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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.

Sentiment analysis is already an important component of many brands' social media strategies, but it can often be limited to basic interpretations of whether a conversation is positive, negative or neutral. At the Cannes Lions international advertising festival in June, data visualization technology provider Buzz Radar conducted an experiment that took sentiment analysis further, diving deeper into different types of emotional nuances. Patrick Charlton, director and co-founder of Buzz Radar, spoke to eMarketer's Maria Minsker just before the festival about what the company hoped to learn from the project. Patrick Charlton: Burberry has used our Command Center platform to look at conversations on social media surrounding their campaigns. We pull in every single mention of Burberry from conversations about London Fashion Week, for example, and analyze the sentiment.

It's something we've been thinking about over at Technical.ly Brooklyn HQ for a bit and it's come up in the news recently a few times, most notably last month with the Facebook Trending Topic Imbroglio of 2016. In the end, the big deal was that people thought that the trending topics that show up on the sidebar on Facebook were value-neutral and reflected only what people were talking about on FB. Well, turns out, no, people have an editorial role, too, and there were serious claims of liberal bias. But to take a step back, liberal bias, sensational bias, or otherwise, maybe we shouldn't have expected that the stories on Facebook or machine-generated content to be some magical, neutral, mechanical thing, anyway.

For a more technical explanation, this and this article can be read. Here you can find a good explanation as well as a list of the mostly used Kernel functions.

Sentiment analysis is a useful service for just about any business. It is always valuable to know whether your customers are saying positive or negative things about you. This gives you more flexibility to start with their sample and then tweak it to your needs. Then you would deploy it yourself and call it yourself.

AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains. AI and Machine Learning, Pattern classifiers and natural language processing (NLP) underpin Sentiment Analysis (SA); SA is a technology that makes rapid assessment of the sentiments expressed in news releases as well as other media sources such as Twitter and blogs. This conference addresses and explains how to extract sentiment from these multiple sources of information and showcases the advances that have taken place in the field of financial innovation. This conference builds on the findings of the six previous highly-regarded conferences on this topic. It highlights the recent developments in the application of AI and machine learning to trading strategies including automatic and algorithmic trading, quantitative fund management.

Emotion isn't a new frontier in business, of course; sentiment analysis and emotional branding have been in practice long before they were formalized. Focus groups date at least as far back as World War II and Mad Men fans will likely recall Draper's tryst with consumer-research (and consultant Faye Miller…) And, of course, as the 20th century progressed, technology joined customer insight's analog tool sets. But it's only more recently that tech-powered emotional analytics have really stepped into the spotlight.

This post is authored by Ollie Newth, Program Manager at Microsoft. Although text often contains highly valuable data for companies, extracting meaningful data from it can be a challenge. The field of text analytics utilizes natural language processing to extract meaningful structured data from text, and often includes areas such as sentiment analysis, entity recognition and linking, and text clustering. The Microsoft Text Analytics API is one of the Cognitive Services that can help you turn unstructured text into meaningful insights. The API is one part of the Cortana Intelligence Suite, a family of services that helps enterprises build large-scale analytics solutions.

Sarcasm is difficult to detect online, whether as a user or as an algorithm. Sentiment analysis can help, but there are limits to its effectiveness. An article from cloud-based social intelligence agency Infegy examines the problems resulting from the use of sarcasm online and offers solutions for more accurately identifying and dealing with sarcasm.