A new report from Hurwitz & Associates seeks to put text analytics vendors in context. In an environment where unstructured text accounts for 80 percent of the data available to companies, the market analyst and research firm has prepared a Victory Index to help companies suss out who can best help them get value from this information.

By providing the ability to analyze unstructured text, extract relevant information, and transform it into structured information, “text analytics has become a key component of a highly competitive company’s analytics arsenal,” write report authors Fern Halper, partner and principal analyst; Marcia Kaufman, COO and principal analyst; and Daniel Kirsh, senior analyst. Often, the research firm notes, companies begin to experiment with text analytics to gain insight into the unstructured text that abounds in social media, and from that move on to other use cases. For instance, they’ll discover value in mining unstructured data and using it with structured data to improve predictive models.

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Text and sentiment analytics for the masses. That could be a tagline for Semantria, which lets users put the technology to work in a pay-as-you-go cloud model. Not only that, but it lets customers deploy a plug-in to run analytics of unstructured content, extracting entities, themes, sentiment, categories, summaries, facets, and relationships, in one of the world’s most common user environments: Microsoft Excel.

More is on the way, too. This December should see the unveiling of a partnership with an as-yet-unnamed vendor to expand the applications with which its platform is compatible. That partner already offers data integrations with 300 applications; when Semantria becomes the 301^st, users will be able to universally and bi-directionally talk to the hundreds of other applications without having to do any integration work on their own.

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Lexalytics has announced a new version of the company’s Salience text analytics engine. According to the company, “This Salience 5.1 release includes several feature enhancements, including improved management of short content, resulting in improved sentiment accuracy for social media updates. The latest version offers both improved sentiment accuracy ‘out of the box’ (for both short and long content), as well as several new tools for configuring sentiment analysis to match business needs. Lexalytics has also changed the way it supports its foreign language offerings overall by adding the Concept Matrix™ to non-English languages. The Concept Matrix™ uses the entire human-generated encyclopedia, Wikipedia® (a registered trademark of the Wikimedia Foundation), and enables Salience to understand the complex relationships between words and meaning. Until now this functionality was only available in English.” Read more

The financial services sector was in focus at this week’s Sentiment Analysis Symposium in New York City, which is organized and produced by Alta Plana Corp. and its founder, Seth Grimes.  Take, for example, the presentation by Rich Brown, head of Elektron Analytics at Thomson Reuters, who disclosed that the company is about to launch market response indicators in support of its Thomson Reuters News Analytics system for the financial community. That product this week also won The Technical Analyst’s 2012 award for best news analytics software.

With its software, originally discussed here, qualitative, unstructured information is turned into a quantitative data set allowing users – machines and humans – to quickly analyze thousands of news stories in less time than it takes to read a single headline, as Thomson Reuters describes it. It uses natural language processing technology to get to the end game, which is to forecast financial market response from news and social media sentiment. Some 82 fields of metadata come into play for automating the analysis of news content. That encompasses sentiment down through to the degree of positive, negative or neutral expressions and how individual companies mentioned in a piece fare in those respects – rather than just the tone of the piece at large. “The computational linguistics system measures the author’s tone as positive or negative on any given entity, which is important and the harder part of it,” Brown said. Other fields include, for example, relevance, genre, intensity of news flow, and more.

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Market Strategies International recently released the first edition of what it says will be an annual Social Media Brand Index, a measure for brands both of consumer-generated social media about them and of their own sponsored content. The Index takes into account four components. Volume, or the amount of buzz about a brand online, is one of them — and its most highly weighted component, too. The others take their cue from what we might call more meaning-related measures, sentiment analytics and semantic markup among them.

For example, there’s net Sentiment, which Market Strategies says represents the ratio of positive to negative sentiments expressed about a brand based on automated natural language processing of the content of posts, comments and mentions. Another component, Positive Emotions, seems to flow from that measure, representing the number of content items that are identified as having the warm fuzzies about them, again based on automated coding of content.

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Bill Ives recently discussed the advancements of Salience 5.0 with Lexalytics CEO Jeff Catlin. Ives writes, “Semantic technology differs from most computing as it learns on the job. This can provide great benefits but it can also be time consuming… [Lexalytics] came up with a clever idea to reduce the learning curve. They had their semantic engine digest Wikipedia to gain an understanding of human thought and build their Concept Matrices™. This allows it to do things that most computer technology would struggle with such as understanding that pizza is a food even though the word food was never associated with pizza in the text it was looking at.” Read more

August is get-away month, so the hospitality industry should start gearing up for what happens when all those travelers get back home….and start to record their impressions of the properties at which they stayed across the social media landscape.

Last week saw the integration of Lexalytics’ sentiment analytics engine into Revinate’s software-as-a-service social media reviews-tracking solution for the hotel industry. In the fairly recent past, Aptech Computer Systems signed on to use Clarabridge’s sentiment and text analytics software for its Execuvue Business Intelligence software for hotel operators. And many other names in the social analytics space, from Attensity to SAS, also see the hospitality sector as a key segment when it comes to mining customer sentiment. At this spring’s Sentiment Analytics symposium, Lexalytics CEO Jeff Catlin called “travel and tourism a natural spot to use it. There’s a lot of data feeding back all the time that helps them make money,” he said. “You can learn things when you’re scoring tones on certain attributes.”

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Lexalytics, whose text and sentiment analytics engine underpins media monitoring solutions from vendors such as Radian6 (the recent acquisition target of Salesforce.com) and Scout Labs, has a new version of its Salience software that digested every bite of knowledge inside Wikipedia and built an understanding of the relationships between words and meaning to deliver its new Concept Matrix and dependent capabilities such as Facets and Collections.

“The common complaint from customers is, ‘There’s my tag cloud and it sucks,’” says Jeff Catlin, CEO of Lexalytics, separating concepts that are contextually similar rather than grouping them together and linking them to broader categories to which they are implicitly related. A 9-iron, golf club and driver are all part of the common concept of golf club, and that concept relates to other concepts like recreation and outdoors.  “Knowing that they are semantically related takes the tag cloud from this unwieldy thing to a directed view of what’s going on.”

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What’s the most important requirement for sentiment analytics to succeed? Make that question plural, and let’s start our answers with something that the tools in this area themselves have no influence on: Good quality data.

During yesterday’s second annual Sentiment Analysis Symposium in New York City, hosted by Alta Plana Corp. and its founder Seth Grimes, the audience got an earful about how bad data can negatively impact efforts to understand sentiment  before they even get underway.

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A new article highlighting interviews with Evan Sandhaus, the lead architect for semantic platforms at the New York Times, and Jeff Catlin, CEO of Lexalytics, takes a look at the effect of the semantic web on news and social media.

First examining news media, the article comments that “while webpages are formatted for humans to easily read them, machines can’t easily determine the underlying meaning of content on a page if it doesn’t follow a consistent structure.” Read more