Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications

By Rafael A. Calvo, S. D’Mello. IEEE Transactions on Affective Computing, 2010

1,781 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3bb177388eebd1440b5748d7bb11cbad3adced0f

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Citations in the corpus

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “Such images can be mined to determine the environmental factors that affect mental health and well-being (e.g., the amount of green space / 11 17 . number of trees in a neighborhood on rapidly expanding area is ambient intelligence, in which sensors are installed on everyday objects and in places…”
Experience-Driven Procedural Content Generation (2011, background): “Attempts at generating game content procedurally have a fairly long history as PCG aspects appear in games such as Rogue (Toy and Wichman, 1980), Diablo (Blizzard Entertainment, 1996) and Elite (Acornsoft, 1984).”
Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals (2018, background): “nition, such as self-report, startle, behavioral responses, and physiological measures [3], [4].”
Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model (2016, methodology): “Sentiment analysis has been useful in the development of online applications for customer reviews and public opinion analysis (Pang and Lee 2008; Calvo and D'Mello 2010; Liu 2012; Feld-man 2013).”
Pedagogical Agents: Back to the Future (2018, unknown): “Affect recognition is the ability to recognize a user’s affective state from a stream of input signals that may include facial expression, heart rate, electrodermal activity, and speech (Calvo and D’Mello 2010; D’Mello and Kory 2015).”
Effects of Dynamic Aspects of Facial Expressions: A Review (2013, methodology): “The first advances have already been made by incorporating dynamic data into the measurement and modeling of facial actions (e.g., Cosker, Krumhuber, & Hilton, 2011; Pantic & Patras, 2006; see also Calvo & D’Mello, 2010; Kappas, 2010).”
Natural language processing in mental health applications using non-clinical texts† (2017, methodology): “The area of aﬀective computing and its literature on emotion detection from texts was also useful, particularly research (e.g. Calvo and D’Mello 2010; Strapparava and Mihalcea 2014) discussed in Section 4.”
Affective Body Expression Perception and Recognition: A Survey (2013, background): “There has been an increased pursuit of affective computing within the last few years in particular, as evidenced by recently published surveys in the field [104], [28], [35], [105].”
The Definition, Assessment, and Mitigation of State Boredom Within Educational Settings: A Comprehensive Review (2012, background): “The use of physiological measures for assessing boredom has also grown in recent years (Calvo and D’Mello 2010; Gross 2007; Koole 2009).”
Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text (2014, background): “Hence, social processes and cultural norms play a significant role in specifying how we express emotions [44].”
Physiological Signals-based Emotion Recognition via High-order Correlation Learning (2019, background): “As Picard pointed out [5], affective computing has three major objectives: first, to enable smart systems to recognize, infer, and interpret human emotions; second, to enable the systems to express emotions like human beings after receiving stimuli (for example, conversation agents, robots); and…”