User Emotions Research Articles

Music Recommendation Based on Current Mood Using Ai & Ml

Music always had a special connection with our emotions. It’s a way to connect people around the world through pure emotion. Yet, it is extremely difficult to generalize about music and say that everyone will like the same kind of music. The mood-based recommendation is very necessary because it can help people relieve stress and listen to calming music based on their current mood. Its main purpose is to accurately predict the user’s mood, and then play songs according to the user’s choice and current mood. It uses human-computer interaction (HCI) to recognize human emotions from facial images and extracts facial features from the user’s face. Whenever a person wishes to listen to a specific kind of music, he might be ending up hearing some other kind of music that doesn’t match his mood. So, the primary objective of our project is to address the major challenges that are faced by users while trying to listen to any music at random. In this paper, two approaches are proposed to overcome the above–mentioned challenges. The first approach is implementing a questionnaire model. The user will be required to respond to a series of questions in this model, and depending on his response, the mood will be determined, and music will be suggested. While the second approach involves designing a model to identify user emotions and then suggest music depending on the mood detected. However, if the user is not satisfied with the emotion captured, he will also have the choice to return to one of the two previously described models and continue the procedure. Our project aims at developing a user interactive and user–friendly model.

Emotion recognition of social media users based on deep learning.

Issues with sentiment analysis in social media include neglecting the long-distance semantic link of emotional features, failing to capture the feature words with emotional hue effectively, and depending excessively on manual annotation. This research provides a user emotion recognition model to achieve the emotional analysis of microblog public opinion events. Three types of inspiring text, "joy," "anger," and "sadness," are obtained by the data collecting and data preprocessing of micro-blog public opinion event comment text. Then, an algorithm using the linear discriminant analysis (LDA) model, emotion dictionary, and manual annotation is created to extract emotional feature words. The captured motivational text is converted into a word vector using Word2vec. After gathering the long-distance semantic data with bidirectional long short-term memories (BiLSTM) and convolutional neural networks (CNN) extract the text's key characteristics to finish the emotion categorization. The test results demonstrate an average increase in F1 value of 3.66 percent for six machine learning models and an average increase in F1 value of 1.84 percent for seven deep learning models. The suggested model performs better at identifying the emotions of social media users than the current machine learning and deep learning methods.

Towards Efficient Emotion Self-report Collection Using Human-AI Collaboration

Emotion-aware services are increasingly used in different applications such as gaming, mental health tracking, video conferencing, and online tutoring. The core of such services is usually a machine learning model that automatically infers its user's emotions based on different biological indicators (e.g., physiological signals and facial expressions). However, such machine learning models often require a large number of emotion annotations or ground truth labels, which are typically collected as manual self-reports by conducting long-term user studies, commonly known as Experience Sampling Method (ESM). Responding to repetitive ESM probes for self-reports is time-consuming and fatigue-inducing. The burden of repetitive self-report collection leads to users responding arbitrarily or dropping out from the studies, compromising the model performance. To counter this issue, we, in this paper, propose a Human-AI Collaborative Emotion self-report collection framework, HACE, that reduces the self-report collection effort significantly. HACE encompasses an active learner, bootstrapped with a few emotion self-reports (as seed samples), and enables the learner to query for only not-so-confident instances to retrain the learner to predict the emotion self-reports more efficiently. We evaluated the framework in a smartphone keyboard-based emotion self-report collection scenario by performing a 3-week in-the-wild study (N = 32). The evaluation of HACE on this dataset (≈11,000 typing sessions corresponding to more than 200 hours of typing data) demonstrates that it requires 46% fewer self-reports than the baselines to train the emotion self-report detection model and yet outperforms the baselines with an average self-report detection F-score of 85%. These findings demonstrate the possibility of adopting such a human-AI collaborative approach to reduce emotion self-report collection efforts.

Selection Interface for Promoting User Selection Diversity by Presenting Positive/Negative Review Text and Video to Evoke Product Impression and User Emotion

In recent years, unintentional bias in user selection has hindered the growth of information device interfaces and algorithms. To address this problem, it is essential to clarify the factors that influence a user’s selection bias and to design methods that diversify the user selection without bias. This study focused on the positivity and negativity of reviews and videos as factors that influence a user’s selection bias and investigated whether viewing them increases or decreases a user’s selection diversity. Based on the results, we aimed to show effective methods for improving a user’s selection diversity by evoking a user’s product impression through a review presentation and by evoking a user’s emotion through a video presentation. This study drew inspiration from the broaden-and-build theory, which suggests that exposure to positive or negative information can impact an individual’s selection diversity. In this study, we implemented a smartphone application for online product purchase for an experimental task in which individuals repeatedly selected one of five different drinks. Two experiments were conducted to investigate the effect of reviews and videos on user selection. Experiment 1 showed that viewing positive reviews promoted diversity in a user selection and that both positive and negative reviews promoted brand switching (i.e., selecting different products in a consecutive selection). Experiment 2 indicated that watching a negative video potentially decreased a user’s selection diversity. Our study is helpful for understanding the effect of viewing reviews and videos on user selection and for designing interfaces that promote diverse user selection.

