Investigations into Facebook's data handling keep piling up. The New York Times has learned that federal prosecutors are in the midst of a criminal investigation into the data deals Facebook arranged with tech companies. It's not known when the investigation began or just what the focus is, but a New York grand jury reportedly used subpoenas to obtain records from two or more "prominent makers of smartphones." The deals included heavyweights like Apple, Microsoft and Sony. Facebook acknowledged the investigation to the Times, stating that it was "cooperating with investigators" and was taking probes "seriously."

Facebook is under criminal investigation by federal prosecutors examining its data-sharing deals with other major technology companies, according to the New York Times. A New York grand jury has subpoenaed records from "at least two prominent makers of smartphones and other devices", the Times reported, citing two unnamed sources. The two companies are among more than 150, including Amazon, Apple and Microsoft, that have entered into partnerships with Facebook for access to the personal information of hundreds of millions of its users, according to the report. "We are cooperating with investigators and take those probes seriously," a Facebook spokesman told the Times. "We've provided public testimony, answered questions and pledged that we will continue to do so."

Nationwide has deployed SAS analytics solutions to improve up its customer interactions. SAS uses AI to create an ecosystem where those using its Analytics solution can make better decisions based on trusted data. Furthermore, using the power of AI, nationwide will be able to tap into more effective and tailored customer interactions. More than half of all email enquiries could be resolved by guiding members towards digital channels. Sentiment analysis from the SAS solutions helped Nationwide to detect the member's mood; for instance, unsurprisingly, analysts identified that people's moods worsen as the number of emails rises.

Created playlists 13 videos Play all Machine Learning in RapidMiner - Playlist 11 videos Play all Machine Learning in Python - Playlist 9 videos Play all Python Programming in ArcGIS - Playlist 26 videos Play all Visualizing Social Media Data - Playlist 28 videos Play all Mining Social Media Data - Playlist 4 videos Play all 27 videos Play all Basic Operations in ArcGIS 10.X - Playlist This item has been hidden Popular uploads Play all 6:21 Overlay, Buffer, and Dissolve in ArcGIS - Duration: 6 minutes, 21 seconds. Created playlists 13 videos Play all Machine Learning in RapidMiner - Playlist 11 videos Play all Machine Learning in Python - Playlist 9 videos Play all Python Programming in ArcGIS - Playlist 26 videos Play all Visualizing Social Media Data - Playlist 28 videos Play all Mining Social Media Data - Playlist 4 videos Play all 27 videos Play all Basic Operations in ArcGIS 10.X - Playlist This item has been hidden Spatial Join, Merge, Append and Create Thiessen Polygons in ArcGIS - Duration: 6 minutes, 1 second. ArcGIS Model Tool4: Create a Python Script Tool - Duration: 4 minutes. Use ArcGIS to Create Feature, Georeference and Digitize Image - Duration: 6 minutes, 22 seconds.

The question of whether the human ability to speak is tightly connected with our ability to synchronize to the world around us is a significant one. For example, it's known that preschoolers' proficiency in synchronizing their bodies to a beat predicts their language abilities. But scientists have not examined whether there is a direct link between speech production rhythms--i.e., the coordinated movements of the tongue, lips, and jaw that constitute speech--and the rhythms of the perceived audio signal.

This 3-month course is an intro to data science for beginners. In this video, I'll explain how a popular data science technique called sentiment analysis works using a real-world scenario. We'll play the role of a data scientist working at a startup making a personal healthcare device. Using sentiment analysis, we'll understand how consumers feel about a competitors product. That'll help us make decisions on how to promote our own product, and what feature we can focus on the most.

The key to perform any text mining operation, such as topic detection or sentiment analysis, is to transform words into numbers, sequences of words into sequences of numbers. Once we have numbers, we are back in the well-known game of data analytics, where machine learning algorithms can help us with classifying and clustering.

One of the most important elements for businesses is being in touch with its customer base. It is vital for these firms to know exactly what consumers or clients think of new and established products or services, recent initiatives, and customer service offerings. Sentiment analysis is one way to accomplish this necessary task. Sentiment Analysis is a field of Natural Language Processing (NLP) that builds models that try to identify and classify attributes of the expression e.g.: In a world where we generate 2.5 quintillion bytes of data every day, sentiment analysis has become a key tool for making sense of that data. This has allowed companies to get key insights and automate all kind of processes.

New York's governor, Andrew Cuomo, has ordered two state agencies to investigate a media report that Facebook may be accessing far more personal information than previously known from smartphone users, including health and other sensitive data. The directive to New York's department of state and department of financial services (DFS) came after the Wall Street Journal said testing showed that Facebook collected personal information from other apps on users' smartphones within seconds of them entering it. The WSJ reported that several apps share sensitive user data including weight, blood pressure and ovulation status with Facebook. The report said the company can access data in some cases even when the user is not signed into Facebook or does not have a Facebook account. In a statement, Cuomo called the practice an "outrageous abuse of privacy".

Accordingly, a prime step in text mining applications is to extract interesting patterns and features, from this supply of unstructured data. Feature extraction can be considered as the core of social media mining tasks such as sentiment analysis, event detection, and news recommendation [2]. In the literature, sentiment analysis tends to be used to refer to the task of classifying the polarity of a given piece of text at the document, sentence, feature, or aspect level [23]. There are various applications on a variety of domains which utilize sentiment analysis, in this regard one can mention applying the sentiment analysis for political reviews to estimate the general viewpoint of the parties [43], predicting stock market prices based on sentiment analysis by utilizing the different financial news data [5], and making use of the sentiment analysis to recognize the current medical and psychological status for a community [23]. Machine learning algorithms and statistical learning techniques have been rising in a variety of scientific fields [9, 10]. A number of machine learning techniques have been proposed to perform the task of sentiment analysis. As one of the powerful sub-domains of machine learning in recent years, deep learning models are emerging as a persuasive computational tool, they have affected many research areas and can be traced in many applications. With respect to the deep learning, textual deep representation models attempt to discover and present intricate syntactic and semantic representations of texts, automatically from data without any handmade feature engineering.