Complex Human Emotions Research Articles

Algorithmically Enhanced Wearable Multimodal Emotion Sensor

Sensing diverse human emotions offers significant potential for understanding deeper cognitive processes and diagnosing neurological diseases. However, capturing the full spectrum of emotions remains a significant challenge due to their intricate and subjective nature. Traditional emotion‐sensing techniques often focus on singular emotions, limiting their ability to grasp the complexity of emotional experiences. Herein, a fully printed, organic wearable sensor capable of multimodal emotion sensing by noninvasively monitoring physiological indicators such as heart rate, breathing patterns, and voice signatures is presented. The recorded signals are processed using a long short‐term memory (LSTM) neural network, achieving over 91% classification accuracy in distinguishing different emotions through a data fusion approach, showing >9% enhancement in accuracy as compared to feature fusion. As a proof of concept, Q‐learning is implemented with the data to simulate emotional responses in a robotic model. The study's approach provides a pathway to understanding complex human emotions and enhances the capabilities of effective human–machine interaction.

The Novel EfficientNet Architecture-Based System and Algorithm to Predict Complex Human Emotions

Facial expressions are often considered the primary indicators of emotions. However, it is challenging to detect genuine emotions because they can be controlled. Many studies on emotion recognition have been conducted actively in recent years. In this study, we designed a convolutional neural network (CNN) model and proposed an algorithm that combines the analysis of bio-signals with facial expression templates to effectively predict emotional states. We utilized the EfficientNet-B0 architecture for network design and validation, known for achieving maximum performance with minimal parameters. The accuracy for emotion recognition using facial expression images alone was 74%, while the accuracy for emotion recognition combining biological signals reached 88.2%. These results demonstrate that integrating these two types of data leads to significantly improved accuracy. By combining the image and bio-signals captured in facial expressions, our model offers a more comprehensive and accurate understanding of emotional states.

Sentiment Analysis Based on Machine Learning Techniques: A Comprehensive Review

In the landscape of digital communication, sentiment analysis stands out as a pivotal technology for deciphering the vast troves of unstructured text generated online. When integrated with machine learning, sentiment analysis transforms into a powerful tool capable of distilling insights from complex human emotions and opinions expressed across social media, reviews, and forums. This review paper embarks on a thorough exploration of the integration of machine learning techniques with sentiment analysis, shedding light on the latest advancements, challenges, and applications spanning various sectors including public health, finance, and consumer behavior. It meticulously examines the role of machine learning in elevating sentiment analysis through improved accuracy, adaptability, and depth of analysis. Furthermore, the paper discusses the implications of these technologies in understanding consumer sentiment, tracking public health trends, and forecasting market movements. By synthesizing findings from seminal studies and cutting-edge research, this review not only charts the current landscape but also forecasts the trajectory of sentiment analysis. It underscores the necessity for ongoing innovation in machine learning models to keep pace with the evolving digital discourse. The insights presented herein aim to guide future research endeavors, highlight the transformative impact of machine learning on sentiment analysis, and outline the potential for new applications that could benefit society at large.

A Visual–Audio-Based Emotion Recognition System Integrating Dimensional Analysis

Dimensional emotion recognition research is an important branch of affective computing, which uses continuous values to represent complex human emotions. In this study, we propose a visual–audio emotion recognition system that integrated emotional dimensions. For the visual part, the corresponding relationship between emotion category and emotion dimension interval is established based on rules, and the respective classifiers are trained and fused using the machine learning methods. For the audio part, some emotion-related features are extracted, and a 128-D global feature is extracted through a deep convolutional neural network (DCNN). We use a combination of Bayesian and machine learning to integrate the information of visual–audio modalities. We have tested the proposed system and its single modalities on the standard databases CK <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> and eNTERFACE ’05, and the experimental results and comparison showed the efficiency of the proposed system. Furthermore, our proposed system uses emotion category label and dimension values simultaneously to represent emotion, providing strong interpretability and expansibility for emotion recognition, which goes beyond the methods only come with either classification or dimension.

