Emotions are an integral aspect of human mental processes and everyday experience. They drive much of our behaviour and play an important part in communication. Emotions are often intertwined with mood, temperament, personality, disposition, and motivation.
To understand human emotions, to react to them, and to intentionally induce them has been a long-standing dream of researchers in human-computer interaction. How much better could our lives be if computers, search engines, or smart personal assistants would be able to sense when we start getting annoyed or frustrated with them and if they could adapt accordingly?
Interactive Business Webinar about the MixedEmotions Platform
When is the next webinar? We are happy to welcome you at June 30th at 12h CEST. Just access http://joingotomeeting.com, and enter meeting id 248-510-485
MixedEmotions brings emotion analysis to your business. We are developing an open source platform for the automatic detection of emotions in speech, audio, video, text and social media data – in multiple languages and with preservation of the semantic context of expressed emotions. Read More
Can you imagine your alarm clock knew if you slept badly and chose your favorite song to raise your spirits? Or that your TV chose for you that movie you needed today to make you smile? Or that your car suggests you to stop to grab a cup of coffee?
These premises, which until now were part of science fiction cinema, from classics like Blade Runner to more current hits like Her, may be closer to reality. Thanks to the convergence of technologies of analysis of emotions, Big Data, and Internet of Things (IoT). Both are being researched in the European project MixedEmotions. Read More
What’s the challenge? One problem has been that traditional approaches to data investigations have been lacking the ability to handle mixed data effectively. Enterprise systems allow to search across structured and unstructured data, but just to reveal “results” in the form of – for example – blue links. And Business Intelligence systems on the other hand are great for seeing aggregates but lack the ability to search and investigate down to the single last record.
Within the context of the MixedEmotion project we’re interested in investigating the “emotions” in texts. At the core are algorithms, which extract measurements of “Emotional states” from text. The harvested emotional data can then be measured, averaged and acted upon.
MixedEmotions finds and identifies emotions in Big Data. How are we doing this? The first step is to select an emotion classification scheme. Research into emotion has proposed several approaches to classification and characterisation of emotion. So, which one to chose?
Early work on linguistic characterisation of emotion found that emotion could largely be characterised by just three dimensions: primarily affective valence (ranging from positive to negative) and arousal (ranging from calm to excited), with a dimension they labelled “dominance” or “control” having less significance. Much work in emotion analysis has used this VAD (Valence, Arousal, Dominance) model, including the widely used dictionary of emotional significance of words “Affective Norms of English Words” (ANEW).