This is 2016 version of the Eurovision prediction. I have explained systematics in quite detailed fashion in the last year post which you can find here. Very shortly, I measured how many tweets have been sent about each song from each country. From this, I estimated amount of votes that each country would give to another. For example, if Germans tweets the most about Polish song, I assume that Germany will give Poland 12 points.

Just when you need Big Data, it's nowhere to be found. After Facebook made headlines this week for allegedly meddling with its Trending Topics section, several analytics firms that have provided International Business Times with social media data in the past declined to provide numbers related to the ruckus. The kerfuffle was kicked off by a Gizmodo report alleging the company's Trending Topics section suppresses conservative topics of interest, thanks to the whims of its curators. Within hours of the news, the U.S. Senate Committee on Commerce wrote a letter to CEO Mark Zuckerberg asking representatives of Facebook to travel to Washington for a briefing on its curation guidelines. And Thursday, Facebook released its full guidelines for news selection, showing the extent to which human judgment is part of the process.

In this talk, Mike Williams, Research Engineer at Fast Forward Labs, looks at how supervised machine learning has the potential to amplify power and privilege in society. Using sentiment analysis, he demonstrates how text analytics often favors the voices of men. Mike discusses how bias can inadvertently be introduced into any model, and how to recognize and mitigate these harms.

Unstructured data is the most prevalent form of information on the planet. It exists in our e-mails, surveys, social media accounts, call center logs, etc. With a strong text analytics strategy in place, companies can get critical information from this data to drive better business decisions.

Unless you've been living under a rock for the last few years, you have probably heard of TV shows such as Breaking Bad, Mad Men, How I Met Your Mother or Game of Thrones. While I generally don't spend a whole lot of time watching TV, I have also undergone some pretty intense binge-watching sessions in the past (they generally coincided with exam periods, which was actually not a coincidence…). As I was watching the epic final season of Breaking Bad, it got me thinking on how TV series compare to one another, and how their ratings evolve over time. I therefore decided to look a bit further into user rating trends of popular TV series (and by popular I mean the ones I know). For this, I simply had to define a quick scraping function in R that retrieves the average IMDB user ratings assigned to each episode of a given series.

As the adoption of sentiment analysis continues to spread across industries, from politics to PR, opinions about the field also run deep. That's especially true among practitioners, and a range of academic and vendor specialists weighed in at the Sentiment Analysis Symposium in New York last week. While the novelty factor begins to subside, clients are looking for more substance, and as befitting such a multifaceted topic, it's complicated. As a follow-up to yesterday's post that covered the analysis of visual images and facial coding, here the experts offered their perspectives on approaching 5 ongoing issues: The degree of accuracy issue is hard to answer, said Bing Liu, a University of Chicago computer science professor specializing in data mining. It depends on what you're measuring, the level of text you're analyzing, the number of data sets across domains and the voice sound quality of videos, among other variables.

Sentiment analysis is a powerful example of how machine learning can help developers build better products with unique features. In short, sentiment analysis is the automated process of understanding if text written in a natural language (English, Spanish, etc.) is positive, neutral, or negative about a given subject. Nowadays, we have many instances where people express opinions and sentiment: tweets, comments, reviews, articles, chats, emails and more. One popular example is Twitter, where real-time opinions from millions of users are expressed constantly. Companies use sentiment analysis on Twitter to discover insights about their products and services.

At first glance, it looks like Fox and Breitbart began to tank compared to the competition. But Corcoran says not to jump to conclusions. "I wouldn't put this decline down to the content of the sites themselves," the analyst told HuffPost. "In the case of Breitbart, their likes and comments actually increased between June 2015 and March 2016. In the case of Fox, those numbers include all local affiliates to the Fox network, so it's a broad coalition of sites, many of which may be seeing challenges in the way that their content is engaged with in the News Feed."

Our motive is to create a simple to integrate "Machine Learning" platform but yet powerful enough to provide high accuracy and low latency API. Such a system provides Data Mining, Machine Learning and Artificial Intelligence algorithms as a service. The system has ability to create training model for datasets uploaded as a training set and performs classification on similar datasets in the future using the saved models. "Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials." Download the sample "sentiment analysis" file Sentiment Analysis The first column should always be the label to be predicted.

This blog was originally published on our Text Analysis blog, the blog post set out to analyze and visualize 11 million tweets collected around the time of and during Apple Live 2014. Apple Live probably got off to the worst start possible earlier this year. Most of us who tried to log on to watch the much-anticipated launch were first, forced to watch the live feed in Safari and second, greeted with the TV Truck Schedule Screen... To add to this Apple also made a complete mess of the audio. We were left sitting refreshing the page, waiting for the stream to start while being subjected to an audio visual nightmare, described brilliantly by this "fan" below: To simulate the #applelive experience, open up several separate YouTube vids, play them simultaneously, minimize, stare at a test pattern. At AYLIEN, we gathered 11 million tweets mentioning'Apple', 'iPhone', 'iOS', 'iPad', 'Mac', 'iPod', 'Macbook', 'iCloud', 'OS X', 'iWatch' and '#AppleLive' from the 4th of September to the 10th of September with a view of analyzing the tweets to gain insight into the voice of Apple Followers.