NPR's Mary Louise Kelly speaks with New York Times reporter Michael LaForgia about the his story about Facebook sharing user data with several hundred companies.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Facebook gave at least 60 device manufacturers, including Apple, Blackberry, Samsung, Amazon, and Microsoft, access to huge amounts of data about users and their friends, the New York Times reported on Sunday. These companies in some cases received access to information about a user's religion, political views, relationship statuses, and other personal details. The manufacturers also reportedly got access to information on users' friends, even if they tried to prohibit their data from being shared with third parties. Facebook recently landed in hot water when revelations surfaced in March that a third-party quiz app was able to collect information from users and their friends.

Starting with the idea that sentiment analysis models should be able to predict not only positive or negative but also other psychological states of a person, we implement a sentiment analysis model to investigate the relationship between the model and emotional state. We first examine psychological measurements of 64 participants and ask them to write a book report about a story. After that, we train our sentiment analysis model using crawled movie review data. We finally evaluate participants' writings, using the pretrained model as a concept of transfer learning. The result shows that sentiment analysis model performs good at predicting a score, but the score does not have any correlation with human's self-checked sentiment.

Over years, a crucial part of data-gathering behavior has revolved around what other people think. With the constantly growing popularity and availability of opinion-driven resources such as personal blogs and online review sites, new challenges and opportunities are emerging as people have started using advanced technologies to make decisions now. Sentiment analysis or opinion mining, refers to the use of computational linguistics, text analytics and natural language processing to identify and extract information from source materials. Sentiment analysis is considered one of the most popular applications of text analytics. The primary aspect of sentiment analysis includes data analysis on the body of the text for understanding the opinion expressed by it and other key factors comprising modality and mood.

Cambridge Analytica was caught tampering with elections by exploiting Facebook, but chances are that this is the tip of the iceberg, and that many others, including scammers and ID thieves, are also exploiting Facebook and other social networks. One way that they do this is as follows. Also, scammers use dozens if not hundreds of IP addresses to create these numerous fake accounts. They do it by recruiting an army of drone workers paid peanuts, or via a Botnet, or recycled or non-static IP addresses, or proxy servers. The smartest ones might even use computer viruses to create Facebook accounts in the background on your hijacked computer (thus via your IP address), without you being aware of it.

More often than not, the best initial use case for AI won't be the company's biggest problem. Making AI real means going beyond the hype, focusing on what is doable in a defined timeframe, with the budget, resources and data that are available. By doing this, firms often discover a more specific use case than they initially considered. For example, instead of trying to improve prediction of customer demand overall, they start with sentiment analysis on social media to establish a better customer dialogue process. It doesn't matter how big the initial use case is.

We recently announced a new version of Excel Add-in which lets you perform state-of-the-art text analysis capabilities from the comforts of your spreadsheets without writing a single line of code. The add-in has been received very well by users working across different industry verticals like Market Research, Software, Consumer Goods, Education, etc. solving a variety of use-cases. Sentiment analysis has been the most used function of our Excel add-in closely followed by Emotion detection. Many of our users use sentiment analysis in Excel to quickly and accurately analyze the responses of their open-ended surveys, online chatter around their product/service or to analyze product reviews from e-commerce sites. In this blog post, we will discuss how to use the function Sentiment Analysis in Excel Add-in to do text analytics for any type of content.

With text analytics, various burning questions around the'why' and'what' of a piece or group of content can be answered. Examples like social media chatter around brand can create a supremely spiraling impact (remember the post which showed a Kentucky man was violently removed from his United Airlines seat on an overbooked flight? And how it lead to a social media disaster for the airline?). Hence there need to be ways to make sense of the unstructured data from diverse sources.

We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, we aim to infer the latent emotional state of the user. Thus, we focus on predicting the emotion word tags attached by users to their Tumblr posts, treating these as "self-reported emotions." We demonstrate that our multimodal model combining both text and image features outperforms separate models based solely on either images or text. Our model's results are interpretable, automatically yielding sensible word lists associated with emotions. We explore the structure of emotions implied by our model and compare it to what has been posited in the psychology literature, and validate our model on a set of images that have been used in psychology studies. Finally, our work also provides a useful tool for the growing academic study of images - both photographs and memes - on social networks.

Before delving into the nitty gritty of exactly how sentiment analysis works, let's break the concept down into something a little more tangible, shall we. Have you ever wondered what the South African public thought about, let's say, Iceland's football team defeating England in the Euro 2016? Well, that right there my friends, is why sentiment analysis software exists – to make vast quantities of data easily understandable at a glance. Think of it like a snapshot of the emotional response to a given topic. You might be asking yourself, but what about online surveys and polls, isn't that their purpose?