With more than 225 indigenous languages, Europe is rich with multi-lingualism, and that’s why the Council of Europe proclaimed today the European Day of Languages. It aims to promote rich linguistic and cultural diversity and encourage lifelong language learning.

However, the interest in languages (and the human language in general) is not only close to the heart of institutions, but also – perhaps somewhat unexpectedly – to the global technology industry.

Historically computers only worked with very narrowly defined input like digits on punch cards. Over time, computers evolved to manage simple structured data.  Our human world, dealing with fuzzy facts and uncertainty thus needed to be translated into rigid structural representation. And reverse conversion also took place with the change from “computer language” into the “natural one” –  it was people who had to adapt.

To deal with large amounts of data we need a new protocol for communicating with computers, pushing it closer to the level of human communication skills where adaptation is now up to the machine.

Natural language has evolved over centuries, not only as a tool for information sharing. Reflecting the complex nature of our world, it includes much more than facts: multiple meanings, fuzziness, irony, humor and a great load of social functions. There is also a gap between the spoken and written form and our communication tends to be multimodal, accompanied by nonverbal aspects, such as facial expressions and body gestures.

For the first time we can see human language becoming a component of the computer world: after logic and mathematic computations. People’s greatest invention of all times, human language, has been now borrowed by IT experts and made into an information protocol for the upcoming big data era.

Working with more than one language, we must be aware of numerous linguistic differences. In some languages, for example, the word order may be flexible, while in others it tends to be rigid. In the latter case, one must pay attention to structuring the sentence, otherwise it sounds a little strange – just think of Yoda in The Star Wars: “Careful you must be.”  There are languages, like Czech, that only work with three basic tenses – past, present and future, while some others (including English or Romanic languages) have finer and more complex tense systems to express succession of events.

At IBM’s R&D Lab in Prague, we are focusing on conversational interactions between people and machines:

• One area is dialogue systems that are used for safe information access and communication while driving a car. One example is GetHomeSafe, a European Commission FP6 collaborative project carried out together with partners including Daimler, Nuance and universities KTH and DFKI.  The project explores in-car systems working not only in passive mode (answering driver’s questions), but also taking initiative and pro-actively serving information useful for the current driving context based on the user’s profile and content learning. Such systems are balancing several key metrics such as driver’s safety, short task completion time, and user’s satisfaction and driving comfort.

•  Other examples may include multimodal dialog applications for smartphones; conversational information kiosks with talking avatars; education tools for improving language skills (Reading Companion), brain jogging voice interfaces for senior citizens.

The development of linguistic interface largely owes to the proliferation of smart phones and tablets: with the absence of keyboards, the speech appears to be the prime choice for information access and transactions. Speech and natural language understanding technologies have been maturing in labs for many years and it was the emergence of intelligent personal search devices that made them so popular today.

New solutions must be easily adjustable for various language environments: this will be the era of human language which also means a great boom for linguists and multidisciplinary experts – computer scientists with machine learning skills including psychologists, statisticians, machine learning and other experts. And this new wave of linguistic computing certainly surpasses borders of Europe and applies to the whole world.

Technorati Tags: European Day of Languages, IBM, linguistics, R&D

By Malcolm Greaves, Undergraduate student, Carnegie Mellon University

It seemed like any other early Wednesday night as an undergraduate computer science student at Carnegie Mellon: I had programming assignments to start and papers to complete. I thought to myself I should find a quiet, empty room on campus, set up shop, and tackle my work right away. But I couldn’t – instead I headed down to a large auditorium on the fourth floor of the Gates computer science building. As a budding machine learning researcher and avid student of modern artificial intelligence, there was something that I absolutely had to see: a one million dollar match between two grand champion Jeopardy! players and a computer system IBM developed and named Watson.

Click here to view the embedded video.