Examination of User Emotions and Task Performance in Indoor Space Design Using Mixed-Reality

Given the COVID-19 pandemic and the resulting social distancing measures with inevitable telecommuting, capturing user emotions is essential as it affects both satisfaction and task performance. Therefore, the purpose of this study was to analyze emotions and task performance in terms of dislike and personalized decision-making in indoor spaces. To facilitate experiments with participants, a mixed-reality environment was utilized with the Pleasure, Arousal, Dominance (PAD) test and cognitive tests. The results of the experiment conducted on 30 subjects identified that aroused and discontented emotions dominated in non-preferred spaces, but pleased, important, and autonomous emotions arose in personalized spaces, as determined through sentimental analysis and statistical methods. Although negative emotions were present in the aversion space, attention and execution abilities were high compared to the personalized space, but working memory was low. By conducting stepwise regression analysis, it was found that working in a visually unfavorable space, which caused an increase in controlled or controlling emotions, improved short-term work efficiency. In addition, important emotions did not have a positive effect on any task performance. However, with pleased and contented emotions in a personalized indoor space, long-term work efficiency was increased, as explained by the Yerkes-Dodson law.

Hastags and Digital Mobilization Movements (DMO): Communication Network Analysis on #BubarkanPOLRI vs #Polripresisi Hastags on Twitter Social Media

This study examines the role of hastags (#BubarkanPOLRI) that first appeared during the Buaya vs Cicak incident where the KPK wiretapped case against the Kabareskrim Mabes Polri which was allegedly involved in disbursing funds from Bank Century customers. Hastag (#BubarkanPolri) Reappeared during the riots at the Kanjuruhan Malang stadium as quoted from the @akmalmarhali20 account "Tear gas shooting is one of the causes of dozens of deaths at Kanjuruhan stadium, STOP COMPETITION ON HUMANITARIAN GROUNDS #Jokowi #BubarkanPOLRI" and @chewulandari__ "Please have a tragedy in Malang to be resolved. Don't ask who's wrong anymore, but ask WHO IS LYING! 1 The police if they turn back the data attacking the community is easy, while the 1000 people shout the dakta is useless. Let alone #BubarkanPOLRI #UsutTuntasTragediKanjuruhan". As well as #polripresisi as a reaction that arises from the development of cases that tarnish the good name of the National Police. As quoted from the @PolsekSebbar account "West Sebatik Police personnel appeal to residents to continue to maintain kamtibmas if there is a problem immediately reported to the Police" and @PolsekLbkPakam "Lubuk Pakam Police SPKT service to the community who make Reports of Loss of Important Letters. Served humanistically, comfortably, and free of levies". This study aims to continue the previous research by comparing two different hastags, namely, #BubarkanPOLRI and #polripresisi to see if the difference in hashtags can have an impact on mobilization between the two fences. #Polripresisi able to create mobilization as measured by the number of network structures formed compared to #BubarkanPOLRI that tend to show the emotions of Twitter users about their dissatisfaction with the Polri as a State institution that should be able to protect and protect the community, of course, the goal is for the National Police to immediately fix themselves with all the dark records that already have, the analysis of this study shows how actors use hastags to get support among twitter users for United in upholding justice.

Emotion Based Music Player Using CNN

Abstract: This work presents a music recommendation system that suggests songs based on the user's emotional state. The system uses computer vision techniques to detect the user's emotions through facial expressions, and a suitable song is recommended accordingly. The proposed system automates the traditional manual process of selecting music based on mood, reducing time and effort. The algorithm uses Haar Cascade and CNN algorithms to detect facial expressions and select an appropriate music track. The inbuilt camera reduces system design costs. The system is based on recommender systems, convolutional neural networks, deep learning, image processing, artificial intelligence, and classification.

