Text Analytics Poll Shows Le Pen Positioned to "Trump" Macron To Americans following the French Presidential Election taking place in less than a week, it might appear as though recent history is repeating itself. And in many ways, it is. The post Text Analytics Reveals Potential French Election Upset appeared first on OdinText.

Use our LinkedIn Login to download this post to PDF or save it to MyLibrary! Everyone has been hearing about AI, some have been hearing about NLP - and everyone has an opinion, belief, or thought about AI. However, that opinion, belief, or thought about AI (and/or NLP) is fully dependent upon the voices of whom everyone has chosen to listen. We are at a stage in our rate of change of technology where we have to let go of how we learned things in the past -- and step into a new stage of keeping ourselves always available to learn, no matter what we believe and/or think. We cannot, more than ever before, solve our problems with the same thinking we used to create them (said Albert Einstein nearly 100 years ago!).

Less than six months ago, Mark Zuckerberg dismissed the idea that the social publishing platform he founded was being used to manipulate voters as "pretty crazy." But in a new report, Facebook now says it has data that "does not contradict" a key U.S. intelligence report that describes "information warfare" ordered by Russian President Vladimir Putin and carried out on Facebook and across the web. "Russia's goals were to undermine public faith in the U.S. democratic process, denigrate Secretary Clinton, and harm her electability and potential presidency," officials wrote in a declassified version of the U.S. Director of National Intelligence report in January. Guided by the Russian government's "clear preference" for Donald Trump, the DNI report said, Moscow followed a strategy "that blends covert intelligence operations--such as cyber activity--with overt efforts by Russian Government agencies, state-funded media, third-party intermediaries, and paid social media users or'trolls.'" Scholars have long theorized about the possibility of people manipulating public opinion on Facebook--Facebook itself carried out a mood experiment on its users--but U.S. intelligence officials call Moscow's latest meddling "unprecedented."

On average approximately 40 to 50 percent of tasks in a call center are good candidates for automation. These are tasks that a call center agent or manager can trigger – updating your address, for example. The dialog between the AI and the customer is controlled by how the AI application is programmed and closely measured with human oversight. AI does not run without tight controls in place. The analytics include sentiment analysis that tells management which AI-conducted customer interactions were positive or negative.

Facebook must obtain the permission of German users of WhatsApp before processing their personal data, a German court confirmed on Tuesday. Last August, Facebook subsidiary WhatsApp changed its privacy policy to allow the transfer of its users' personal information to Facebook for processing. That angered the Hamburg Commissioner for Data Protection and Freedom of Information, which in September ordered the companies to stop the transfer until they had obtained users' consent, and to delete any data they had already transferred. Facebook challenged the order in Hamburg's administrative court, and on Tuesday the court handed down its ruling. The court upheld the Commissioner's requirement to obtain consent, but threw out the order to delete the data on procedural grounds.

Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set.

Wherever the winds of the market may blow, he always seems to find a way to deliver impressive returns for his investors and his company, Berkshire Hathaway. Every year he authors his famous "shareholder letter" with his musing about the market and investment strategy and -- perhaps as reflects his continued success -- this sentiment analysis of his letters by data scientist Michael Toth shows that the tone has been generally positive over time. Only five of the forty years of letters show an average negative sentiment: those correspond to market downturns in 1987, 1990, 2001/2002 and 2008. Michael used the R language to generate a sentiment score for each letter, and the process was surprisingly simple (you can find the R code here). The letters are published as PDF documents, from which the text can be extracted using the pdf_text function in the pdftools package.

Back to the University Ranking of my designed application. Ranking technology in my application is to parse tweets crawled from Twitter and then rank related tweets according to their relevance to a specific university. I want to filter high-related tweets (topK) to do the Sentiment Analysis, which will avoid trivial tweets that make our results inaccurate. There are may ranking methods actually, such as rank them based on TF-IDF similarity, text summarization, spatial and temporal factors or machine learning ranking method. Even Twitter itself has provided a method based on time or popularity. However, we need a more advanced method which can filter out the most spam and trivial tweets.

Many social media, like Twitter, Facebook and etc, are evolving to become a source of information for people to scrape varied kinds of data, since microblogs on which users post real time messages shows millions of opinions about their attitudes or sentiment towards hot topics and current issues. Recently, I decided to learn how Regional sentiment analysis can help people to make specific decisions or policy strategies for different regions. Notably, Tweets scraped from Twitter can provide tremendous real-time data for our analysis. In my approach, I develop a Twitter Sentiment Classifier, which will classify a scraped tweet into three main polarities: Positive, Negative and Neutral. To make our analysis more straightforward and clear, I will only extract certain data fields related with one tweet.