Emotional State Of User Research Articles

Construction of Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Psychological education is beneficial in maintaining the psychological health of college students, resolving psychological issues, and creating a harmonious campus and society. Mental health education for college students is a development-oriented basic education activity that contributes significantly to educational quality. To improve the accuracy of college students' mental health assessments, a model for mental health education for college students based on fine-grained parallel computing programming is proposed. This study uses a deep learning algorithm to subdivide the classification of an emotion dictionary, which can be classified by adding negative word lists, polarity conversion dictionaries, and online dictionaries, among other things, based on the theory of ecological instantaneous evaluation. It can be used for both multiclass and detailed emotion analysis. The model is more accurate in assessing the mental health of college students, according to the results of the study. The current emotional state of users can be identified, as well as signs of psychological risk, using emotional analysis of Weibo data, which will become a valuable resource for users seeking clinical psychological consultation in the future.

LEXICAL AND STYLISTIC MEANS OF EXPRESSION OF EMOTIONS IN MODERN INTERNET COMMUNICATION

The objective of the article is to find out the specifics of using multilevel means of communication to indicate the emotional states of users in English-language social networks. Methods. The main scientific results were obtained using a set of general scientific and special research methods, namely: analysis, generalization and systematization of scientific and educational literature on literary studies, psychology, and linguistics; theoretical generalization, analysis and synthesis, as well as comparative, descriptive and analytical. Results. The authors note that the features of virtual communication are: dialogic, emotional, special authorial nature, the ratio of categories reader-author or speaker-listener, removal of restrictions in time and space, the status of participants is usually equal, the general picture of the world, unlimited choice of language. Emotionality is a linguistic characteristic of the text that can cause an emotional effect. It is the result of the transmission of human emotions through language. Accordingly, any verbal means of expression either convey neutral emotions, or positively or negatively colored. The study found that emotions could be expressed at any level of language. If the sentence has a neutral emotional tone, it can be enhanced by the use of lexical items with a positive meaning. The best indicator of positive emotions in statements is the words. Investigated that common language means of expressing positive emotions are tokens. Among the vocabulary that expresses positive emotions, a special place is occupied by exclamations. The study of utterances found that all exclamations were used in sentences with a positive emotional tone. Positive emotions are also reflected using vocabulary, the meaning of which has a pleasant effect on the audience. At the stylistic level, the authors of Internet texts used anaphora, epiphora, antithesis, gradation, alliteration, etc. Grammatical and syntactic means such as adverbs, exclamatory sentences are often used to express positive emotions in Internet discourse. Speaking of the negative tone of emotionality, it should be noted that the linguistic means of expression of negative emotions are often lexical items. The most common indicator is obscene language, which helps to express the feelings of the speaker. The syntactic level is important for expressing negative emotions. Authors often use the second or third type of conditional sentences to convey feelings of sadness, emphasizing the unreality of the situation and the inability to change circumstances.

A Hybrid Time-Distributed Deep Neural Architecture for Speech Emotion Recognition.

In recent years, speech emotion recognition (SER) has emerged as one of the most active human-machine interaction research areas. Innovative electronic devices, services and applications are increasingly aiming to check the user emotional state either to issue alerts under some predefined conditions or to adapt the system responses to the user emotions. Voice expression is a very rich and noninvasive source of information for emotion assessment. This paper presents a novel SER approach based on that is a hybrid of a time-distributed convolutional neural network (TD-CNN) and a long short-term memory (LSTM) network. Mel-frequency log-power spectrograms (MFLPSs) extracted from audio recordings are parsed by a sliding window that selects the input for the TD-CNN. The TD-CNN transforms the input image data into a sequence of high-level features that are feed to the LSTM, which carries out the overall signal interpretation. In order to reduce overfitting, the MFLPS representation allows innovative image data augmentation techniques that have no immediate equivalent on the original audio signal. Validation of the proposed hybrid architecture achieves an average recognition accuracy of 73.98% on the most widely and hardest publicly distributed database for SER benchmarking. A permutation test confirms that this result is significantly different from random classification ([Formula: see text]). The proposed architecture outperforms state-of-the-art deep learning models as well as conventional machine learning techniques evaluated on the same database trying to identify the same number of emotions.

The emotional influence of different geometries in virtual spaces: A neurocognitive examination

In this paper, a multidisciplinary approach to examining the connection between visual perception, human emotions and architectural space is presented. It details a study in which emotional reactions to architectural space geometry are empirically measured and quantified. Using various sensors, including EEG (Electroencephalography), GSR (Galvanic Skin Response), and eye-tracking (ET), we collected data from 112 individuals experiencing virtual environments (VEs), characterized by a variance of geometric manipulations. Diffusion map algorithms, as well as other statistical methods were used to analyze the data. Findings suggest that criteria of protrusion, curvature, scale and proportion of space influence the user's emotional state. Indices of ET, GSR, electrical brain activity, as well as dwelling duration and self-report liking ranks, show both “negative” and “positive” interest changes. Impact statementThis research examined the connection between the properties of space and human emotions by means of empiric measurements of emotional affect, generated by changes in the geometry of space, related to the criteria of protrusion, curvature, scale and proportion. Results demonstrated that changing the geometric properties of architectural space is significantly associated with a significant effect on human emotions. This indicates that emotional responses generated by architectural spaces can be empirically measured and quantified. Implications for residential, education, and rehabilitation settings are evidenced.

