Emotion Recognition and Sentiment Analysis Market to Reach $3.8 Billion by 2025

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Significant advances have been made during the past few years in the ability of artificial intelligence (AI) systems to recognize and analyze human emotion and sentiment, owing in large part to accelerated access to data (primarily social media feeds and digital video), cheaper compute power, and evolving deep learning capabilities combined with natural language processing (NLP) and computer vision. According to a new report from Tractica, these trends are beginning to drive growth in the market for sentiment and emotion analysis software. Tractica forecasts that worldwide revenue from sentiment and emotion analysis software will increase from $123 million in 2017 to $3.8 billion by 2025. The market intelligence firm anticipates that this growth will be driven by several key industries including retail, advertising, business services, healthcare, and gaming. According to Tractica's analysis, the top use case categories for sentiment and emotion analysis will be as follows: "A better understanding of human emotion will help AI technology create more empathetic customer and healthcare experiences, drive our cars, enhance teaching methods, and figure out ways to build better products that meet our needs," says principal analyst Mark Beccue.


Affect-Driven Generation of Expressive Musical Performances

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These theories of musical perception and musical understanding are the basis of the computational model of musical knowledge of the system. SaxEx is implemented in Noos (Arcos Plaza 1997; 1996), a reflective object-centered representation language designed to support knowledge modeling of problem solving and learning. In our previous work on SaxEx (Areos, L6pez de M ntaras, Serra 1998) we had not taken into account the possibility of exploiting the affective aspects of music to guide the retrieval step of the CBR process. In this paper, we discuss the introduction of labels of affective nature (such as "calm", "tender", "aggressive", etc.) as a declarative bias in the Identify and Search subtasks of the Retrieval task (see Figure 2). Background In this section, we briefly present some of the elements underlying SaxEx which are necessary to understand the system.


A neural network, connected to a human brain, could mean more advanced prosthetics

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In the future, some researchers hope people who lose the use of limbs will be able to control robotic prostheses using brain-computer interfaces -- like Luke Skywalker did effortlessly in "Star Wars." The problem is that brain signals are tricky to decode, meaning that existing brain-computer interfaces that control robotic limbs are often slow or clumsy. But that could be changing. Last week, a team of doctors and neuroscientists released a paper in the journal Nature Medicine about a brain-computer interface that uses a neural network to decode brain signals into precise movements by a lifelike, mind-controlled robotic arm. The researchers took data from a 27-year-old quadriplegic man who had an array of microelectrodes implanted in his brain, and fed it into a series of neural nets, which are artificial intelligence systems loosely modeled after our brains' circuits that excel at finding patterns in large sets of information.


Affectiva raises 14 million to bring apps, robots emotional intelligence

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Affectiva, a startup developing "emotion recognition technology" that can read people's moods from their facial expressions captured in digital videos, raised 14 million in a Series D round of funding led by Fenox Venture Capital. According to co-founder Rana el Kaliouby, the Waltham, Mass.-based company wants its technology to become the de facto means of adding emotional intelligence and empathy to any interactive product, and the best way for organizations to attain unvarnished insights about customers, patients or constituents. She explained that Affectiva uses computer vision and deep learning technology to analyze facial expressions or non-verbal cues in visual content online, but not the language or conversations in a video. The company's technology ingests digital images--including video in chat applications, live-streamed or recorded videos, or even GIFs--through simple web cams typically. Its system first categorizes then maps the facial expressions to a number of emotional states, like happy, sad, nervous, interested or surprised.


Affectiva raises 14 million to bring apps, robots emotional intelligence

#artificialintelligence

Affectiva, a startup developing "emotion recognition technology" that can read people's moods from their facial expressions captured in digital videos, raised 14 million in a Series D round of funding led by Fenox Venture Capital. According to cofounder Rana el Kaliouby, the Waltham, Mass.-based company, wants its technology to become the de facto means of adding emotional intelligence and empathy to any interactive product, and the best way for organizations to attain unvarnished insights about customers, patients or constituents. She explained that Affectiva uses computer vision and deep learning technology to analyze facial expressions or non-verbal cues in visual content online, but not the content or conversations in a video. The company's technology ingests digital images--including video in chat applications, livestreamed or recorded videos, or even GIFs--through typically the simplest web cams. Its system first categorizes then maps the facial expressions to a number of emotional states, like happy, sad, nervous, interested or surprised.