Watching Watson triumph on Jeopardy! was inspiring: it was the stuff that we computer scientists live for. I clearly remember sitting on the edge of my seat, heart racing, and eyes glued to the screen and thinking, with the slightest hint of jealousy, “I wish that I could work on Watson.” At the conclusion of the match, when Watson showed the world exactly the kind of stellar performance that we can expect from it, I thought, “those are all PhDs who worked on Watson; it will be a very long time before I’m qualified enough to work on Watson.

I am so very glad I was wrong. Half a year after watching that historic match I learned that IBM created it’s first-ever Watson internship program and I, along with nearly 1400 other students applied to participate. Along with 18 others, I was fortunate to be selected and have spent the past 3 months working with IBM developers, researchers and software and business industry experts in IBM’s software group in Littleton, Massachusetts with the goal of commercializing the most advanced, state of the art artificial intelligence system.

As eager as I was to explore Watson, in the beginning I was apprehensive about this internship. My perception of IBM was of a monolithic, sturdy, awesomely gigantic company that made me worry that I was walking straight into the movie Office Space. My first day was filled with so many buzzwords, IBM lingo, protocols, (the occasional IBM joke) and “blue” everything that I thought I should have worn a smock rather than a pair of khaki pants. I thought to myself, “Am I going to be painted over to blend with the rest of this company? Or will I remain an individual?”

After a week of adjusting to the ebb and flow of IBM’s Littleton lab my preconceived notions were dispelled. In practice, I have found that the day to day operations of IBM, from the perspective of a software engineer, are like a small business. Most days, I directly work with less than ten people. As such, I’ve been able to nurture close bonds with my colleagues; this includes full time IBMers as well as fellow interns. Working at IBM has a small business feel coupled with the vast resources of one of the largest businesses in the world – when my team needs a new server to run multiple experiments simultaneously on gigabytes of data, we receive one in less than a day. When I cannot figure out how to solve a problem, and my internet searches turn up nothing, there is an IBMer an email away who is more than willing to help. I have found that working at IBM, you sense your efforts and individualism are being woven into a greater, more complete whole.

At the conclusion of my internship experience, I can reflect and say working on Watson has been an excellent experience. My fears of being painted over and hoarding red staplers were unfounded. In actuality, throughout the summer, I was graced with not only an intellectually stimulating project, but with a group of fantastic, intelligent, and determined colleagues. My mentors were brilliant, helpful, and wise and I am grateful that I have learned so much under their tutelage. My fellow interns have been a blast to work with and we have been able to produce some amazing work in just twelve short weeks. The scope and ambition of the entire IBM Watson division is inspiring; I feel honored to have been one of the inaugural interns working on Watson this summer for a company that has never stopped changing the world.

Technorati Tags: Carnegie Mellon University, IBM, intern, internship, Watson

Dimitri Kanevsky demonstrates an Internet-based system for capturing real-time transcripts of teleconferences.

Kanevsky, a member of the speech and language algorithms department at IBM Research, has invented a long string of hearing- and speech-related  technologies. They include  a system for helping people improve the effectiveness of lip-reading, a method that enables deaf people to converse on the telephone and an Internet-based system for capturing real-time transcripts of phone conferences. “I like to solve challenging problems, and I get a thrill from creating novel math concepts and making discoveries,” he says.

Today, Kanevsky will get another kind of thrill–when he’s honored with a Champion of Change award at the White House. The award recognizes individuals who make a positive impact on science, technology, engineering and math for people with disabilities.  Here’s a livestream video link for the event.

While Kanevsky has a long record of achievements as an inventor, including 152 US patents, it’s clear from talking to him that some of his most important inventions may come in the future.

One area where his work could have a significant impact is in turning IBM’s Watson computer into a conversationalist. The brainy computer, which gained fame last year by beating two past-grand-champions on the TV quiz show Jeopardy!, translates text into speech, but, so far, doesn’t understand speech. Potential users of the technology say they would like to be able to converse with the machine in their work settings.  Physicians, for instance, envision using IBM Watson as a well-informed adviser when they’re examining and talking to patients.