Identification of cyberbullying by neural network methods

The authors consider in detail the identification of cyberbullying, which is carried out by fraudsters with the illegal use of the victim's personal data. Basically, the source of this information is social networks, e-mails. The use of social networks in society is growing exponentially on a daily basis. The use of social networks, in addition to numerous advantages, also has a negative character, namely, users face numerous cyber threats. Such threats include the use of personal data for criminal purposes, cyberbullying, cybercrime, phishing and cyberbullying. In this article, we will focus on the task of identifying trolls. Identifying trolls on social networks is a difficult task because they are dynamic in nature and are collected in several billion records. One of the possible solutions to identify trolls is the use of machine learning algorithms. The main contribution of the authors to the study of the topic is the use of the method of identifying trolls in social networks, which is based on the analysis of the emotional state of network users and behavioral activity. In this article, in order to identify trolls, users are grouped together, this association is carried out by identifying a similar way of communication. The distribution of users is carried out automatically through the use of a special type of neural networks, namely self-organizing Kohonen maps. The group number is also determined automatically. To determine the characteristics of users, on the basis of which the distribution into groups takes place, the number of comments, the average length of the comment and the indicator responsible for the emotional state of the user are used.

Face Emotion Recognition based Recommendation System

Face recognition technology has gotten a lot of press because of its wide range of applications and market potential. It is used in a variety of fields, including surveillance systems, digital video editing, and other technical advancements. In the fields of tourism, music, video, and film, these systems have overcome the burden of irrelevant knowledge by taking into account user desires and emotional states. Advice systems, emotion recognition, and machine learning are proposed as thematic categories in the analysis. Our vision is to develop a method for recommending new content that is based on the emotional reactions of the viewers. Music is a form of art that is thought to have a stronger connection to a person's emotions. It has the unique potential to boost one's mood, and video streaming services are becoming more prevalent in people's lives, necessitating the development of better video recommendation systems that respond to their users in a customised manner. Furthermore, many users will believe that travel would be a method to help them cope with their ongoing emotions. Our project aims to create a smart travel recommendation system based on the user's emotional state. This project focuses on developing an efficient music, video, movie, and tourism recommendation system that uses Facial Recognition techniques to assess the emotion of users. The system's overall concept is to identify facial expression and provide music, video, and movie recommendations based on the user's mood.

Eye-Tracker Study of Influence of Affective Disruptive Content on User's Visual Attention and Emotional State.

When reading interesting content or searching for information on a website, the appearance of a pop-up advertisement in the middle of the screen is perceived as irritating by a recipient. Interrupted cognitive processes are considered unwanted by the user but desired by advertising providers. Diverting visual attention away from the main content is intended to focus the user on the appeared disruptive content. Is the attempt to reach the user by any means justified? In this study, we examined the impact of pop-up emotional content on user reactions. For this purpose, a cognitive experiment was designed where a text-reading task was interrupted by two types of affective pictures: positive and negative ones. To measure the changes in user reactions, an eye-tracker (for analysis of eye movements and changes in gaze points) and an iMotion Platform (for analysis of face muscles’ movements) were used. The results confirm the impact of the type of emotional content on users’ reactions during cognitive process interruptions and indicate that the negative impact of cognitive process interruptions on the user can be reduced. The negative content evoked lower cognitive load, narrower visual attention, and lower irritation compared to positive content. These results offer insight on how to provide more efficient Internet advertising.

A review on music recommendations based on facial expression

Deciding which music to listen to from the huge collection of existing options is often confusing. Depending on the user's mood, multiple suggestion frames are available for topics such as music, food, and shopping. The main purpose of this music recommendation system is to provide users with suggestions based on users' tastes. By analysing the user's facial expressions and emotions, it is possible to understand the user's current emotional and psychological state. Music and video are areas where there is a great opportunity to offer customers a wide range of choices, taking into account customer preferences and recorded information. It is well known that people use facial expressions to more clearly expresses what they want to say and the context of the words. More than 60% of the users believe that the song library has too many songs at any given time to find the one that needs to be played. Developing a recommendation system could help users decide which music to listen to and reduce stress levels. Users do not have to waste time searching and searching for songs; it will recognize the track that best fits the user's mood and present the songs to the user according to the user's mood. Music plays a role in emotions, which in turn affects mood. Books, movies, and Television (TV) shows are a few more means, but unlike these, music conveys its message in pure moments. It can help us when people feel low. When people listen to sad songs, their mood tends to drop. When they listen to happy songs, it makes them feel happier. This music recommendation model will mainly work to improve the mood of the user by providing a detection track for the user's facial expression and recommending the preferred song according to the user's expression. User images are captured using webcams. A user's picture is taken, and depending on the user's mood or feeling, appropriate songs are displayed from the user's playlist to meet the user's requirements.

