Emotional Intentions Research Articles

Cognitive computing method based on decoding psychological emotional states

The current artificial intelligence is constrained to passively executing human command control. It cannot perceive, learn, and guide itself. Furthermore, the majority of these systems are unable to comprehend the human psychological cognitive state or emotional intention actively. To enhance the universality and generalization ability of existing emotion recognition methods, a psychological emotion state decoding method combining singular spectrum analysis and entropy measure is designed based on an electroencephalography signal. This method is developed to empower the cognitive computing system with the ability to perceive emotions. The entropy measure is accelerated by the vector dissimilarity criterion. Finally, dynamic sample entropy model learning is used to realize the cognitive calculation of psychological and emotional state recognition. The results demonstrated that the computational efficiency of the proposed decoding algorithm was significantly improved. The average recognition accuracy of positive and negative emotional states reached 82.78±16.22. The best recognition accuracy of the proposed electroencephalography emotion recognition method was 84.64 %. The proposed electroencephalography emotion recognition method demonstrated an accuracy of 64.15 % in recognizing cross-individual positive, neutral, and negative emotional states. The proposed emotion recognition method shows better universality and generalization performance, and can effectively identify people's psychological and emotional states.

EMOKINE: A software package and computational framework for scaling up the creation of highly controlled emotional full-body movement datasets

EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer vision. A computational framework, comprehensive instructions, a pilot dataset, observer ratings, and kinematic feature extraction code are provided to facilitate future dataset creations at scale. In addition, the EMOKINE framework outlines how complex sequences of movements may advance emotion research. Traditionally, often emotional-‘action’-based stimuli are used in such research, like hand-waving or walking motions. Here instead, a pilot dataset is provided with short dance choreographies, repeated several times by a dancer who expressed different emotional intentions at each repetition: anger, contentment, fear, joy, neutrality, and sadness. The dataset was simultaneously filmed professionally, and recorded using XSENS® motion capture technology (17 sensors, 240 frames/second). Thirty-two statistics from 12 kinematic features were extracted offline, for the first time in one single dataset: speed, acceleration, angular speed, angular acceleration, limb contraction, distance to center of mass, quantity of motion, dimensionless jerk (integral), head angle (with regards to vertical axis and to back), and space (convex hull 2D and 3D). Average, median absolute deviation (MAD), and maximum value were computed as applicable. The EMOKINE software is appliable to other motion-capture systems and is openly available on the Zenodo Repository. Releases on GitHub include: (i) the code to extract the 32 statistics, (ii) a rigging plugin for Python for MVNX file-conversion to Blender format (MVNX=output file XSENS® system), and (iii) a Python-script-powered custom software to assist with blurring faces; latter two under GPLv3 licenses.

Intelligent emotion recognition in product design using multimodal physiological signals and machine learning

Identifying emotional responses in products is essential for product design and user research. Traditional methods, such as interviews and surveys, for gathering product experience data are time-consuming and resource-intensive, and often fail to capture users’ genuine emotional intentions. This article introduces an intelligent method for accurately identifying user-product emotions using multimodal physiological signals and machine learning techniques. The study involves designing experiments with 63 representative product images, and collecting various physiological signals (eye movement, electrodermal activity, and pulse). Using the K-means algorithm, we establish efficacy-arousal assessment labels to create a product emotion dataset. Preprocessing methods, including time–frequency analysis and wavelet transformation, ensure high-quality signal analysis data. By extracting temporal, frequency, and time–frequency features from multimodal physiological signals, the Relief algorithm determines the optimal feature combination. Finally, the study evaluates the performance of five machine learning classifiers, with results indicating an 81.31% recognition accuracy for the proposed product emotion recognition framework. This framework proves effective for objectively analyzing user emotions in product experiences.

Modern vocal pedagogy: Investigating a potential curricular framework for training popular music singing teachers

This study explores the content and pertinence of a curricular framework for training popular music (PM) singing teachers. The author conducted three extensive literature review studies over two years via an action research framework to investigate common areas of interest for potential pedagogical strategy. She developed a draft curricular topic framework, incorporating five PM-specific areas: vocal health and hygiene, PM stylistic advice, microphones/audio technology, resonance and breathing technique. Five international PM voice teaching representatives took part in the research as focus group participants and provided feedback on the curriculum content. Data from this feedback were analysed in an iterative cycle process and adjustments suggested. Findings from this study indicate that such a curriculum would be both necessary and timely. Suggestions were made to amend some elements and to place greater emphasis on aesthetic artistry, cultural context and emotional intent.