Kanevsky can help out. He and colleagues at IBM Research have contributed notable improvements to the Baum-Welch algorithm, which is used to make up for shortages of training data in speech-recognition systems. These days, they’re developing new methods for producing more accurate results in situations where people speak with heavy accents or use jargon, or where there’s a lot of background noise.  These so-called discriminative algorithms could make possible real-time speech recognition systems that are fully automated and extremely accurate.

Watson is a first step in the emergence of a new era in information technology–which IBM calls the Era of Cognitive Systems. Big shifts are coming in chips, systems, data and programming. Kanevsky believes that these developments offer the promise of major breakthroughs in accessibility technologies–but only if those who architect them take people with disabilities into account from the start. “If this is done in the right way, it will have a tremendous impact on all people with disabilities,” he says.

One of Kanevsky’s earlier inventions, Artificial Passenger, is a system for keeping sleepy drivers awake by telling them stories and jokes, asking questions, and suggesting that the driver take a rest break if it detects that she or he is sleepy. Kanevsky came up with the idea after keeping his wife, Galina, awake by talking to her on a late night drive.

Watson’s a whole lot smarter than Artificial Passenger. For example, Watson can digest the universe of published information about a particular topic and then answer open-ended questions about it. So adding conversational capability to Watson would take speech recognition to a whole new level. Systems can be created that understand both speech and meaning. That’s a worthy challenge for an ambitious serial inventor.

Technorati Tags: Champion of Change, Dimitri Kanevsky, IBM, Watson, white house

Coming from IBM Research, I think of innovation in two dimensions.  First, there is the continuous innovation that goes into IBM’s products and services.  This innovation provides important advances to current technology as well as helps IBM introduce breakthrough products.  The benefits of this approach are clear in IBM’s next-generation computing platform PureSystems.

But we also look for more exploratory challenges that help us advance science by leaps and bounds.  We call them grand challenges.  Meeting them requires a very different set of practices and capabilities – and presents some interesting problems.

And that was the topic today as the Information Technology and Innovation Foundation in Washington, DC hosted a forum with the White House’s Office of Science and Technology Policy, IBM and Qualcomm to discuss how we can meet the next Grand Challenges.

So, what makes a grand challenge?  It must be difficult, inspiring and risky.  These are scientific game-changers by nature – with broad implications for industry and society alike.  Some call them ‘moon shots.’

And as the Apollo program of the 1960′s proved, grand challenges often demand new, cross-discipline thinking and rich models of collaboration among various institutions.

Creating a computer that could compete against human chess champions was a grand challenge. Since the late 1960’s popular wisdom suggested it would be impossible for a computer to ever beat a human grand chess master.   The Deep Blue computer’s winning match against Gary Kasparov in 1997 was a landmark in showing that we understood computing technology enough to take on the task – one we thought was restricted to human intelligence.

The late 1990’s saw a grand challenge of another kind.  In 1997, the Japanese government announced plans to build the world’s most powerful supercomputer – the ‘Earth Simulator.’ In 1999, IBM announced plans to build a new class of high performance computing system that would be even more powerful. BlueGene would go on to, among many other things, crack the complex problem of protein folding.The project represented a deep collaboration between IBM, the Department of Energy and the agency’s National Nuclear Security Administration – in a shared mission to apply the new technology at extreme scales in a highly energy-efficient manner.  And in 2004, it would become the world’s most powerful supercomputer.

Our newest grand challenge is of course the IBM Watson computer.  In the mid-2000′s it had become increasingly clear that deep search and deep analytics were becoming a challenge for commercial computers. Watson was developed to tackle question/answer problems on the largest of scales – and it famously showcased that capability in its Jeopardy! victory.

We continue to pursue this challenge today, extending Watson’s capabilities to industry, medicine and science.  This requires deep collaboration with domain experts like Citi and the Memorial Sloan-Kettering Cancer Center.

These models of collaboration are quickly becoming the standard for tackling today’s grand challenges.  This is why we work closely with our partners as well as encourage our own researchers to work across disciplines.  These are the things that produce the big breakthroughs – and are the blueprint for addressing the grand challenges of tomorrow.

