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.

A computer algorithm that can identify patterns in the social media activity of Islamic State supporters could provide clues about where terrorist attacks are likely to occur. Scientists have found they can spot distinct behavioural patterns in the interactions between groups on social media, and it could even help them predict'lone wolf' attacks'. Social media has been a key tool for organisations like Isis to help them recruit supporters and coordinate their activities. Scientists at the University of Miami have used equations used in physics and chemistry to track the constantly shifting behaviour of supporters of Isis (Isis flag pictured). While law enforcement authorities have attempted to keep track of Isis members using social media, they have tended to focus on monitoring the posts made by individuals.

As part of my Technology and Innovation MBA program at Ted Rogers School of Management, I took a data and knowledge management course which teaches students the principles and practices of knowledge management. The second part of the course delves on tools used in data management and analytics. Although the theoretical part of the course was a bit dry, the hands-on portion was very interesting and exposed students to several different tools to capture, clean and analyze data. One of the tasks given to students was to capture and analyze twitter data. Although students had access to Netlytics, which is a neat cloud-based text and social network analysis tool that also collects Twitter data, students were encouraged to find other ways to collect Twitter data.

I'm Julie, Director of Ops, back for a quick tutorial of two awesome new offerings we've built for you. I'm on the non-technical / cat loving side of things so I break it down a bit. I also run the Machine Learning Without a PhD group on LinkedIn where jargon isn't allowed. Feel free to join if you'd like #machinelearning4everyone So today you're getting a 2-for-1 (or as my dad likes to call it a "TooFer"). Today's topic will be the 2016 Tony Awards that aired this past Sunday night. Let's begin by heading to your dashboard.