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.

At least 11 popular apps are reportedly sharing people's sensitive data with Facebook, even if they don't have an account on the social network. The Wall Street Journal found that apps which can help track personal information such as body weight, menstrual cycles and pregnancy are sending such details to Facebook. The apps that were found to share personal data include Flo Period & Ovulation Tracker, BetterMe: Weight Loss Workouts, Breethe, Realtor.com and Instant Heart Rate: HR Monitor. The report suggests none of these apps had an option for users to prevent them from sharing personal data with Facebook, nor do they necessarily make it clear to people their data is making its way to Facebook's servers. The publication was only able to specifically decipher the types of data that iOS apps send Facebook, but a third-party test determined at least one Android fitness app shares weight and height data too.

Sentiment analysis is a task that may suffer from a lack of data in certain cases, as the datasets are often generated and annotated by humans. In cases where data is inadequate for training discriminative models, generate models may aid training via data augmentation. Generative Adversarial Networks (GANs) are one such model that has advanced the state of the art in several tasks, including as image and text generation. In this paper, I train GAN models on low resource datasets, then use them for the purpose of data augmentation towards improving sentiment classifier generalization. Given the constraints of limited data, I explore various techniques to train the GAN models. I also present an analysis of the quality of generated GAN data as more training data for the GAN is made available. In this analysis, the generated data is evaluated as a test set (against a model trained on real data points) as well as a training set to train classification models. Finally, I also conduct a visual analysis by projecting the generated and the real data into a two-dimensional space using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method.

NLP is a technology through which computers can "understand" and reproduce human language. Although the language processing applications are in the early stage of development that isn't very mature yet, they are definitely one of the most interesting tools that might soon change the online space for good. NLP applications are mostly used in chat-bots and other tools providing virtual customer support. Of course, any interaction with customers is meticulously tracked, analysed and optimised to perform better and better. This itself makes the NLP technology a perfect tool for the sentiment analysis.