IBM created a culture that embraces the idea of tackling the most difficult challenges. It’s been a priority for us no matter the business environment, so when necessary, we made the tough choices that allowed us to continue investing in the future.

By working together, with both private and public entities to drive research and innovation, we will continue to tackle the world’s largest problems, transforming society and making the world work better.

Technorati Tags: IBM, Information Technology and Innovation Foundation, Qualcomm, White House Office of Science and Technology Policy

Cancer is the second most common cause of death in the United States, and, according to the American Cancer Society, more than 1.6 million new cases are expected to be diagnosed this year. Discoveries in molecular biology and genetics in recent years have produced new insights into cancer biology, but these advances have also ratcheted up the complexity of diagnosing and treating each case.

The disease is one of the most important fields of medicine, yet it’s devilishly complex and there’s too much information for any single practitioner to keep up with.

A collaboration announced today between Memorial Sloan-Kettering Cancer Center in New York City and IBM could revolutionize how physicians  in the United States and worldwide get access to world-class information about cancer.

Click here to view the embedded video.

Our two organizations are combining IBM Watson’s natural language processing and machine learning capabilities with Memorial Sloan-Kettering’s clinical knowledge and repository of cancer case histories. We aim to develop a decision support tool that can help physicians everywhere arrive at individualized cancer diagnostic and treatment recommendations for their patients based on the most complete and up-to-date information.

I credit leaders at Memorial Sloan-Kettering for envisioning a way to have a huge impact on cancer treatment worldwide. Patricia Skarulis, the organization’s chief information officer, first approached us last April, shortly after she watched the Watson computer defeat two past grand-champions on the Jeopardy! TV quiz show. She said MSK had collected more than a decade’s worth of digitized information about cancer–including treatments and outcomes for all of their patients–which could be mined for insights and made widely available.

She thought Watson could help. We decided to work together to try to make that happen. And, today, we believe the goal is attainable.

Since Watson’s television victory last year,  IBM has been on a path to improving the technology. We’re making it possible for people to engage Watson in ongoing dialogues aimed at surfacing the most useful insights. After receiving an initial query, Watson will be able to ask for additional information to help it understand more precisely what the human wants to know. Also, people will be able to view the logic and evidence upon which Watson makes a recommendation.

Memorial Sloan-Kettering’s oncologists will assist in developing IBM Watson to use a patient’s medical information combined with a vast array of medical information–including an extensive library of medical literature, diagnosis and treatment guidelines, a database of MSK cancer cases and the institution’s knowledge management system. Watson will learn from its encounters with clinicians. It will also get smarter as it amasses more information and correlates treatments with outcomes.

Our two organizations will spend most of this year loading Watson with information. This data will be used to train a version of Watson created specifically for this task. Then, starting late this year and continuing in 2013, we’ll run a pilot program focused on the diagnosis and treatment of a handful of cancers, including lung, prostate and breast cancer.

Memorial Sloan-Kettering is one of the most accomplished cancer treatment centers in the world. But, when you do the math, you see that only a small percentage of cancer patients are able to receive care at MSK and other world-renown institutions.

The vast majority of patients are treated by physicians who don’t have access to the more advanced knowledge that MSK oncologists possess. If MSK and IBM succeed at developing an effective decision-support tool, physicians anywhere could potentially have access to the knowledge of some of the field’s top experts–and more cancer patients could get better care no matter where they live in the world.

Can you think of other fields where IBM Watson could help bring specialized expertise to the masses?

———–

Here’s a link to a post about the hookup between IBM and WellPoint, the giant health benefits company, which is complimentary to the MSK relationship.

Technorati Tags: IBM, Memorial Sloan Kettering, Watson

By Jonathan Taplin
Director of the Annenberg Innovation Lab
The University of Southern California

  As the parade of gowns and penguin suits made their way down the red carpet and into the Oscar awards ceremony last night, I had one eye glued to my TV and the other to my Twitter feed.