Adapting Software with Affective Computing: A Systematic Review

Strategies aimed at keeping the user's interest in using computer applications are being studied to provide greater user engagement, and can influence how people interact with computers. One of the approaches that can promote user engagement is Affective Computing (AC), based on the premise of recognizing the user's emotional state and adjusting the computer application to respond to such state in real-time. Although it is a relatively new area, over the past few years many research works have investigated the use of AC in various activities and objectives. To provide an overview on the use of AC in computer applications, this article presents a systematic literature review based on available articles on the main scientific databases of the Computer Science area. The main contribution of this review is the analysis of different types of applications. Based on the 58 articles analyzed, the main emotion recognition techniques and approaches to the adaptation of computer applications, as well as the limitations and challenges to be overcome were compiled. Our conclusions present the limitations and challenges still to be overcome in the area of automatic adaptation of computer applications by means of AC.

A neurocognitive study of the emotional impact of geometrical criteria of architectural space

The paper presents a new approach to examining the connection between human emotions and architectural space, focusing on the aspect of geometry. It examines how emotional reactions to geometrical manipulations within architectural space can be empirically measured and quantified. By using physiological sensors, such as EEG (Electroencephalography), GSR (Galvanic Skin Response), and eye-tracking (ET), we were able to collect data from participants experiencing virtual environments, differentiated by criteria of scale, proportion, protrusion and curvature. Findings suggest that these criteria influence the user's emotional state. The developed methodology, which combines both qualitative and quantitative measurements, shows changes of interest, both ‘positive’ and ‘negative’, suspected to indicate different emotional states.

Presence Effects in Virtual Reality Based on User Characteristics: Attention, Enjoyment, and Memory

Presence refers to the emotional state of users where their motivation for thinking and acting arises based on the perception of the entities in a virtual world. The immersion level of users can vary when they interact with different media content, which may result in different levels of presence especially in a virtual reality (VR) environment. This study investigates how user characteristics, such as gender, immersion level, and emotional valence on VR, are related to the three elements of presence effects (attention, enjoyment, and memory). A VR story was created and used as an immersive stimulus in an experiment, which was presented through a head-mounted display (HMD) equipped with an eye tracker that collected the participants’ eye gaze data during the experiment. A total of 53 university students (26 females, 27 males), with an age range from 20 to 29 years old (mean 23.8), participated in the experiment. A set of pre- and post-questionnaires were used as a subjective measure to support the evidence of relationships among the presence effects and user characteristics. The results showed that user characteristics, such as gender, immersion level, and emotional valence, affected their level of presence, however, there is no evidence that attention is associated with enjoyment or memory.

Recommender System for Sentiment Analysis using Machine Learning Models

In recent years, with the rapid growth of Internet technology, online shopping has become a rapid way for users to purchase and consume desired products. Large volume of user-generated content on social media sites like twitter has resulted in tweet sentiment analysis. Sentiment analysis supports as base for decision support systems and recommendation systems and it becomes an essential tool on online platforms to extract the information on user emotional state to improve user satisfaction. This paper proposes an effective sentiment analysis recommender system framework using machine learning models.

A Novel User Emotional Interaction Design Model Using Long and Short-Term Memory Networks and Deep Learning.

Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient (MFCC) of the speech signal is used as the input of the improved long and short-term memory network (ILSTM). To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed.

Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering.

Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

Process Modelling and Creating Predictive Models of Sensory Networks Using Fuzzy Petri Nets

The current period is evidence of the massive replacement of human power with the results of the mental cognitive work of researchers. This mainly concerns the area of industrial production, where the aim is to apply new technologies on the principle of artificial intelligence, using the possibilities of a new direction in the use of a technology called the Internet of Things (IoT). With this idea, however, the question of the user disappears, who in this chaos of massive information can get into a state, where his activities will no longer be manageable. To cope with this situation, it is necessary to examine the impact of these technologies on the quality of life in the work and home environment. This paper investigates process modelling and the creation of predictive models to obtain the emotional state of users. The created predictive models will later allow the simulation of complex sensory systems, which will be able to predict the emotional states of users based on their physiological states. The aim is to also find connections between physiological states and basic emotions to be able to classify user emotions with the highest possible accuracy in the classification process.

