Machine learning is cool, but let's spend a few minutes talking process – the application of data science to derive business insights. Let's look in particular at capital markets, where news and mood drive trading strategies. Trading is highly competitive, yet traders like to talk, and StockTwits – "the largest social network for investors and traders" – is where they often do it. Traders flock to the platform to share assertions and perceptions, analyses and predictions. This activity produces a combination of hard data and subjective information that can be profitably modeled via natural language processing, sentiment analysis, and machine learning.

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The world of big data analytics is incredibly diverse, and people are coming up with new analytic tools and techniques every day. But one particularly productive combination that should not be overlooked involves the use of text analytics and machine learning. Tom Sabo, principal solutions architect at analytics giant SAS, says the one-two punch of predictive modeling on structured data, and text mining with unstructured data, can deliver insights that are more than the sum of their analytic parts. "They really run side by side," Sabo tells Datanami. "Let's say somebody has predictive models in place against whether customer will churn or to maximize profit, for instance. If they have text, like notes, in the rest of that structured data…we can incorporate that additional free form information for actionable insight."

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.

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.

BOSTON, June 30, 2016 (GLOBE NEWSWIRE) -- indico, an innovator in the machine learning and artificial intelligence space, will make a presentation on deep learning at the Sentiment Analysis Symposium, which takes place in New York, July 12th. Dr. Daniel Kuster, a researcher at indico, will focus on the differences between deep learning and traditional machine learning approaches, and how the advantages of deep learning can be exploited to quickly gain new insights about what people say online, and how they say it. The presentation will take place at Fordham University's Lincoln Center Campus in New York City. Machine learning is becoming the tool of choice for analyzing text and image data. While traditional text processing solutions rely on the ability of experts to encode domain knowledge, machine learning models learn directly from the data.

Computational linguists and computer scientists, among them University of Texas professor Jason Baldridge, have been working for over fifty years toward algorithmic understanding of human language. They are, however, doing a pretty good job with important tasks such as entity recognition, relation extraction, topic modeling, and summarization. These tasks are accomplished via natural language processing (NLP) technologies, implementing linguistic, statistical, and machine learning methods. Voice response and personal assistants -- Siri, Google Now, Microsoft Cortana, Amazon Alexa -- rely on NLP to interpret requests and formulate appropriate responses. Search and recommendation engines apply NLP, as do applications ranging from pharmaceutical drug discovery to national security counter-terrorism systems.