For more than three decades, my career in entertainment has spanned the worlds of music, film, technology and finance. As a long-standing member of the Academy of Motion Picture Arts and Sciences and the Director of the Annenberg Innovation Lab, I always await this event with eager anticipation.

But this year I decided to marry my love of film, digital media and technology by applying science to the Oscars. Why?  I wanted to better understand how the public’s opinion of Oscar nominees stacks up against the actual winners on awards night.

Like many movie fans, not all of my favorite picks mirror the Academy’s choices, or those of the movie-going public for that matter.  I’m not ashamed to admit that I was shouting at the TV during the 2011 Oscar telecast when “The Social Network” didn’t take home Best Picture. That’s why the collaboration between our Lab, IBM and the LA Timesto create the Oscar ‘Senti-Meter’ was so groundbreaking.

Using advances in analytics and natural language processing, the Senti-Meter enabled us to analyze millions of daily public comments via Twitter, comparing volume and even more importantly assessing the tone. It let us pick up on positive, negative and neutral opinions, even snarky vs. sincere tweets about the best actor, actress and film nominees.

Click here to view the embedded video.

Like similar analyses by the Lab and IBM on fashion and sports, we’re now able to take advantage of technology to uncover hidden insights and better understand consumer sentiment.  It was a fun way to follow the Oscars horse race, for sure, adding a visual and entirely new dimension to reporting by the Los Angeles Times.  And it was also a terrific way for our students to gain highly sought skills in the latest technologies that will set them apart in the business world.

But this is about even more.  Social sentiment analysis can change the way movie studios typically operate by giving them the ability to quantify the types of movies and celebrities fans prefer. It can also help movie distributors better ensure box-office success by tailoring marketing campaigns to specific audiences. Studios can use this technology to help evolve the test screening and exit survey process that typically involves polling a much smaller sample size with predetermined questions. By adopting social analysis, the studios can cast a far wider net and gain more thoughtful and honest feedback.

I believe analytics is the next game changer for Hollywood.  These technology tools will influence entertainment content and dramatically impact the bottom line by using consumer sentiment to create future box office hits.

So you should be aware that as you were pecking out Tweets last night during the Oscars, you were participating in the early days of a grand experiment on how we’ll put all the data in our lives to work. From now on, via Twitter and other social media, the voices of movie fans will be amplified and will be “Extremely Loud”, but you won’t need to be “Incredibly Close” to the red carpet to be heard.

Jonathan Taplin is a Professor at The University of Southern California and the Director of the school’s Annenberg Innovation Lab. He is a member of the Academy Of Motion Picture Arts and Sciences. The Annenberg Innovation Lab and IBM are collaborating on student-led research to apply analytics technologies to business.

Learn more about how IBM and USC are collaborating on analytics and social sentiment.

Read more about how the Oscar Senti-Meter project will impact the media and entertainment industries.

Technorati Tags: Analytics, Big Data, business analytics, communications, data, IBM, IBM Research, predictive analytics, retail, smarter commerce, Smarter Planet, social business, Watson

A year has passed since the Watson computer developed by my team at IBM Research defeated two all-time champions on the TV quiz show Jeopardy! A lot has happened since then. IBM launched a new business, IBM Watson Solutions, which is tasked with commercializing the technology. The Solutions team is developing versions of Watson for a number of industries, starting with healthcare and financial services. (Suggestions? Tweet to #WhatShouldWatsonDoNext?) Meanwhile, there’s plenty to do in IBM Research. We spent four years developing Watson for Jeopardy!, but that’s just the beginning of what Watson can become.

Watson is a first step in a new era of computing. There were two previous eras in the evolution of data processing machines: the tabulating era, which began in the late 1800s; and the computing era, which started in the 1940s. We’re now entering a period when machines will become increasingly capable of learning – graduating from moving bits around to understanding what they mean and how they apply to our lives.  These machines will be ubiquitous. They’ll be extremely powerful. And they’ll utterly transform the relationships between humans with computers. No longer will computers be simply data processing devices. Think of them as intelligent machines.

