After we Save and Activate our process, we are now ready to test it.

Extracting value from a universe of data, analysing sentiment around company names (equities) or about anything else (macro), is a complex journey and we are only about 5% down that road. The parameters are evolving by which an ever-expanding data set, including the likes of Twitter, pictures, text, video is processed; relying on experts versus the wisdom of the crowd; sentiment derived from a "bag of words", as opposed to structured linguistic analysis. Last week's Unicom conference, AI, Machine Learning and Sentiment Analysis Applied to Finance (July 14) brought together a group of experts in this area. Professor Gautum Mitra, OptiRisk Systems introduced Elijah DePalma and James Cantarella, Thomson Reuters; Pierce Crosby, StockTwits; Anders Bally, Sentifi; Peter Hafez, RavenPack; Stephen Morse, Twitter. DePalma differed somewhat from the others because the Thomson Reuters sentiment engine uses only accredited Reuters news data, rather than raw social media chatter.

Access Facebook from the western half of North America and there's a good chance your data will be pulled from a computer cooled by the juniper- and sage-scented air of central Oregon's high desert. In the town of Prineville, home to roughly 9,000 people, Facebook stores the data of hundreds of millions more. Rows and rows of computers stand inside four giant buildings totaling nearly 800,000 square feet, precisely aligned to let in the dry and generally cool summer winds that blow in from the northwest. The aisles of stacked servers with blinking blue and green lights make a dull roar as they process logins, likes, and LOLs. Facebook has lately added some new machines to the mix in Prineville.

There are six main types of stories in fiction. That's what computer scientists found after teaching a machine to map the emotional arc of a huge corpus of literature. The overall research they did is fascinating (I wrote about it in greater detail here), but several smaller components of the work are compelling in their own right. To prepare a machine to carry out a sentiment analysis, for instance, computer scientists had to assign a happiness index to 10,222 individual words. That way, as the machine scanned passages from books, it could assess the emotional arc of the narrative.

Lexalytics, the leader in cloud and on-prem text analytics solutions, today announced that Chief Marketing Officer Seth Redmore will present "Natural Language Machine Learning: A Method and a Challenge to our Industry Competitors, Partners, and Friends" at the Sentiment Analysis Symposium in New York on July 12. While Machine Learning has the potential to positively impact every aspect of a business, access to date has been very limited. Seth will discuss the critical industry need for the machine learning industry to develop a more broad, user-friendly method to interact with machine learning, asserting that words (natural language) are the easiest for a user to comprehend. With over 20 years of combined experience in product management, marketing, text analytics and machine learning, Seth is currently the CMO of text analytics leader Lexalytics. Prior to this role, Seth held executive positions at both hardware and software companies, including co-founder of Netiverse (acquired by Cisco Systems).

It's a privilege to have Rama Akkiraju, IBM distinguished engineer and master inventor, participate as a Vision and Opportunity panelist at the 2016 Sentiment Analysis Symposium. I organize the symposium – this year's event takes place July 12 in New York – and recognize the many ways IBM has, over the years, expanded what's possible in the realm of what I'd characterize as "human data." "My team at IBM has been focused on developing technology to better understand people at a deeper level based on sentiment, emotion, attitude, and personality," said Rama. "With our work with Watson APIs – such as Tone Analyzer, Personality Insights, Emotion Analysis, and Sentiment Analysis – we're working to enable more compassion, engagement, and personalization in conversations across various channels." IBM's Marie Wallace, a 2014 sentiment symposium speaker, relates in a blog article that she "joined IBM in 2001 to build the next generation of NLP technology for IBM… the 3rd generation of IBM LanguageWare, which initially started back in the '80s." And I wrote, myself, in a 2008 InformationWeek article, BI at 50 Turns Back to the Future, about 1950s work by IBM researcher Hans Peter Luhn on the creation of business intelligence via text analysis.

WIRE)--International Data Corporation (IDC) has published a 2016 IDC Innovators report recognizing pioneering players in the machine learning-based text analytics market. IDC Innovators are companies with under 50M in revenue that offer an inventive technology and/or groundbreaking new business model. Kira Systems, Loop AI Labs, NetBase, and Taste Analytics were all named as IDC Innovators in the machine learning-based text analytics market for 2016. "Organizations are continually looking to improve their handling of data, especially unstructured data, given the explosion of information that is available via the Internet today," said David Schubmehl, Research Director, IDC's Content Analytics, Discovery and Cognitive Systems research. "Understanding and utilizing this human-generated data is a significant challenge for most organizations and the use of machine learning based text analytics is rapidly becoming the best approach to dealing with this type of data."

The consumer sentiment analysis of this one's pretty easy, but will they be compensated? When a person feels sufficiently wronged to lodge a complaint with the Consumer Financial Protection Bureau (CFPB), there's likely to be some negative sentiment involved. But is there a connection between the language they use and the likelihood they will be compensated by the offending company? At the upcoming Sentiment Analysis Symposium, I will discuss how machine learning and rule-based sentiment analysis can support each other in a complementary analysis, and produce actionable information from large amounts of free form text. In this case, machine learning and sentiment analysis could improve and evolve the CFPB's ability to assess consumer complaints.

Cognovi Labs is a new analytics startup that relies on Twitris, a Wright State University-developed tool that claims to be able to take a sample of social media chatter about a specific topic and deduce real-time, large-scale, automated sentiment about the specific topic they are researching. As a real-world example of the tool's capability, the Cognovi Labs research team -- led by Wright State University researcher (and Cognovi Labs inventor) Dr. Amit Sheth -- analyzed Twitter chatter leading up to the Great Britain/European Union Membership Referendum (Brexit) on June 23. The team was able to predict some six hours before the news broke that the polls leaning toward the "remain" camp were incorrect. This was predicted by running Twitter chatter through the Cognovi Labs Twitris tool. The machine learning tool leverages Cognovi Labs' semantic intellectual property to be able to automate and extract aggregate meaning from social media chatter (including slang) in new, more precise ways.