Emotions are a subject of intense debate in various disciplines. Despite the proliferation of theories and definitions, there is still no consensus on what emotions are, and how to model the different concepts involved when we talk about -- or categorize -- them. In this paper, we propose an OWL frame-based ontology of emotions: the Emotion Frames Ontology (EFO). EFO treats emotions as semantic frames, with a set of semantic roles that capture the different aspects of emotional experience. EFO follows pattern-based ontology design, and is aligned to the DOLCE foundational ontology. EFO is used to model multiple emotion theories, which can be cross-linked as modules in an Emotion Ontology Network. In this paper, we exemplify it by modeling Ekman's Basic Emotions (BE) Theory as an EFO-BE module, and demonstrate how to perform automated inferences on the representation of emotion situations. EFO-BE has been evaluated by lexicalizing the BE emotion frames from within the Framester knowledge graph, and implementing a graph-based emotion detector from text. In addition, an EFO integration of multimodal datasets, including emotional speech and emotional face expressions, has been performed to enable further inquiry into crossmodal emotion semantics.

Emotive language is also correlated with depression, as mental health issues affect the emotional state of people. It is empirically We propose a deep architecture for depression detection from established that depressed individuals express more negative social media posts. The proposed architecture builds upon thoughts, emotions and perspectives [9, 10, 11]. BERT to extract language representations from social media posts and combines these representations using an attentive Depression detection from social media can be performed bidirectional GRU network. We incorporate affective information, either at the individual post level or at the user level, given by augmenting the text representations with features extracted a collection of posts by said user. In [12], authors classify from a pretrained emotion classifier. Motivated by psychological depression-related LiveJournal posts, while in [5] authors focus literature we propose to incorporate profanity and on Twitter post classification. In [13], a shared task for CLPsych morality features of posts and words in our architecture using a 2015 is proposed for clinical diagnoses from Twitter posts.

Kate Crawford (pictured), a principal researcher at Microsoft, and author of Atlas of AI (2021), is warning at Nature that the COVID-19 pandemic "is being used as a pretext to push unproven artificial-intelligence tools into workplaces and schools." The software is touted as able to read the "six basic emotions" via analysis of facial expressions: During the pandemic, technology companies have been pitching their emotion-recognition software for monitoring workers and even children remotely. Take, for example, a system named 4 Little Trees. Developed in Hong Kong, the program claims to assess children's emotions while they do classwork. It maps facial features to assign each pupil's emotional state into a category such as happiness, sadness, anger, disgust, surprise and fear. It also gauges'motivation' and forecasts grades.

The first thing to do in any machine learning task is to collect the data. What we need are thousands of images with labeled facial expressions. The public FER dataset [1] is a great starting point with 28,709 labeled images. However, since the resolution of these images is only 48 x 48, it would be nice to also have a dataset with richer features. To do this, we will use the google_images_download python package to query and scrape data from Google Images.

Karthigayan Muthukaruppanof Manipal International University in Selangor, Malaysia, and co-workers have developed a system using a genetic algorithm that gets better and better with each iteration to match irregular ellipse fitting equations to the shape of the human mouth displaying different emotions. They have used photos of individuals from South-East Asia and Japan to train a computer to recognize the six commonly accepted human emotions -- happiness, sadness, fear, angry, disgust, surprise -- and a neutral expression. The upper and lower lip is each analyzed as two separate ellipses by the algorithm. "In recent years, there has been a growing interest in improving all aspects of interaction between humans and computers especially in the area of human emotion recognition by observing facial expression," the team explains. Earlier researchers have developed an understanding that allows emotion to be recreated by manipulating a representation of the human face on a computer screen. Such research is currently informing the development of more realistic animated actors and even the behavior of robots.

First dates can be a nerve-wracking experience, filled with unease and uncertainty over whether or not the other person is interested. But new technology that could help clear up at least some of the mystery surrounding such dates might be on its way. An'emotion detector' has been created which researchers claim will be able to tell whether or not a person finds you attractive almost instantly. An'emotion detector' concept has been designed that can tell if a person finds you attractive on the first date by measuring skin and heart rate responses and a pupil-dilation measure. Researchers at the University of Lancaster were inspired to make the device by a gadget featured in the 1982 sci-fi fantasy film'Blade Runner'.

First dates can be a nerve-wracking experience, filled with unease and uncertainty over whether or not the other person is interested. But new technology that could help clear up at least some of the mystery surrounding such dates might be on its way. An'emotion detector' has been created which researchers claim will be able to tell whether or not a person finds you attractive almost instantly. An'emotion detector' concept has been designed that can tell if a person finds you attractive on the first date by measuring skin and heart rate responses and a pupil-dilation measure. Researchers at the University of Lancaster were inspired to make the device by a gadget featured in the 1982 sci-fi fantasy film'Blade Runner'.

Affective cues are a natural way that humans give feedback to learning systems. My students and I currently use tools of expression recognition to gather data to hone the abilities of our research systems, always with the consent nontechnical users are in the majority, of those involved. However, Sloman's to Aaron Sloman for his their feelings and fears demand not remarks imply that I favor Sloman was one I use the expression emotion recognition even the relatively benign intrusions, of the first in the AI community to only when established as shorthand such as emotional agents that jiggle write about the role of emotion in for the unwieldy but more accurate about on the screen, smiling at you in computing (Sloman and Croucher description "inference of an an annoying and inappropriate fashion, 1981), and I value his insight into theories emotional state from observations of costing you precious time while of emotional and intelligent systems. The Although inappropriate use of affect largely on some details related to computer cannot directly read internal might be the most common affront unknown features of human emotion; thoughts or feelings, and therefore, with this technology, there are also hence, I don't think the review captures there is no "emotion detector" as potentially more serious problems the flavor of the book. It can detect certain expressions (chapter 4.) he does raise interesting points, as well that arise in conjunction with an Sloman writes that in lieu of being as potential misunderstandings, both internal state: pressure profiles of hooked up to emotion-sensing of which I am grateful for the opportunity banging on a mouse, video signals of devices, he would prefer us all to to comment on. What Sloman misses in more. The aphorism "if you detect in the foreseeable future is teacher and pupil." These users tend to not desires. In contexts where humans wake-up call to us: Current forms of understand the limits of the technology; interact with computers naturally and computer-mediated interaction limit they are already so amazed at what socially (Reeves and Nass 1996), we affective communication. For example, the computer computer, "Does it know that I don't might speed up if we seem Sloman's review might seem confusing like it?" At one time, I would have discounted bored, offer an alternate explanation if in places whether or not you've read such remarks, but now that we appear confused, and try to my book. When the athlete rattles off her list of feelings to the public eye, she rattles off not just what she thinks she feels but able to a misunderstanding about what or otherwise. In this flurry of comes from the Latin sentire, the root of modulation, which indeed exist, thoughts and feelings, she anticipates the words sentiment and sensation.) Sentic especially given an incomplete understanding an event and concludes, "The thought modulation, such as voice inflection, of the phenomena.