EMOJIFY

Emoticons have become a ubiquitous part of online communication, but their limitations hinder the expression of nuanced emotions. This project proposes "Emojify," a system that leverages deep learning for real-time emotion recognition through video input. By employing techniques like pre-processing, face detection, and Convolutional Neural Networks (CNNs), Emojify analyzes facial expressions and generates corresponding emojis. This approach goes beyond traditional emoticons, dynamically reflecting emotional intent and enriching virtual communication. The project explores the potential for entertainment applications and user-friendly emoji creation tools with encryption and decryption methods. Emojify holds promise for bridging the emotional gap in virtual interactions, fostering a more expressive and engaging online experience.

Broad-deep network-based fuzzy emotional inference model with personal information for intention understanding in human–robot interaction

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human–robot interaction. It aims to understand students’ intentions in the university teaching scene. Initially, we employ convolution and maximum pooling for feature extraction. Subsequently, we apply the ridge regression algorithm for emotional behavior recognition, which effectively mitigates the impact of complex network structures and slow network updates often associated with deep learning. Moreover, we utilize multivariate analysis of variance to identify the key personal information factors influencing intentions and calculate their influence coefficients. Finally, a fuzzy inference method is employed to gain a comprehensive understanding of intentions. Our experimental results demonstrate the effectiveness of the BDFEI model. When compared to existing models, namely FDNNSA, ResNet-101+GFK, and HCFS, the BDFEI model achieved superior accuracy on the FABO database, surpassing them by 12.21%, 1.89%, and 0.78%, respectively. Furthermore, our self-built database experiments yielded an impressive 82.00% accuracy in intention understanding, confirming the efficacy of our emotional intention inference model.

A broad-deep fusion network-based fuzzy emotional intention inference model for teaching validity evaluation

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students' intentions and evaluate the teaching validity through quantitative analysis in the university teaching scene. Specifically, broad-deep fusion network is carried out for facial emotion and behavior recognition. Then, fuzzy inference with personal information is used to understand the intention. The V-A emotion space is used to quantify students' studying status with originality, by mapping the output of Softmax. Finally, teaching validity is obtained by fuzzy inference. According to the recognition results, the accuracy on bimodal face and body gesture database (FABO) of our proposal is 12.21%, 1.89% and 0.78% higher than those of the sparse autoencoder-based fuzzy deep neural network (FDNNSA), geodesic flow kernel integrated with residual network (ResNet-101+GFK), the hierarchical classification fusion strategy (HCFS), respectively. Through the experiment on the self-built database, the accuracy of intention understanding is 90.48%, and the effectiveness of teaching validity evaluation method is confirmed.

Establishing Teacher-Student Communicative Empathy in a Multimodal Interactive Environment and Skill Enhancement

The multi-modal interactive environment is an important object of research in the field of modern education, and its influence on the communicative empathy of teachers and students gets increasingly obvious these days. However, the complex and dynamic features of communicative empathy of teachers and students are usually overlooked in existing studies, so this paper aims at investigating the teacher-student communicative empathy in a multi-modal interactive environment to fill in the said research gap. At first, the problem of feature fusion of communicative emotions of teachers and students in a multi-modal interactive environment was discussed, and a comprehensive and detailed model was established for measuring the features of these emotions. Then, problems of the recognition of emotional intentions of teachers and students and the establishment of their communicative empathy were studied, and a set of effective measures were proposed for enhancing the said communicative empathy, in the hopes of providing useful theoretical and practical evidences for noticing the importance of teacher-student communicative empathy in teaching and enhancing the ability of the said emotions.

