Emotion recognition

If an automated method achieves the same results as a group of observers it may be considered accurate, even if it does not actually measure what Alex truly feels.

In general, getting to the truth of what emotion is actually present can take some work, can vary depending on the criteria that are selected, and will usually involve maintaining some level of uncertainty.

[4] The accuracy of emotion recognition is usually improved when it combines the analysis of human expressions from multimodal forms such as texts, physiology, audio, or video.

[5] Different emotion types are detected through the integration of information from facial expressions, body movement and gestures, and speech.

[8] Some of the works that have applied an ensemble of knowledge-driven linguistic elements and statistical methods include sentic computing and iFeel, both of which have adopted the concept-level knowledge-based resource SenticNet.

Affectiva, which spun out of MIT, provides artificial intelligence software that makes it more efficient to do tasks previously done manually by people, mainly to gather facial expression and vocal expression information related to specific contexts where viewers have consented to share this information.

[33] Academic research increasingly uses emotion recognition as a method to study social science questions around elections, protests, and democracy.

[34][35][36] However, this research finds that computer vision tools such as Amazon Rekognition are only accurate for happiness and are mostly reliable as 'happy detectors'.

[38] A patent Archived 7 October 2019 at the Wayback Machine filed by Snapchat in 2015 describes a method of extracting data about crowds at public events by performing algorithmic emotion recognition on users' geotagged selfies.

[42] Eyeris is an emotion recognition company that works with embedded system manufacturers including car makers and social robotic companies on integrating its face analytics and emotion recognition software; as well as with video content creators to help them measure the perceived effectiveness of their short and long form video creative.

[43][44] Many products also exist to aggregate information from emotions communicated online, including via "like" button presses and via counts of positive and negative phrases in text and affect recognition is increasingly used in some kinds of games and virtual reality, both for educational purposes and to give players more natural control over their social avatars.

Text data is a favorable research object for emotion recognition when it is free and available everywhere in human life.

[29] ERC can take input data like text, audio, video or a combination form to detect several emotions such as fear, lust, pain, and pleasure.