The method of real-time acoustic measurement of dynamical changes in the speaker’s emotional state

In this paper we consider the issues in implementations of interactive voice response systems with remote access. Their efficiency can be improved by automatically analyzing changes in the user's emotional state during the dialogue. In order to measure the indicator of the dynamics of the emotional statein real time, it is proposed to use the effect of sound (phonetic) variability of the user's speech at short intervals (fractions of a minute). The novel method of acoustic measurements in conditions of small samples has been developed based on information-theoretic approach by using a scale-invariant gain-optimized dissimilarity measure of the speech signals in the frequency domain. An example of its practical implementation in soft real time is considered. It is shown that the delay in obtaining the measurement results does not exceed in this case 10–20 sec. The experimental results confirmed the high speed of the proposed method and its sensitivity to changes in the emotional state under the influence of external noise. The proposed method can be used for automated quality control of voice samples of users in unified biometric systems, as well as to improve safety by non-contact identification of potentially dangerous persons with short-term psycho-emotional disorders.

Simultaneous prediction of valence / arousal and emotion categories and its application in an HRC scenario

We address the problem of facial expression analysis. The proposed approach predicts both basic emotion and valence/arousal values as a continuous measure for the emotional state. Experimental results including cross-database evaluation on the AffectNet, Aff-Wild, and AFEW dataset shows that our approach predicts emotion categories and valence/arousal values with high accuracies and that the simultaneous learning of discrete categories and continuous values improves the prediction of both. In addition, we use our approach to measure the emotional states of users in an Human-Robot-Collaboration scenario (HRC), show how these emotional states are affected by multiple difficulties that arise for the test subjects, and examine how different feedback mechanisms counteract negative emotions users experience while interacting with a robot system.

Emotional State Recognition Performance Improvement on a Handwriting and Drawing Task

In this work we combine time, spectral and cepstral features of the signal captured in a tablet to characterize depression, anxiety, and stress emotional state recognition on the EMOTHAW database. EMOTHAW contains the emotional states of users represented by capturing signals from sensors on the tablet and pen when the user is performing 3 specific handwriting and 4 drawing tasks, which had been categorized into depressed, anxious, stressed, and typical, according to the Depression, Anxiety and Stress Scale (DASS). Each user was characterized with six time-domain features, and the number of spectral-domain and cepstral-domain features for the horizontal and vertical displacement of the pen, the pressure on the paper, and the time spent on-air and off-air, depended on the configuration of the filterbank. As next step, we select the best features using the Fast Correlation-Based Filtering method. Since our dataset has 129 users, then as next step, we augmented the training data by randomly selecting a percentage of the training data and adding a small random Gaussian noise to the extracted features. We then train a radial basis SVM model using the Leave-One-Out (LOO) methodology. The experimental results show an average accuracy classification improvement ranging of 15%, and an accuracy classification improvement ranging from 4% to 34% compared with baseline (state of the art) for specific emotions such as depression, anxiety, stress, and typical emotional states.

Recognizing emotional state of user based on learning method and conceptual memories

With the increased use of computers, electronic devices and human interaction with computer in the broad spectrum of human life, the role of controlling emotions and increasing positive emotional states becomes more prominent. If a user's negative emotions increase, his/her efficiency will decrease greatly as well. Research has shown that colors are to be considered as one of the most influential basic functions in sight, identification, interpretation, perception and senses. It can be said that colors have impact on individuals' emotional states and can change them. In this paper, by learning the reactions of users with different personality types against each color, communication between the user's emotional states and personality and colors were modeled for the variable "emotional control". For the sake of learning, we used a memory-based system with the user’s interface color changing in accordance with the positive and negative experiences of users with different personalities. The end result of comparison of the testing methods demonstrated the superiority of memory-based learning in all three parameters of emotional control, enhancement of positive emotional states and reduction of negative emotional states. Moreover, the accuracy of memory- based learning method was almost 70 percent.

Classification of auditory attention focuses during speech perception.

Passive brain-computer interfaces (BCIs) covertly decode the cognitive and emotional states of users by using neurophysiological signals. An important issue for passive BCIs is to monitor the attentional state of the brain. Previous studies mainly focus on the classification of attention levels, i.e. high vs. low levels, but few has investigated the classification of attention focuses during speech perception. In this paper, we tried to use electroencephalography (EEG) to recognize the subject's attention focuses on either call sign or number when listening to a short sentence. Fifteen subjects participated in this study, and they were required to focus on either call sign or number for each listening task. A new algorithm was proposed to classify the EEG patterns of different attention focuses, which combined common spatial pattern (CSP), short-time Fourier transformation (STFT) and discriminative canonical pattern matching (DCPM). As a result, the accuracy reached an average of 78.38% with a peak of 93.93% for single trial classification. The results of this study demonstrate the proposed algorithm is effective to classify the auditory attention focuses during speech perception.