Automatic music emotion classification model for movie soundtrack subtitling based on neuroscientific premises

The ability of music to induce emotions has been arousing a lot of interest in recent years, especially due to the boom in music streaming platforms and the use of automatic music recommenders. Music Emotion Recognition approaches are based on combining multiple audio features extracted from digital audio samples and different machine learning techniques. In these approaches, neuroscience results on musical emotion perception are not considered. The main goal of this research is to facilitate the automatic subtitling of music. The authors approached the problem of automatic musical emotion detection in movie soundtracks considering these characteristics and using scientific musical databases, which have become a reference in neuroscience research. In the experiments, the Constant-Q-Transform spectrograms, the ones that best represent the relationships between musical tones from the point of view of human perception, are combined with Convolutional Neural Networks. Results show an efficient emotion classification model for 2-second musical audio fragments representative of intense basic feelings of happiness, sadness, and fear. Those emotions are the most interesting to be identified in the case of movie music captioning. The quality metrics have demonstrated that the results of the different models differ significantly and show no homogeneity. Finally, these results pave the way for an accessible and automatic captioning of music, which could automatically identify the emotional intent of the different segments of the movie soundtrack.

Modeling intentionality in the human brain.

This paper is focusing on a rather neglected issue that concerns both aspects of philosophy and neurobiology in relation to the concept of intentionality. Intentionality is concerned with the 'directedness' or 'aboutness' of mental phenomena towards an object. Despite the fact that in philosophy both concepts of aboutness and directedness are conceptually identical with intentionality, a careful neuroscientific approach can demonstrate that these two phenomena represent two distinct conceptual and neurobiological aspects of intentionality with complementary functions. We described the interaction between a series of intentionality and pathogenetic psychobiological factors, the corresponding brain topography, and the resulting clinical manifestation and psychopathology. A permanent failure of intentionality dominates in psychosis, which includes an inappropriateness of the intentional object or connection, from the outset, or even from the prodromal phase of the disorder. Affective disorders may result from imprecise interoceptive prediction error signals, due to a confused identification of the intentional object. In suicidal patients there is an emotional intentionality failure, characterized by an absence of intentional object or a loss of conscious access to normal intentional objects. We may model an 'intentional system' as a higher order system, with a monitoring and regulatory role attributed to the brain and behavior. Also, we may consider mental disorders as the result of a radical disruption of intentionality, due to an inappropriateness or lack of the intentional object or due to an inappropriate connection in some points of the suggested brain pathways of intentionality.

Instructed timing and body posture in guitar and bass playing in groove performance

Body movements play a crucial role in music performance and perception, and they do so well beyond those devoted to sound production itself. Various movements related to the performer’s emotional intentions or structural aspects of the music are also part of the performance and crucial to the listening experience. In the present study, we investigated the effect of instructed timing on such non-sound producing body movements, focusing on musicians’ body posture. We used an infrared motion-capture system to record the movements of skilled guitarists and bassists while they were playing electric guitar and electric bass, respectively. We instructed the musicians to perform under three different timing-style conditions: laid-back (behind), on-the-beat, and pushed (ahead). We also conducted short semistructured interviews to gain further insight into their movement strategies. The results show that performers generally leaned forward when instructed to play systematically slightly ahead of the pulse. We suggest that this change is related to an alteration in the performer’s experience of the feel of the music. The results support the view that musicians’ non-sound-producing body movements are not random, but integral to the performance, and that they are closely related to the music’s microrhythmic feel.

LINGUISTIC PECULIARITIES OF MODERN EXPRESSIVE DISCOURSE

In this article we deal with different approaches to the study of notion of expressive discourse and functions of this phenomenon in contemporary linguistics. The article is interesting for presentation of points of view of some linguists on existing types of discourse as well as an attempt to determine the place of expressive discourse among them. In a general sense, discourse is considered by scientists as a written or spoken result of a communicative event. When studying the typology of discourse, scientists in one way or another associate it with expressiveness, an expressive communicative act, an expressive type of speech acts, etc. Expressiveness, which is the main distinguishing feature of expressive discourse, is defined as expressive and depicting qualities of language that make it figurative and emotionally coloured. According to many linguists, expressiveness should be considered in relation to the concept of emotionality. The emotional in language is necessary for expressing feelings, and the expressive is necessary for enhancing expressiveness and imagery. Expressiveness is a broader concept than emotionality and includes emotionality as its most important component. One of the purposes of emotional expression is the realization of the speaker’s emotional intentions. The mechanisms of creating expressiveness and expressive colouring require further research. Expressiveness is usually implemented through the lexical means of language, stylistic devices, as well as with the help of various emotional intentions of the speaker and the corresponding emotional reaction of the recipient.