This new era of computing is being enabled in part by epochal shifts in technology that we believe will enable people to make the planet smarter in every dimension. Because the world is increasingly instrumented, we can gather immense amounts of information about everything from climate change, to the way transportation systems interact with one another, to the changes in society caused by government actions. Because computing devices are interconnected, all of that data plus many of the digital communications between people can be combined in ways that turn it into useful information. Using analytics tools, we can explore our troves of information and understand better how the world works, predict what will happen next and make better decisions.

With the Jeopardy! experiment, we showed that we could teach Watson how to gather information, how to interpret it in context and how to share insights with humans. Watson 2.0 and 3.0 will continue to expand this ability to learn. A future Watson will learn by analyzing the huge reservoirs of knowledge captured in human language, drawing inferences and engaging with humans to expand and validate its knowledge. Watson will help us grapple with information overload by enabling people to absorb, integrate, evaluate and apply otherwise unimaginably large volumes of data.  Think of it this way: A new era of computing will facilitate an intelligent dialogue between an individual and ALL other humans. These machines will make it possible for humans to collaborate in much more powerful ways than they can today.

Here’s a scenario that helps me picture the role that intelligent machines could play in society a decade or so from now: Today, governmental leaders in democratic societies make their decisions based on the best information they can gather, their own beliefs and political calculations. The problem is, the systems they deal with–everything from healthcare to national defense–are not only extremely complicated, but they evolve over time. These systems are beyond the ability of one person or even teams of people to understand, manage and predict. So we’re on the defensive. We are in reactive mode — waiting for things to break and then responding to local disruptions and often missing the big picture.

As a result, leaders and decision makers lack the information and analysis necessary to know what is truly the best course of action or the best policy for their city or nation. They end up making decisions that are primarily weighted towards personal beliefs and political considerations.

But what if they knew much more? What if computers could gather all manner of data about the complex systems of society, digest it, and make rational and well-documented predictions about the consequences of particular actions? Also, imagine that this data is open and available to all citizens where they can explore the implications of different policies as if exploring a simulated world.

In this scenario, leaders will have a crisp and more transparent understanding of what the best decision or policies might be. Every citizen can easily explore the simulated outcomes of well rationalized scenarios rather than rely on sound bites or politically motivated guesses. Leaders will be under tremendous pressure to do what makes the most sense.. In this way, intelligent computer systems can help us control our collective destiny.

There’s a lot of work to be done by scientists to get from where we are today to a future where intelligent machines can help transform the way societies, governments and businesses operate. At IBM, we’re improving the Watson technology in four key dimensions. We’ll extend the information that Watson understands from specific questions to problem-solving scenarios. We’ll shift from simple question-and-answer interactions with humans to rich conversations. We’ll enhance Watson’s ability to explain its results. And, finally, we’ll change how Watson learns. Instead of depending on human programs to feed it information in batches, Watson will be able gather information continuously and learn deeply about specific domains of knowledge.

It was a thrill to lead the team that created a software program that beat very smart humans at Jeopardy! But it’s even more thrilling to lead the team into a new era of computing.

Technorati Tags: Analytics, David Ferrucci, ibm watson, Jeopardy

Click on the image for a larger view:

Technorati Tags: Analytics, healthcare, ibm watson, IBM Watson Solutions, Smarter Industries, Watson

By Steve Canepa, General Manager, Global Media & Entertainment Industry

February seems to be a month of excitement for all movie, television and sports enthusiasts. It’s that time of year – Super Bowl madness and Oscar Buzz – frenzy so electric that it transcends worlds – into the social media world. Think about it, how long does it take for you to see a Tweet or Facebook post once you hear the winner for Best Motion Picture or following the first touch-down? Seconds?

Information flows so quickly that Twitter alone is handling approximately 35MB of data a second, every second. The majority of this social media data represents public ‘streams of consciousness’, data that approximates human thought and speech, what we in the business call unstructured data.  But, as anyone who has filled in a tax form, booked a flight or applied for a loan knows: computers prefer data with structure, data fields that have entries in strictly controlled formats.