Machine Learning Based Music Recommendation System Using Facial Expression

Abstract: Music plays a key role in improving your well-being, as it is one of the key sources of entertainment and inspiration to keep you going. Recent studies have shown that people respond very positively to music and that music has a great effect on a person's brain activity. It is often better to listen your favorite music these days. This job focuses on systems that suggest songs to users based on their state of mind. This computer vision system uses components to determine a user's emotion from facial expressions. When an emotion is detected, the system will suggest songs with that emotion, saving users a lot of time manually selecting and playing songs. It enables the migration of computer vision techniques for such systems to automation. To achieve this goal, we use the algorithm to classify human expressions, game play, and music tracks according to their currently detected emotions. Reduces the effort and time required to manually search for songs in the list. Recognize human facial expressions by extracting facial features using Haar Cascade and CNN algorithms.

Exploring the Potential of Plant-Driven Interaction Design

This study aims to explore the potential and impact of using plants as interactive design objects. With increasing attention to nature and the pursuit of more meaningful user experiences, plants, as living and sentient entities, have garnered significant interest in the field of interactive design. Through theoretical analysis, this paper delves into key areas of using plants as interactive design objects. We examine the potential value of plants as user interfaces, including their form, color, texture, and their impact on user emotions and attention. Additionally, we investigate the perceptual abilities of plants and their response to external stimuli, exploring innovative ways to leverage these features for immersive user experiences. In the realm of emotional design, we emphasize the potential role of plants in conveying emotions and creating atmospheres, utilizing attributes such as shape, color, and growth rate to communicate emotional information and influence user mood and affective states. Furthermore, we explore the relationship between plants and sustainable design, exploring how to integrate the ecological functions of plants with interactive design to achieve more sustainable products and systems. Finally, we consider the influence of cultural factors on plant-based interactive design, underscoring the importance of incorporating and respecting diverse cultural elements in the design process. The findings of this study provide a theoretical foundation and practical guidance for using plants as interactive design objects, offering valuable insights for further exploration of the relationship between plants and interactive design.

SENTIMENT ANALYSIS OF TEXT AND EMOJI DATA FOR TWITTER NETWORK

Twitter is a social media platform where users can post, read, and interact with 'tweets'. Third party like corporate organization can take advantage of this huge information by collecting data about their customers' opinions. The use of emoticons on social media and the emotions expressed through them are the subjects of this research paper. The purpose of this paper is to present a model for analyzing emotional responses to real-life Twitter data. The proposed model is based on supervised machine learning algorithms and data on has been collected through crawler “TWEEPY” for empirical analysis. Collected data is pre-processed, pruned and fed into various supervised models. Each tweet is assigned to sentiment based on the user's emotions, positive, negative, or neutral.

Facial Emotion Song Recommender System

Sometimes, it is very difficult for someone to determine whether a person wants to hear a particular music from the vast array of available choices. So, this paper has proposed a new concept of playing music that is based on the emotion of the user. The primary goal of the music recommendation system which is proposed in this paper is to offer customers recommendations that match their tastes. The most current view of the paper involves manually playing the jukebox, using wearable computers, or classifying based on auditory characteristics. Understanding the user's present emotional or mental state may result from analysing the user's facialexpression and emotions. One area is having a great possibility to provide the audience, with a vast variety of options that are based on their preferences and music and video. In this paper, the primary goal is to show a playlist of songs on any particular music application (YouTube/Spotify) based on each person’s mood. Several images of the user are collected at that precise moment using a camera with the user's consent. To determine a person's mood, these photos go through a thorough testing andtraining process. For this, the deep learning technique called CNN is used to categorize various emotions. After this, based on the trained model, the various emotions are categorized and based on this the music playlist is generated.

Using event-related potentials to identify user emotion caused by product color attribute

