In the run-up to the election last year, Ben Heubl from The Economist used the Emotion API to chart the emotions portrayed by the candidates during the debates (note: auto-play video in that link). In his walkthrough of the implementation, Ben used Python to process the video files, and R to create the charts from the sentiment scores generated by the API. Now, the learn dplyr blog has recreated the analysis using R. A detailed walkthrough steps through the process of creating a free Emotion API key, submitting a video to the API using the httr package, and retrieving the emotion scores as an R data frame. For the complete details, including the R code used to interface with the Emotion API, follow the link below.
Lightwave, the groundbreaking bioanalytics company, has developed innovative solutions that enable brands to measure emotion and use that insight as a metric of success. Every event, brand activation, gala, or launch can make customers feel something. But how do you measure and visualize emotion? Learn how Lightwave uses human traits like heart rate and facial reactions to measure emotion at TIDE--the creative conference June 5 at the Park MGM in Las Vegas--where Rana June, C.E.O. of Lightwave, is a keynote speaker. June is one of a dozen innovative thinkers you'll hear from at TIDE--experts who have created experiences for Uber, Facebook, Nike, Pepsi, Unilever, 20th Century Fox, and many more.
This study reviews research on social emotions in robotics. In robotics, emotions are pursued for a long duration, such as recognition, expression, and computational modeling of the basic mechanism behind them. Research has been promoted according to well-known psychological findings, such as category and dimension theories. Many studies have been based on these basic theories, addressing only basic emotions. However, social emotions, also called higher-level emotions, have been studied in psychology. We believe that these higher-level emotions are worth pursuing in robotics for next-generation social-aware robots. In this review paper, while summarizing the findings of social emotions in psychology and neuroscience, studies on social emotions in robotics at present are surveyed. Thereafter, research directions towards implementation of social emotions in robots are discussed.
Then 13, Takeuchi returned to find cinders where her home had been. Only an iron rice pot survived. The forbidden English dictionary, a gift from her father, was ash. She held a single page, which the wind soon swept away. A second firebombing on March 10 left her with images of running through a maelstrom of debris and smoke, and passing charred bodies--one, a mother who had tried to shield her infant beneath her.
The final step for many artificial intelligence (AI) researchers is the development of a system that can identify human emotion from voice and facial expressions. While some facial scanning technology is available, there is still a long way to go in terms of properly identifying emotional states due to the complexity of nuances in speech as well as facial muscle movement. The University of Science and Technology researchers in Hefei, China, believe that they have made a breakthrough. Their paper, "Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition," expresses how an AI system may be able to recognize human emotion through state-of-the-art accuracy on a popular benchmark. In their published paper, the researchers say, "Automatic emotion recognition (AER) is a challenging task due to the abstract concept and multiple expressions of emotion. Inspired by this cognitive process in human beings, it's natural to simultaneously utilize audio and visual information in AER … The whole pipeline can be completed in a neural network."