Fine Grained Analysis of Intention for Social Media Reviews Using Distance Measure and Deep Learning Technique

Intent analysis and classification are performed to identify the expressions of intent in the given text. In this paper, the dataset is classified into emotion classifications by utilizing machine learning model SVM, Bipolar classification, Fine Grained Analysis, and Sarcasm detection, with Naïve Bayes and Random Forest techniques of deep learning, including Long Short-Term Memory to perform intention analysis on social media data. Then Fine-grained or Multi-Class Sentiment analysis is used for further classification of the five classes, viz. negative, strong negative, neutral, positive, and strong positive, which detects the sarcastic reviews in the movie dataset. The emotional intention behind the review comments is classified as happiness, rage, sadness, joy, anger, and disgust by using SVM. The reviews are analyzed and calculated based on their subjectivity and context level similarity using Related Relaxed Word Mover Distance (RRWMD) semantic similarity measure. With the advantage of the RRWMD algorithm, the reviews from the context containing deviated or irrelevant contents were removed before being applied to the classification algorithms, thereby reducing the execution time, which obtains a 3% improvement in accuracy. The disadvantage of the RRWMD algorithm is only one deep learning algorithm is compared. From the observed accuracy scores and classification reports, the LSTM has provided higher accuracy, despite the long execution time. The Naïve Bayes model has produced lower accuracy than the neural network model but was efficient, taking less time to fit and classify. The results from various experiments have proven that the semantic similarity measure provides more accurate results than the state-of-the-art model.

Abstractive Summarization of Text Document in Malayalam Language: Enhancing Attention Model Using POS Tagging Feature

Over the past few years, researchers are showing huge interest in sentiment analysis and summarization of documents. The primary reason being that huge volumes of information are available in textual format, and this data has proven helpful for real-world applications and challenges. The sentiment analysis of a document will help the user comprehend the content’s emotional intent. Abstractive summarization algorithms generate a condensed version of the text, which can then be used to determine the emotion represented in the text using sentiment analysis. Recent research in abstractive summarization concentrates on neural network-based models, rather than conjunctions-based approaches, which might improve the overall efficiency. Neural network models like attention mechanism are tried out to handle complex works with promising results. The proposed work aims to present a novel framework that incorporates the part of speech tagging feature to the word embedding layer, which is then used as the input to the attention mechanism. With POS feature being part of the input layer, this framework is capable of dealing with words containing contextual and morphological information. The relevance of POS tagging here is due to its strong reliance on the language’s syntactic, contextual, and morphological information. The three main elements in the work are pre-processing, POS tagging feature in the embedding phase, and the incorporation of it into the attention mechanism. The word embedding provides the semantic concept about the word, while the POS tags give an idea about how significant the words are in the context of the content, which corresponds to the syntactic information. The proposed work was carried out in Malayalam, one of the prominent Indian languages. A widely used and accepted dataset from the English language was translated to Malayalam for conducting the experiments. The proposed framework gives a ROUGE score of 28, which outperformed the baseline models.

Exploring Spatio-spectral Electroencephalogram Modulations of Imbuing Emotional Intent during Active Piano Playing.

Imbuing emotional intent serves as a crucial modulator of music improvisation during active musical instrument playing. However, most improvisation-related neural endeavors have been gained without considering the emotional context. This study attempted to exploit reproducible spatio-spectral electroencephalogram (EEG) oscillations of emotional intent using a data-driven independent component analysis framework in an ecological multiday piano playing experiment. Through the four-day 32-ch EEG dataset of 10 professional players, we showed that EEG patterns were substantially affected by both intra- and inter-individual variability underlying the emotional intent of the dichotomized valence (positive vs. negative) and arousal (high vs. low) categories. Less than half (3-4) of the 10 participants analogously exhibited day-reproducible (≥ three days) spectral modulations at the right frontal beta in response to the valence contrast as well as the frontal central gamma and the superior parietal alpha to the arousal counterpart. In particular, frontal engagement facilitated a better understanding of the frontal cortex (e.g., dorsolateral prefrontal cortex and anterior cingulate cortex) and its role in intervening emotional processes and expressing spectral signatures that are relatively resistant to natural EEG variability. Such ecologically vivid EEG findings may lead to better understanding of the development of a brain-computer music interface infrastructure capable of guiding the training, performance, and appreciation for emotional improvisatory status or actuating music interaction via emotional context.

Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

SHOPPING EXPERIENCES AND BUYING INTENTIONS OF ONLINE SHOPPERS DURING THE COVID-19 PANDEMIC

The study focused on determining online shoppers' shopping experiences and buying intentions during the Covid-19 pandemic. This also determined the effects of shopping experiences during the Covid-19 pandemic on their buying intentions. It employed a descriptive-correlation research design using a survey questionnaire administered using Google Forms. For this study, the researcher used the purposive sampling technique to consider 150 online shoppers working from home in Tanza, Cavite. Data were tabulated and analyzed using SPSS. Results show that the participants have an excellent online shopping experience during the Covid-19 pandemic in terms of shopping enjoyment, convenience, security, privacy, and a very good experience in terms of personalization quality. Also, their pandemic shopping experiences highly affected their buying intentions regarding physical and emotional intentions, while their rational, product, and patronage intentions were moderately affected. Results also show no significant difference in the profiles of the participants regarding their shopping experiences and buying intentions except for their frequency of buying products online and the products bought online.

Feeling in Values: Axiological and Emotional Intentionality as Living Structure of Ethical Life, Regarding Max Scheler’s Phenomenology

Some of the contemporary ethical debates have put in value the rational feature of feelings because of the estimative intentionality that is implied in them. In this context, some claim that the intentionality of emotions is a kind of value perception, as Phenomenology stressed at the beginning of the twentieth century, particularly Max Scheler, by analysing emotional Feeling [Fühlen] in the frame of emotional life. In order to extend the context of this philosophical debate, and after describing Scheler’s phenomenology of feelings of the intentional and non-intentional emotional life, firstly, we defend theaxiologicalandemotionalintentionalityasdoubleintentionality that—in our understanding—Scheler analyses phenomenologically, while taking distance from Franz Brentano and Edmund Husserl’s intentional structure of estimative emotions. And, secondly, we propose a tentative interpretation of the link between estimative and emotional double intentionality as livingstructureofethicallife, in the light of Martin Heidegger’s Hermeneutical Phenomenology.

Faces and situational Agency

Though there are many challenges to Ekman’s thesis that there are basic emotions with universal corresponding facial expressions, our main criticism revolves around the extent to which grounding situations alter how people read faces. To that end, we recruit testifying experimental studies that show identical faces expressing varying emotions when contextualized differently. Rather than dismissing these as illusions, we start with the position—generally favored by embodied thinkers—that situations are primary: they are where specifiable and hence knowable properties first show up. We further argue that situationally inflected emotional expressions are informationally meaningful. We reject the idea that reading expressions is primarily about ascertaining internal mental states, arguing instead that people are registering overall situations when looking at faces. However, if mind is understood as a situated phenomenon that extends into active ecological frames, then one can still argue that mindreading is going on. Although we do not claim isolated things like cliffs or cars have agency, we speculate networked systems with cliffs, people, cars, bears, etc., collectively function with intentionality, more so if advancing a robust situated mind thesis, contra figures like Dennett who argue that people over-impute mind to things. Our position has practical implications insofar as it casts doubts on recent attempts to develop AI systems that extract emotional intent out of facial expressions since many of these systems are grounded on Ekman’s basic view.

Different stages of emotional prosody processing in healthy ageing-evidence from behavioural responses, ERPs, tDCS, and tRNS.

Past research suggests that the ability to recognise the emotional intent of a speaker decreases as a function of age. Yet, few studies have looked at the underlying cause for this effect in a systematic way. This paper builds on the view that emotional prosody perception is a multi-stage process and explores which step of the recognition processing line is impaired in healthy ageing using time-sensitive event-related brain potentials (ERPs). Results suggest that early processes linked to salience detection as reflected in the P200 component and initial build-up of emotional representation as linked to a subsequent negative ERP component are largely unaffected in healthy ageing. The two groups show, however, emotional prosody recognition differences: older participants recognise emotional intentions of speakers less well than younger participants do. These findings were followed up by two neuro-stimulation studies specifically targeting the inferior frontal cortex to test if recognition improves during active stimulation relative to sham. Overall, results suggests that neither tDCS nor high-frequency tRNS stimulation at 2mA for 30 minutes facilitates emotional prosody recognition rates in healthy older adults.