The accurate quantification of the correlation between product color attributes and user emotions is the key to product color emotional design (PCED). However, the traditional method of emotion quantification is subjective, one-sided, and contingent, which reduces the reliability of the research results. To this end, this paper proposes a method for PCED based on the quantification of electroencephalogram (EEG) physiological indicators. A medical product, namely an infant incubator, is used as the experimental stimulus samples, and “unsafe-safe” is used as the perceptual imager word pair to conduct EEG measurement experiments of the user's emotional state. Two types of data are obtained in the experiment, namely behavioral data and EEG data. Via the analysis of the two types of data, the EEG physiological characteristic indicators (the event-related potentials (ERPs) components) are obtained, which can explain the user’s emotional cognitive mechanism. Finally, the relationship between the user’s emotional state and EEG characteristics under the influence of product color attributes is explored. The conclusions are as follows. (1) The “safety” emotional value of the two-color samples is higher than that of the three-color samples, which indicates that the simpler the color matching, the higher the safety emotion attribute of the samples. (2) Via the study of the three attributes of hue, lightness, and chroma in the assistant colors of the two-color samples, it is found that when the hue attributes of the samples are red, cyan, and blue, the safety emotional value is higher; moreover, the higher the lightness attribute and the chroma attribute, the higher the safety emotion. Research on the two color attribute dimensions of hue harmony and color tone harmony between the auxiliary color of the three-color samples and the embellishment color revealed that the more consistent the hue harmony, the higher the safety emotion, and the more significant the difference in the color tone harmony, the higher the safety emotion. (3) The reaction time data in the behavioral data demonstrate that the participants had the longest average reaction time under neutral emotions. (4) The results of the time–frequency analysis of the EEG data reveal that there are apparent mid-to-early ERPs components between 100 and 300 ms after the appearance of the stimulus samples. The ERPs component analysis results show that the P1 component can reflect the emotional valence to a certain extent; the higher the amplitude of the P1 component, the more pronounced the negative emotions of the participants, and the lower the safety emotional evaluation value of the two-color samples, the higher the amplitude of the P1 component. Moreover, the N2 and P3 components can reflect the degree of emotional arousal to a certain extent, and the increase in their amplitude indicates the more substantial emotion of the participant; furthermore, the correlation between N2 and emotional arousal is higher. The two-color and three-color experiments revealed that the more neutral the emotional state, the lower the amplitudes of N2 and P3. This research is expected to provide a theoretical foundation and experimental data basis for PCED methods based on EEG physiological indicators.

Designing and Evaluating an Emotionally Responsive Virtual Patient Simulation.

Virtual patient (VP) simulations have been widely used for healthcare training, education, and assessment. However, few VP systems have integrated emotion sensing and analyzed how a user's emotions may influence the overall training experience. This article presents a VP that can recognize and respond to 5 human emotions (anger, disgust, fear, joy, and sadness), as well as 2 facial expressions (smiling and eye contact). The VP was developed by combining the capabilities of a facial recognition system, a tone analyzer, a cloud-based artificial intelligence chatbot, and interactive 3-dimensional avatars created in a high-fidelity game engine (Unity). The system was tested with healthcare professionals at Changzhou Traditional Chinese Medicine Hospital. A total of 65 participants (38 females and 27 males) aged between 23 and 57 years (mean = 38.35, SD = 11.48) completed the survey, and 19 participants were interviewed. Most participants perceived that the VP was useful in improving their communication skills, particularly their nonverbal communication skills. They also reported that adding users' affective states as an additional interaction increased engagement of the VP and helped them build connections with the VP. The emotionally responsive VP seemed to be functionally complete and usable. However, some technical limitations need to be addressed before the system's official implementation in real-world clinical practice. Future development will include improving the accuracy of the speech recognition system, using more sophisticated emotion sensing software, and developing a natural user interface.

It’s Not My Fault, But I’m to Blame: The Effect of a Home Robot’s Attribution and Approach Movement on Trust and Emotion of Users

This study investigated the effect of attribution and approach movement of the social robot when the user wrongly perceives an error as the robot’s responsibility. The robot’s responsibility attribution and approach movement strategies for the error recovery were examined in a situation where the robot was functioning normally, but the user misunderstood it as robot’s fault. In the experiment participants were exposed to four different verbal and movement interaction scenarios with a social robot and then responded to a survey concerning emotions and trust. Results showed that people no longer trusted the robot that approached while attributing the responsibility to the user. The implication of this study is that a powerful self-serving bias is aroused when a robot attributes the responsibility to the user, and thus, it negatively impacts user experiences even if the event took place due to the user’s misunderstanding. This study suggests empirical guidance for designing a social robot’s attribution strategies and movement interactions.

