social media and a potent tool for competitive advantage. The rise of Facebook and the proliferation of information from Twitter users have created awareness of social media across consumer and business markets. The competitive advantage angle has become increasingly important, as examples of sentiment-centric content processing have diffused at conferences and in professional journals.
My research into this facet of content processing has identified the Southwest Airlines incident as one pivot point. You might remember that Kevin Smith, a Hollywood notable, was asked to get off a Southwest flight because he was too large for the seat. Smith used social media to call attention to Southwest's action. In a matter of minutes of his exiting the aircraft, Southwest found itself behind the curve. Over the span of a day, the incident went viral. Not surprisingly, the airline apologized and set up a social media program. The story flitted across the national media, and the message was not lost on other organizations' managers.
Vendors of sentiment analysis technology and related monitoring and reporting services found that after that fateful day in February 2010, their products and services were of considerable interest to many companies.
One company has become a touchstone for me in measuring the interest in sentiment analysis. That firm is Attensity, originally positioned as a specialist firm with "deep extraction" technology. The founder of the company focused on processing text iteratively. With each cycle of his novel approach, the Attensity system would generate additional data about the text. In today's lingo, Attensity was generating metatags, connections and entities. The system would then perform a range of analytic functions.
Attensity's approach to the analysis of unstructured content was of interest to the U.S. government and certain agencies that performed intelligence and analytic functions for law enforcement and other federal activities. Attensity received an injection of investment money from In-Q-Tel in 2002.
The company, like other firms with technology of interest to In-Q-Tel, continued to win contracts in the government markets. However, Attensity wanted to grow more rapidly, so the company decided to expand its products and services for the commercial sector Attensity merged with Empolis and Living-e to form Attensity Group and Attensity Europe GmbH. (Attensity also has a unit that focuses on the government market.)
Attensity is a privately held company, but the firm has been growing rapidly. Unlike some content processing companies, its jump from a government-centric vendor to the commercial sector has been successful. As early as 2009, the company was trumpeting the payoff from the emerging interest in social media. At a time when many companies were mired in the financial doldrums, Attensity was revving its engines.
But sentiment may not be enough. According to Riza C. Berkan, founder and CEO of Hakia: "I think the next phase of the search will have credibility rankings. For example, for medical searches, first you will see government results (FDA, National Institutes of Health, National Science Foundation), then commercial (WebMD), then some doctor in Southern California, and then user-contributed content. You give users such results with every search.
In early 2010, Hakia rolled out SENSEnews, a service that uses some of the methods of "sentiment" and applies them in a way that has struck a nerve with the financial services market. In the Semantic Technology Blog (http://priyankmohan.blogspot.com/2011/01/hakias-sensenews-can-they-they-really.html), Priyank Mohan described Hakia's semantic application as "a service to make buy-and-sell recommendations for any stock. SENSEnews reads news sources (more than 30,000 news sources), blogs (more than 1 million) and Twitter, and performs an advanced computation to make buy/sell recommendations to you."
Hakia's engineers know that news about a company is not mathematical in nature. Hakia has looked at precursors like Monitor 110 (no longer in business), Relegence (acquired by AOL), Need to Know (acquired by Deutsche Borse, deutsche-boerse.com, in November 2009) and Reuters' NewsScope (now part of Thomson Reuters' Eikon, thomsonreuters.com/products_services/financial/eikon).
Hakia uses semantics in a way that makes sense to enterprise professionals and individual investors. In my opinion, SENSEnews is an application that makes "sense" to the user. Who does not want an edge when making an investment in a publicly traded stock?
Hakia's innovation moves beyond sentiment analysis, which is useful in customer support and advertising. Hakia's approach, according to the Semantic Technology Blog, pushes ahead by "building a recommendation model on top of semantic filtering of the content ... Its delivery model is subscription/consumer-based, which is different from most of the ones that have tried this before or are trying now, as they were/are more enterprise-focused with the exception of StockTwits."
One should weigh some considerations before getting rid of a personal financial adviser. For example, the service requires a subscription. Details about the inner workings of the SENSEnews system are sparse. The latency between the availability of information and the updating of the SENSEnews index is not evident.
As I look over the uptake of semantic technology in the enterprise and in consumer markets, three changes are taking place. First, there is a growing awareness that key word search is not appropriate for certain types of information retrieval tasks. A few years ago, the notion that a search box would unlock the treasure chest of high-value information was exhausted. Users grew tired, then annoyed and understandably more vocal in their criticism of search systems that generated a need for endless opening, scanning and processing of "hits." When most of the items in the search results list were irrelevant to the user's information need, a semantic window opened. Attensity and Hakia are two firms trying to squeeze through that aperture.
Second, the semantic technology is not exposed. The systems and methods are manifested in an application that solves a problem or does something the user can look at, evaluate and decide if the output has value. Equations, canned demonstrations and lectures about latent semantic indexing lose sales in my experience. What is encouraging about Attensity's push into Madison Avenue and Hakia's foray into stock picking is that the outputs are useful and concrete. Today, unless "there's an app for that," semantic technology will be just another content processing complexity. Made fungible, semantic applications can be magnets that pull customers.
Third, the semantic application market is just now taking shape. Are Attensity and Hakia going to remain static? No. Both firms will continue to evolve. In addition, the attention both attract will create the now familiar cycle of what I call the sheep approach. Innovators with semantic technology will follow Attensity, Hakia and others into the market for semantic applications. This is exciting on one hand and a repeat of the all-too-familiar pattern of competitor proliferation. For those who are stakeholders in Attensity and Hakia or other companies working in this space, a buyout may be in the future.
I am not ready to say "a semantic revolution" has occurred. But it looks to me as if there are signals that traditional enterprise search, content processing and knowledge management solutions will face a choice: Go semantic or go away.