Planet Money tries to make a program that reads Donald Trump's tweets and then trades stocks. The first step is training the program to interpret the tweets using something called sentiment analysis.

CDC has even identified food safety as one of seven "winnable battles"; however, progress to date has been limited. We show that adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent over 9,000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.

Foodborne illness afflicts 48 million people annually in the U.S. alone. Over 128,000 are hospitalized and 3,000 die from the infection. While preventable with proper food safety practices, the traditional restaurant inspection process has limited impact given the predictability and low frequency of inspections, and the dynamic nature of the kitchen environment. Despite this reality, the inspection process has remained largely unchanged for decades. CDC has even identified food safety as one of seven ”winnable battles”; however, progress to date has been limited. In this work, we demonstrate significant improvements in food safety by marrying AI and the standard inspection process. We apply machine learning to Twitter data, develop a system that automatically detects venues likely to pose a public health hazard, and demonstrate its efficacy in the Las Vegas metropolitan area in a double-blind experiment conducted over three months in collaboration with Nevada’s health department. By contrast, previous research in this domain has been limited to indirect correlative validation using only aggregate statistics. We show that adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent over 9,000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.

Representing documents is a crucial component in many NLP tasks, for instance predicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with generally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Derived from the weighted multiple-instance regression (MIR) framework, the model learns decomposable document vectors for each individual category and thus overcomes the representational bottleneck in previous methods due to a fixed-length document vector. During prediction, the estimated relevance or saliency weights explicitly capture the contribution of each sentence to the predicted rating, thus offering an explanation of the rating. Our model achieves state-of-the-art performance on multi-aspect sentiment analysis, improving over several baselines. Moreover, the predicted saliency weights are close to human estimates obtained by crowdsourcing, and increase the performance of lexical and topical features for review segmentation and summarization.

The interplay between AI and Sentiment analysis is also a new area. There are already many synergies between AI and Sentiment analysis because many functions of AI apps need sentiment analysis features. "The common interest areas where Artificial Intelligence (AI) meets sentiment analysis can be viewed from four aspects of the problem and the aspects can be grouped as Object identification, Feature extraction, Orientation classification and Integration. The existing reported solutions or available systems are still far from being perfect or fail to meet the satisfaction level of the end users. The main issue may be that there are many conceptual rules that govern sentiment and there are even more clues (possibly unlimited) that can convey these concepts from realization to verbalization of a human being."

Technology innovations meet greatest success in business when these are entirely'client focussed'. Developments in the retail sector, which is consumer-led, are addressing client demand for more personalised, faster and competitive services. Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which these services are offered. In particular Financial Organisations are creating and leveraging such innovation in the domain of wealth management. This trend is now being taken on board by multiple innovators: academia, start-ups, technology companies and financial market participants.

Political speech and the freedom to engage in political activity without being subjected to undue government scrutiny are at the heart of the First Amendment,

The Microsoft Azure ML team recently announced the availability of 3 ML templates on the Azure ML Studio – for online fraud detection, retail forecasting and text classification. These templates demonstrate industry best practices and common building blocks used in an ML solution for a specific domain, starting from data preparation, data processing, feature engineering, model training to model deployment (as a web service) . The goal for Azure ML templates is to make data scientists more productive and faster in building and deploying their custom ML solutions on the cloud. Templates include a collection of pre-configured Azure ML modules as well as custom R scripts in the Execute R Script modules to enable an end-to-end solution. We'll walk through these templates in detail in this and future webinars.

Today in class, I taught some fundamentals of API consumption in R. As it was aligned to some Microsoft content, we first used HaveIBeenPwned.com's API and then played with Microsoft Cognitive Services' Text Analytics API. This brief post overviews what you need to get started, and how you can chain consecutive calls to these APIs in order to perform multi-lingual sentiment analysis. Get the key by signing up for free on the Cognitive Services site for the Text Analytics API. Make sure to verify your email address!

This article was contributed by Mark Edmondson and Peter Meyer, both from IIH Nordic, specialists in online marketing and web communication headquartered in Copenhagen, Denmark. Many analytics specialists agree that Machine Learning is going to revolutionise the digital analytics industry in the future, as all the major vendors battle to provide Machine Learning APIs that offer to surface interesting features of your data. These APIs offer cloud solutions that you can use to both scale up your own models, or take advantage of pre-trained models. Following our presentation at Superweek Hungary 2017, we wanted to show how you could start using these services today to enhance your own digital analytics capabilities. Among all Machine Learning techniques, we chose Sentiment Analysis since it is a common use case, but the same code with small modifications could be used for any of the machine learning APIs offered by the services we chose, Algorithmia and Google Natural Language API.