The good news is change is coming. Computers are becoming smarter about unstructured data (unstructured data isn’t just natural language … it’s photos, videos, emails, tweets, audio, sensor data, mobile device data).  For example, using advanced analytics technologies and natural language processing we can now begin to understand the patterns behind human expression. Not just ‘key words’ that have been identified and indexed, but all words, as we type them or say them.  We may have spent most of the computing age training humans to communicate with computers, using methods optimized for the machines, but today the reverse is happening. We’re now training computers to communicate with us and understand us in our own language. It is not easy. It is as the IBM Research team behind Watson declared, a Grand Challenge. But it’s a challenge that can lead to some very important and far-reaching results.

Watson represents a pinnacle achievement in Deep QA and natural language processing but there are many routes to the top and plenty of room for additional exploration and discovery. The team of researchers, students and faculty at the University of Southern California (USC) Annenberg Innovation Lab are taking a slightly different approach to the Grand Challenge. Rather than using the Answer Question formulation of Jeopardy!, they are applying IBM analytics software, and some very smart coding and modeling, to train computers to understand and analyze Tweets. The project is part of an ongoing collaboration between the lab and IBM to explore how technology can be used by organizations from news outlets and journalists to movie studios, broadcasters and retailers to better understand, respond, and predict public sentiment. To date, the model has been applied to film forecasting, the World Series and fashion retailing trends, in an effort to identify social media trends and better understand public opinions. For example, just last week IBM and USC analyzed millions of public tweets to determine the fans’ sentimental Super Bowl Quarterback favorite – Tom Brady or Eli Manning. Just like the game, Eli Manning in a late game-changing move, overtook Tom Brady as the Social Media MVP with 66% positive sentiment vs. Brady’s 61%.

But why stop at the World Series and Super Bowl? AIL and IBM are now collaborating with the Los Angeles Times to measure moviegoer sentiment toward the upcoming Academy Awards race.  Dubbed a ‘Senti Meter’, we’re analyzing Oscar- related positive and negative opinions shared via millions of tweets to determine who will win “The People’s Oscars”. The project has been profiled by the Los Angeles Times and we can all follow the evolving sentiment for Best Actor, Best Actress and Best Picture categories over the next two weeks by visiting http://graphics.latimes.com/senti-meter/.

This project is much more than just analyzing which best picture or movie star fans are rooting for – it’s an example of how movie studios can better understand their audience preferences and use social media to improve their marketing programs and in turn improve box office results.   There is no doubt that the Twitterverse and other social media platforms are changing communication as we know it. Tweets, Facebook and blog posts are becoming a vital resource for many organizations including the media industry to identify trends, inform reporting and understand as well as connect with their audience.

Think of how much change in the last year has been driven or expressed or reported in social media. Think how much social value could have been derived if we’d had the ability to understand and react to these social media conversations and sentiments – in context and in real time. We can now analyze the vast river of public data that streams from Twitter in its unstructured complexity, and apply a level of sentiment to the commentary. In other words the computer can now determine, with the certainty level of a non-native speaker, that the tweet it just analyzed expressed a positive or negative sentiment and how strongly that sentiment was stated – all in real-time. We can then apply this analysis to deliver business value – the effectiveness of marketing activities, customer responses to services, products and promotions, the impact of advertising, or the reaction to real world events…   the list is limitless.

This new capability will eventually deliver solutions founded on semantic analysis of Big Data that are only just now being imagined. And it will happen faster than we expect. Stay tuned, there is more on the way….

Learn more about the work IBM and USC are doing on social media sentiment http://www.ibm.com/press/us/en/pressrelease/36720.wss

Technorati Tags: Academy Awards, Analytics, Big Data, business analytics, Education, Entertainment, entrepreneur, IBM, IBM Research, Jeopardy, Media, nfl, Oscars, picture stories, predictive analytics, retail, smarter commerce, Smarter Planet, social business, social media, storytelling, twitter, Watson

John Squire is IBM’s director of Digital Marketing and Analytics.