Dominance of Queer Theory in Manju Kapur's A Married Woman

"A Married Woman," by Manju Kapur's novel, is a literary work that intricately weaves the threads of complex human emotions, societal norms, and the exploration of identity and sexuality. This article delves into the dominance of queer theory within Kapur's narrative, showcasing how the author employs this theoretical framework to dissect the lives of her characters. By employing queer theory, Kapur examines how societal constructs impact the lives of her protagonists, Astha and Peeplika, as they navigate the traditional roles and expectations thrust upon them as married women. This article explores how queer theory provides a lens through which to analyze themes of desire, resistance, and agency, ultimately shedding light on the characters' attempts to break free from the confines of heteronormative structures. Kapur's "A Married Woman" emerges as a powerful literary testament to the enduring influence of queer theory in contemporary Indian literature, unravelling the complexities of human relationships and the ongoing struggle for self-discovery and acceptance in a world shaped by convention.

A Critique of Automated Approaches to Code Facial Expressions: What Do Researchers Need to Know?

Facial expression recognition software is becoming more commonly used by affective scientists to measure facial expressions. Although the use of this software has exciting implications, there are persistent and concerning issues regarding the validity and reliability of these programs. In this paper, we highlight three of these issues: biases of the programs against certain skin colors and genders; the common inability of these programs to capture facial expressions made in non-idealized conditions (e.g., "in the wild"); and programs being forced to adopt the underlying assumptions of the specific theory of emotion on which each software is based. We then discuss three directions for the future of affective science in the area of automated facial coding. First, researchers need to be cognizant of exactly how and on which data sets the machine learning algorithms underlying these programs are being trained. In addition, there are several ethical considerations, such as privacy and data storage, surrounding the use of facial expression recognition programs. Finally, researchers should consider collecting additional emotion data, such as body language, and combine these data with facial expression data in order to achieve a more comprehensive picture of complex human emotions. Facial expression recognition programs are an excellent method of collecting facial expression data, but affective scientists should ensure that they recognize the limitations and ethical implications of these programs.

Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support

Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks such as scheduling meetings and grammar-checking text. However, such human–AI collaboration poses challenges for more complex tasks, such as carrying out empathic conversations, due to the difficulties that AI systems face in navigating complex human emotions and the open-ended nature of these tasks. Here we focus on peer-to-peer mental health support, a setting in which empathy is critical for success, and examine how AI can collaborate with humans to facilitate peer empathy during textual, online supportive conversations. We develop HAILEY, an AI-in-the-loop agent that provides just-in-time feedback to help participants who provide support (peer supporters) respond more empathically to those seeking help (support seekers). We evaluate HAILEY in a non-clinical randomized controlled trial with real-world peer supporters on TalkLife (N = 300), a large online peer-to-peer support platform. We show that our human–AI collaboration approach leads to a 19.6% increase in conversational empathy between peers overall. Furthermore, we find a larger, 38.9% increase in empathy within the subsample of peer supporters who self-identify as experiencing difficulty providing support. We systematically analyse the human–AI collaboration patterns and find that peer supporters are able to use the AI feedback both directly and indirectly without becoming overly reliant on AI while reporting improved self-efficacy post-feedback. Our findings demonstrate the potential of feedback-driven, AI-in-the-loop writing systems to empower humans in open-ended, social and high-stakes tasks such as empathic conversations. AI language modelling and generation approaches have developed fast in the last decade, opening promising new directions in human–AI collaboration. An AI-in-the loop conversational system called HAILEY is developed to empower peer supporters in providing empathic responses to mental health support seekers.

An Analysis of Persistent Expressions: Examining the Resonance of Words - A Literature Review

In Julian Barnes' novel "The Sense of an Ending," the author effectively employs the literary technique of repetition to depict a wide range of complex human emotions that elude facile categorization. This provides an additional function, namely, to emphasize the inherent subjectivity and fallibility that are intrinsic to the disciplines of history and memory. Notwithstanding the importance of this literary technique, scholars have yet to fully explore it. The present study aims to address this gap by providing a systematic classification of the literary repetitions identified in the novel and elucidating their role in shaping the narrative's central themes. The study delineates and characterizes three fundamental iterations, namely the reoccurrence of indistinguishable circumstances, the manifestation of sceneries with minor alterations, and the replication of identical lines with varying connotations. The text has various instances of these types of repeats. The fundamental objective of this research is to elucidate the way the motifs of obsession, misunderstanding, and consequences reverberate throughout the narrative, so augmenting the reader's grasp of the central topics of the text.

