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Among the topics covered at this week’s Sentiment Analysis’ Symposium was an exploration of just how much the negative or positive expression of sentiment about a company or a product really matters – and in what context it does. (Another one, which The Semantic Web Blog covered yesterday here, looked at the expected transition from sentiment to emotions analytics.)

Augie Ray, director of social media at Prudential Financial, and formerly a social media leader at USAA and Forrester, recounted some of the bigger blow-ups online in recent years: The passenger whose guitar was broken by United Airlines and made a Youtube video that went viral; NBC’s 2012 London Olympics coverage that was criticized for dissing a tribute to the victims of terrorist bombings, among other things; and Bank of America’s being castigated for its announced plan to institute debit card fees.

“We live and die by the concept that negative sentiment matters,” he said.

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ArnoldIT reports, “At Cebit in Hannover, Germany, I caught up with Antonio S. Valderrábanos, the founder of Bitext. His company has been growing rapidly, announcing deals with Salesforce and the Spanish government. Bitext provides multilingual semantic technologies, with probably the highest accuracy in the market, for companies that use text analytics and natural language interfaces. I interviewed him in April 2008. Bitext has added significant and important technology to its multi-lingual content processing system. In addition to support for more languages, the company is getting significant attention for its flexible sentiment analysis system.” Read more

Bitext, provider of multilingual technology of Text Analytics for the main European languages, has announced the publication of the Portuguese version of the “Bitext API Demo” (http://svc8.bitext.com/api-demo), the demonstration platform of its semantic API. Along with Portuguese, there are already three languages available (English and Spanish are the other two) for testing Bitext API for free.

The services available in this platform are Entity and Concept Extraction, Sentiment Analysis and Categorization. This API can be tested using some pre-established texts and also interactively (by entering everyone’s own text). It is also possible to test the API programmatically, thanks to its easy integration into third-party systems. Read more

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Ever heard of Big Language? It’s a Big Data-affiliated term coined by customer experience management company SDL, to describe the challenge faced by the text analytics software community whose global customers want more in the way of supported languages.

“Text analytics is all about language, and the game is changing for translation,” says Brian Otis, vice president, Business Development, SDL Language Technologies. “If you look at all the user-generated content on the web, it’s not feasible to rely on human translation, even for many big companies. Machine language translation is the only economic and time-to-market and sane way to go.”

The vendor this week offers up its machine translation solution, SDL BeGlobal, as an integration option for text analytics software providers, so that they can keep up with more demands from global customers that want to understand what’s being said about them by customers in whatever language it’s being said, without busting their R&D budgets on fulfilling expectations for expanded language support.

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Dr. Carlo Trugenberger, co-founder and Chief Scientific Officer at InfoCodex Semantic Technologies AG, has co-authored a report reflecting the topic he discussed at last fall’s London SemTech event: An approach to drug research that relies on identifying relevant biochemical information using the company’s autonomous self-organizing semantic engines to text mine large repositories of biomedical research papers.

The model, says Trugenberger, is a departure from many other semantically-engineered approaches to streamlining drug research, which are based on natural language processing (NLP). That’s good for extracting information from documents, he says, but not as adept at discovering knowledge. “That’s what our InfoCodex software is designed for, to find new facts and hidden correlations” in repositories of unstructured information.

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The Software & Information Industry Association this year debuted a category for Best Semantic Technology Solution in its 2013 SIIA CODiE Awards. Two products were finalists in the category (see our story here), and yesterday the winner was announced. It was the Luxid Content Enrichment Platform from TEMIS Inc, which is used by publishers to automate the extraction of entities, relationships, concepts and topics from their digital assets, and augment content enrichment and linking.

It won out over finalist Elsevier with its ClinicalKey solution for helping doctors and clinicians search Elsevier’s medical and surgical content smarter and faster. ClinicalKey maps content to Elsevier’s proprietary medical taxonomy, and builds relationships using a semantic framework to faster and more clinically relevant answers. Elsevier, by the way, just ended the voting for its own ClinicalKey Key Innovator Awards, with the prize being a $10,000 grant to a U.S.-based hospital, medical school or institution that has demonstrated the most innovative use of information and technology to save lives and improve patient care.

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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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Like lots of other families over the recent Thanksgiving weekend, we made our way to the movies. Our choice: Life of Pi. We’d highly recommend it, and according to the IBM Social Sentiment Index, as applied to Moviegoer Social Sentiment over the holiday weekend, so too would a lot of other folks. It earned a 90 percent positive rating.

IBM has engaged in the social sentiment index pursuit in some other endeavors – using its advanced analytics and natural language processing technologies to analyze large volumes of social media data, it had another recent take on Black Friday, for example. It tallied up that shoppers expressed positive consumer sentiment on promotions, shipping and convenience as well as the retailers themselves at a three to one ratio (see our story here for other takes on semantic tech weighing in on the holiday shopping season).

It’s also applied its social media analysis smarts to studying births of trends (cycle chic is on the rise), and which tennis player was on the hearts and minds of the crowd at the U.S. Open (Novak Djokovic and Laura Robson winning the love, with positive sentiment scores at 90 percent or better).

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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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Anthony Ha of Tech Crunch reports, “We cover a lot of startups using social data, but Eldar Sadikov, co-founder of a company called Jetlore, said whenever someone else wants to build a social app, they have to ‘reinvent the wheel again and again.’ That’s why Jetlore is launching a platform designed to power these kinds of services. Sadikov said he wants his platform to serve as ‘a layer that sits between social networks and consumer Internet companies.’ The company’s specialty is analyzing pieces of text and matching that text with different topics.” Read more