Updated Post
AN UPSET IN THE MAKING
3 February 2012, 11:30 AM Eastern

Just like on the field, Eli Manning is riding a late surge to overtake Tom Brady in the IBM and USC analysis of Super Bowl XLVI social media sentiment.  Overnight results of Super Bowl Twitter buzz drove Giants quarterback Eli Manning’s ‘T score’ for positive sentiment ahead of Tom Brady. Manning now leads with 66% vs. Brady’s 61%, which represents an 8-point shift compared to the previous day. In another interesting development positive sentiment for Giants head coach jumped dramatically with his score rating increasing to 76% positive. That places Coach Coughlin above all of the players and coaches on both teams.

This day-to-day shift in Super Bowl fan sentiment illustrates the speed at which consumer sentiments can shift online — a factor that businesses are watching closely due to the potential impact on their brand equity and sales.

By applying analytics in social media settings we can identify nuances – positive, negative, irony, snarky vs. sincerity, in real-time.  That’s enough time to help an organization, or in this case professional athletes, adjust their comments and actions to dramatically (and positively) impact their brands.

Original Post
SUPER BOWL ANALYSIS TAKES US BEYOND THE TWEETS
2 February 2012

One of the most dramatic NFL games ever played was Super Bowl XLII pitting the undefeated (18–0) New England Patriots led by record-setting quarterback Tom Brady against the surprising NY Giants with young, unproven Eli Manning at the helm.   A thrilling, some say shocking victory for the Giants ended the Patriots bid to be the only 19–0 undefeated champion in league history.  And now Super Bowl XLVI –  The Rematch —   anticipated to be the most watched American television show in history, promises to take social media to a whole new level.

As my colleague, and former NFL player Kevin Nosbusch posted on Wednesday, IBM and the University of Southern California Annenberg Innovation Lab are conducting the first sentiment analysis of the two Super Bowl quarterbacks to illustrate how new analytics technologies make it possible to quickly assess the positive, negative and neutral sentiments shared by fans.

Why is this sentiment analysis important to IBM? In addition to being a longtime partner of the NFL, IBM recognizes that its clients, just like football players, are closely connected to their brand presence.

Using advances in analytics companies, academics, journalists can gain new insights into consumer perceptions via social media on endless topics from football and baseball to movies and retailing. Technologies can even distinguish irony and figure out which tweets are just background noise and those that are truly important.

Branding Upset on the Digital Playing Field

The Super Bowl analysis shows us that today the two quarterbacks, Tom Brady and Eli Manning are in statistical dead heat:  Brady earning 65% positive sentiment and Eli Manning earning 62% positive sentiment.  That actually represents a big branding upset on the digital playing field. Most sports and marketing followers would assume that Brady should be far ahead given his lofty status as an elite QB for many years and three championship rings.

Other noteworthy findings show that wide receivers have upstaged the quarterbacks, who are being positioned in the news media as the chief protagonists — Wes Welker is #1 in positive sentiment and Victor Cruz is a close 2nd.  Interestingly Brady leads by 3% points, exactly the point spread Las Vegas oddsmakers have favored the Patriots.

So while it looks like Tom Brady is going into the game as the Social MVP, now is not the time to get cocky.  Eli Manning is holding his own against the more experienced Brady in terms of positive sentiment.

The IBM USC analysis illustrates the potential insight and benefits that social media analytics can deliver to a brand — whether you’re an professional football player or a global enterprise.  Businesses that ignore the impact of social media will be stuck on the sidelines.

Learn more about IBM and USC AIL social media analysis projects.

Technorati Tags: Analytics, Big Data, business analytics, Education, entrepreneur, IBM, IBM Research, Jeopardy, nfl, picture stories, predictive analytics, retail, smarter commerce, Smarter Planet, social business, storytelling, twitter, Watson