Emotion Recognition Using Explainable Genetically Optimized Fuzzy ART Ensembles

There is a growing demand for explainability in complex artificial intelligence solutions to support critical applications’ decision-making processes. Barriers to explainable processes include black-box classifiers, such as deep learning, and noisy datasets. Affect recognition involving neural networks attempts to map complex human emotions onto Arousal and Valence scales based on physiological signal measurements. Datasets collected for this purpose are inherently noisy and may contain outliers and imbalanced classes, hindering accurate classification. In our approach, these issues are addressed using Fuzzy ART (FA) for clustering data samples into more condensed memory templates, introducing stochastic resonant noise to amplify signal-to-noise ratio, and SMOTE sampling to generate synthetic minority samples. A genetic algorithm is developed for FA optimization and ensemble model selection. Clusters obtained from the resulting ensembles are then used to train an ensemble of boosted decision trees for classification and to visualize the decision-making processes. Individual features such as heart rate variability and EEG band power, as well as feature interactions between pairs of features, may contain critical information as human affect indicators. Contributions of individual features and feature interactions toward describing human affect are quantified and interpreted using Shapley additive explanation values. Three established affect recognition datasets were considered for mapping physiological features onto a binary classification of Low/High Arousal and Positive/Negative Valence. Our framework was able to achieve good generalization for both classification tasks as well as provide detailed insights into the contributions of physiological features towards describing Arousal and Valence affects.

The Effects of an Equine Assisted Learning Supervision Intervention on Counselors’-in-training Performance Anxiety and Counseling Self- Efficacy

ABSTRACT Counseling deals with a myriad of complex human emotions, and competency takes years of practice and experience. Counselors-in-training (CITs) who do not expect this complexity experience overwhelming performance anxiety. The purpose of this study was to examine the impact of an equine assisted learning supervision (EAL-S) intervention on CITs’ performance anxiety and counseling self-efficacy. The State-Trait Anxiety Inventory-State and Counseling Self-Estimate Inventory measured CITs’ pre-post (N = 20; treatment group, n = 10; control group n = 10) performance anxiety and counseling self-efficacy. Results yielded a significant difference between the treatment and control groups’ counseling self-efficacy but not their performance anxiety. Participants were able to relate their interaction with horses to interactions with clients. Counseling self-efficacy of the treatment group increased ten times more than the control group. This study supports that a one-hour EAL-S intervention may improve CITs’ counseling self-efficacy and improve their tolerance for performance anxiety.

Happiness and Fear

In February 2016, Facebook launched Reactions, an interactive feature expanding the Like button to include five additional emotional responses: Love, Sadness, Anger, Wow, and Haha. In this article, we examine users’ feedback about this new feature and identify important design implications of this significant modification of Facebook's interface. We did this by applying theories of human emotion and emotion-specific influences on cognitive appraisals to conduct a heuristic evaluation of Facebook Reactions and a thematic content analysis of the 3,000 “top” comments posted by Facebook users on the official pre- and post-launch announcements about Reactions. Prior to launch, many users were concerned that the addition of a Dislike button would lead to abuse; thus, they favored the more nuanced design of Reactions. After launch, users were more positive about the feature as many of their misconceptions were clarified through actual use. Overall, we identified several design constraints of this new feature, including users’ inability to express conflicting emotions. We conclude the article by discussing the implications of our findings and the challenges around research and design for sociotechnical systems that involve complex human emotions.

A probability and integrated learning based classification algorithm for high-level human emotion recognition problems

In this paper, a probability and integrated learning (PIL) based classification algorithm is proposed for solving high-level human emotion recognition problems. Firstly, by simulating human thinking mode and construction, a novel topology of integrated learning is proposed to obtain the essential material basis for analyzing the complex human emotions. Secondly, classification algorithm based on PIL is presented to adapt the emotion classification fuzziness caused by the emotional uncertainty, which is realized by calculating the confidence interval of the classification probability. This paper also presented three new analyses methods based on classification probability including the emotional sensitivity, emotional decision preference and emotional tube. Our study expects that the proposed method could be used in the affective computing for video, and may play a reference role in artificial emotion established for robot with a natural and humanized way.

