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Mind Versus Machine: Human Insight in the Age of Automation - insideBIGDATA


The future is dystopian: a world in which we humble humans will be replaced by fleets of slick automatons – mechanical menials destined to not only solder, weld and glue us out of jobs, but account, diagnose, and translate us out, too. Or, so goes a certain line of argument. Certainly, there have been some heavyweight concerns voiced about the rise of artificial intelligence. Of course, there are counterarguments too. Just as the Industrial Revolution sparked fears around the supplanting of man by machine (fears which lead some as far as destroying the new mechanical marvels: hence today's use of the word'Luddite' to denote those opposed to technological progress), all new vistas are likely to provoke both optimism and hesitance.

They predicted President Trump and Brexit


Companies, and some governments, pay BrandsEye to alert them to significant swings in sentiment to put out fires and identify opportunities. Starting in July 2016, their machines used artificial intelligence (AI) to pull messages from social media feeds relevant to the US presidential campaign, primarily mentions of Hillary Clinton or Trump. BrandsEye then put the word out to crowdsource human analysis of individual messages. "We use people for what people are good at and machines for what machines are great at. And by using that interplay between the two, we are able to measure sentiment very deeply and by using computers we measure very deeply," says Kloppers.

Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise Machine Learning

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.

Data Scientists need designer labels too


Labels are how humans define and categorise different concepts. There's lots of evolutionary psychology, neuroscience and linguistics behind this, but without going into that, without labels human (and other animal) intelligence would not be possible. Labels are the algebra of everyday life.

Sentiment Analysis Just Got Smarter


Sentiment analysis, sometimes called opinion mining, is one of the easiest and quickest ways to find out what consumers are thinking about a brand, product or event. It's a natural language processing technique often used in social listening scenarios, that aims to systematically identify opinions in a document and give it a score of positive, negative or neutral. There are few things as mind-numbingly tedious as manually tagging documents with the right sentiment because the technology doesn't get it. Sentiment analysis (ironically) has a bad reputation in the social listening industry, because truth be told, it needs a lot of manual work to deliver great results. Our data science guys (the brains behind our award winning image recognition technology) have been working on fixing this behind the scenes, and I'm excited to finally share their fantastic results.