Shakespeare’s Drama in View of the Indian Aesthetic Theory of Rasa

Human feelings have been categorized into several kinds of emotions according to their cultural traditions. Indian aesthetic theory of rasa divides emotional states up into eight rasas common to human beings: Sringara Rasa (pleasure or love), Hasya Rasa (laughter or humor), Karuna Rasa (sorrow or compassion), Raudra Rasa (anger), Vira Rasa (heroism or courage), Bhayanaka Rasa (terror), Bibhatsa Rasa (disgust), and Adbhuta Rasa(wonder or awesome). We could have some experiences like these in Shakespeare’s plays with all the complex human emotions shown in his dramatic world. Rasa theory deals with entire literary process: from the artist through the work of art to the reader or audience. Thus, Shakespeare’s tragedies could be read or watched in view of the theory, so that we could find the similarities and differences between ‘catharsis’ in Shakespeare and ‘santa rasa’ in Indian arts. Santa rasa is enlightenment like silence, peace or bliss: all rasas of eight emotions rise from and fall into santa rasa. As Shakespeare creates and destroys the world of his own dream in his drama, his tragedies go beyond the effect of catharsis and come near santa rasa. When Shakespeare creates dramatic illusions and he himself destroys them, he has at once the subjective and objective vision of the world. This is breaking the spell of our lives full of various kinds of emotions like eight rasas. Through the process of aesthetic experiences in Shakespeare’s drama, we could attain to enlightenment like santa rasa.

That's disgusting: unraveling the mysteries of repulsion

Why do we watch horror movies? What is the best way to persuade someone to quit smoking? Why are we more likely to buy a given item if an attractive person has just touched it? And what on earth is the appeal of competitive eating? In this lively, colorful new book, Rachel Herz answers these questions and more, shedding light on an incredible range of human traits-from food preferences and sexual attraction to moral codes and political ideology-by examining them through the lens of a fascinating subject: disgust. One of the most complex human emotions, disgust is the product of both culture and instinct and so it allows us a unique perspective on the relationship between nature and nurture. A component of fear and prejudice, it also gives us powerful-sometimes disturbing-insights into the fabric of society. Herz draws on the latest psychological studies and neurological research to offer surprising observations about human behavior and biology. For example, we learn that a man's scent matters more than his looks or his income in determining whether or not a given woman will find him attractive, that lust and disgust activate the same area of the brain, and that watching a gory movie triggers your immune system as if you were facing an actual threat. We even learn that washing your hands after thinking about a past misdeed-a la Lady Macbeth-can help you feel less guilty. What makes That's Disgusting so remarkable is Herz's ability to weave these curious findings and compelling facts into a narrative that tackles important questions. What matters more: our brain wiring or our upbringing? Is there such a thing as normal? And how might politicians and marketers use disgust to manipulate us? Combining lucid scientific explanations and fascinating research with a healthy dose of humor, That's Disgusting illuminates issues that are central to our lives: love, hate, fear, empathy, prejudice, humor, and happiness.

Employing hierarchical Bayesian networks in simple and complex emotion topic analysis

Traditional emotion models, when tagging single emotions in documents, often ignore the fact that most documents convey complex human emotions. In this paper, we join emotion analysis with topic models to find complex emotions in documents, as well as the intensity of the emotions, and study how the document emotions vary with topics. Hierarchical Bayesian networks are employed to generate the latent topic variables and emotion variables. On average, our model on single emotion classification outperforms the traditional supervised machine learning models such as SVM and Naive Bayes. The other model on the complex emotion classification also achieves promising results. We thoroughly analyze the impact of vocabulary quality and topic quantity to emotion and intensity prediction in our experiments. The distribution of topics such as Friend and Job are found to be sensitive to the documents’ emotions, which we call emotion topic variation in this paper. This reveals the deeper relationship between topics and emotions.

Autonomous emotion development using incremental modified adaptive neuro-fuzzy inference system

In this paper, we propose an autonomous emotion development system with incremental learning for interacting with human subjects, and autonomously understanding the emotional status of humans. For the understanding of human emotion the proposed system needs human-like visual senses perceiving natural scenes as stimuli. According to the relationship between emotional factors and characteristics of an image, we incorporate the fuzzy concept to extract emotional features using L⁎C⁎H⁎ color and orientation information. Additionally, it can sense inputs that have no analog in human senses-reading brain signals in human subjects. We consider the electroencephalography (EEG) signals which are stimulated by natural stimuli to form the semantic emotional features as well. We develop a novel adaptive neuro-fuzzy inference system (ANFIS) based on an incremental learning algorithm to autonomously develop the capability of understanding complex emotions. The proposed incremental modified ANFIS needs only the newly arrived data to adjust the shape of Gaussian membership functions with full covariance matrix, to generate new membership functions or new rules for labeling emotion according to the characteristics of the new data. Utilizing the developmental process, the proposed system can autonomously develop the mental ability to understand more complex human emotions by mining the characteristics of emotional features and interacting with human subjects.

Disgust: the disease-avoidance emotion and its dysfunctions

This review analyses the accumulating evidence from psychological, psychophysiological, neurobiological and cognitive studies suggesting that the disease-avoidance emotion of disgust is a predominant emotion experienced in a number of psychopathologies. Current evidence suggests that disgust is significantly related to small animal phobias (particularly spider phobia), blood-injection-injury phobia and obsessive-compulsive disorder contamination fears, and these are all disorders that have primary disgust elicitors as a significant component of their psychopathology. Disgust propensity and sensitivity are also significantly associated with measures of a number of other psychopathologies, including eating disorders, sexual dysfunctions, hypochondriasis, height phobia, claustrophobia, separation anxiety, agoraphobia and symptoms of schizophrenia--even though many of these psychopathologies do not share the disease-avoidance functionality that characterizes disgust. There is accumulating evidence that disgust does represent an important vulnerability factor for many of these psychopathologies, but when disgust-relevant psychopathologies do meet the criteria required for clinical diagnosis, they are characterized by significant levels of both disgust and fear/anxiety. Finally, it has been argued that disgust may also facilitate anxiety and distress across a broad range of psychopathologies through its involvement in more complex human emotions such as shame and guilt, and through its effect as a negative affect emotion generating threat-interpretation biases.

From evolutionary roots to a broad spectrum of complex human emotions: Future research perspectives in the field of emotional vocal communication: Reply to comments on “Emotional voices in context: A neurobiological model of multimodal affective information processing”

From evolutionary roots to a broad spectrum of complex human emotions: Future research perspectives in the field of emotional vocal communication: Reply to comments on “Emotional voices in context: A neurobiological model of multimodal affective information processing”

Emotion development system by interacting with human EEG and natural scene understanding

In this paper, we propose a mental development system for understanding the emotional status of humans, and sharing emotions with human subjects. According to the relationship between emotional factors and characteristics of an image, we incorporate the fuzzy concept to extract emotional features using L *C *H * color and orientation information. On the other hand, we also consider the EEG signals which are stimulated by natural stimuli to form the semantic emotional features as well. Emotionally relevant features are firstly clustered into two categories with degrees of belongingness to each cluster to initialize the membership functions of a neuro-fuzzy system. The IF–THEN rules of a neuro-fuzzy system to understand the positive and negative human emotions will be constructed by interacting with human. Then the system attempts to extend the number of understandable emotion. Through the time, the system sub-clusters the emotional features so that the number of membership function of the neuro-fuzzy network will increase to incorporate more complicated human expertise considering more human emotions. Using such a developmental process, the proposed system can develop a mental ability to understand more complex human emotions by mining the characteristics of emotional features and interacting with its environment.

Embracing the wide sky: a tour across the horizons of the human mindEmbracing the wide sky: a tour across the horizons of the human mind Daniel Tammet Hodder &amp; Stoughton 296pp £16.99 9780340961322 0340961325

One OF the key points made by Daniel Tammet is that the amazing abilities of so-called autistic savants are not the outcome of dry mechanical processes, but derive from complex human emotions and imaginative minds. narrowing the gap between ‘normal’ and savant minds is not easy, but the author makes the arguments with wit and style. as he criticises the notion of the brain as ‘a human computer’, he insists: ‘…whilst computers crush numbers, i dance with them’.