Using Gamification To Increase E-Learning Engagement

E-learning is limited from a pedagogical standpoint because it can't emote or captivate students the same way a teacher can. To compensate for the lack of sensation or emotional engagement, an e-learning system must aim to encourage students in other ways. JB Fogg conducted study on the subject of persuasive technology and how to develop tools that affect a user's emotions. He introduces the Fogg's Behavior model, which looks into the circumstances that can lead to a particular behavior. There are numerous uses for this idea in human-computer interaction. Games are a regular aspect of life that delight players while also modeling behavior. We can improve user engagement with an e-learning application and its particular activities by incorporating game mechanics and dynamics into tasks and e-learning processes. Gamification is a term that has many applications in business practices and suggests tried-and-true methods that are comparable to those used in games. Gamification is the process of integrating elements of gaming into non-gaming applications, particularly user-friendly internet and mobile platforms. Furthermore, it seeks to persuade users to engage in the appropriate behaviors in respect to the applications. This description is connected to the terms serious games, serious gaming, playful engagement, and game-based technology. In this section, we'll examine each one in more detail, as well as examine how well they apply to e-learning and the learning process as a whole.

Using Gamification To Increase E-Learning Engagement

E-learning is limited from a pedagogical standpoint because it can't emote or captivate students the same way a teacher can. To compensate for the lack of sensation or emotional engagement, an e-learning system must aim to encourage students in other ways. JB Fogg conducted study on the subject of persuasive technology and how to develop tools that affect a user's emotions. He introduces the Fogg's Behavior model, which looks into the circumstances that can lead to a particular behavior. There are numerous uses for this idea in human-computer interaction. Games are a regular aspect of life that delight players while also modeling behavior. We can improve user engagement with an e-learning application and its particular activities by incorporating game mechanics and dynamics into tasks and e-learning processes. Gamification is a term that has many applications in business practices and suggests tried-and-true methods that are comparable to those used in games. Gamification is the process of integrating elements of gaming into non-gaming applications, particularly user-friendly internet and mobile platforms. Furthermore, it seeks to persuade users to engage in the appropriate behaviors in respect to the applications. This description is connected to the terms serious games, serious gaming, playful engagement, and game-based technology. In this section, we'll examine each one in more detail, as well as examine how well they apply to e-learning and the learning process as a whole.

Studying the Influence of Toxicity and Emotion Features for Stress Detection on Social Media

It is crucial to detect and manage stress as early as possible before it becomes a severe mental and physical health problem. Some authors even introduce stress as a “silent killer” to emphasize the significance of early stress management. Traumatic global events such as COVID-19 have amplified stress throughout online communities and it is quite common to see that social media users often vent about their problems or situations online. The ability to detect a person's stress from their posts on social media platforms like Reddit or Twitter in a timely manner can help early stress management and consequently counters mental health conditions. In order to detect stress from social media posts, we must obtain the characteristics that signal a user's stress. Which motivates us to study how salient features influence stress detection. On social media, text-based methods of communication predominantly overtake verbal forms, which makes these platforms a convenient rich medium with an extensive amount of text content to analyze a user's thoughts and emotions. We present a novel approach that helps improve stress detection on social media textual content with sentiment, emotion, and toxicity features. We design our framework based on multiple Transformer-based state-of-the-art sentiment, emotion, and toxicity analysis tools and models for feature extraction and discuss the stress detection tasks’ interpretability via inspecting multiple dimensions. For the evaluation, we use publicly available and high-quality datasets where the social media posts are real, carefully selected and labeled. Our experiments show the influence of the proposed new feature dimensions on stress detection by comparing the state-of-the-art baselines and suggesting future directions in stress detection on social media. Furthermore, our extensive feature correlation analysis highlights different aspects, such as 1) Positive and Negative sentiment, 2) Joy, Sadness, and Fear emotions, and 3) Obscene and Insult toxicity as governing factors in improving stress detection performance.

A Modular Haptic Agent System with Encountered-Type Active Interaction

Virtual agents are artificial intelligence systems that can interact with users in virtual reality (VR), providing users with companionship and entertainment. Virtual pets have become the most popular virtual agents due to their many benefits. However, haptic interaction with virtual pets involves two challenges: the rapid construction of various haptic proxies, and the design of agent-initiated active interaction. In this paper, we propose a modular haptic agent (MHA) prototype system, enabling the tactile simulation and encountered-type haptic interaction of common virtual pet agents through a modular design method and a haptic mapping method. Meanwhile, the MHA system with haptic interaction is actively initiated by the agents according to the user’s intention, which makes the virtual agents appear more autonomous and provides a better experience of human–agent interaction. Finally, we conduct three user studies to demonstrate that the MHA system has more advantages in terms of realism, interactivity, attraction, and raising user emotions. Overall, MHA is a system that can build multiple companion agents, provide active interaction and has the potential to quickly build diverse haptic agents for an intelligent and comfortable virtual world.

Cyberemotions in the Era of New Media

The introduction of new media has accelerated changes in the generation and delivery of information. Cyberemotions, as a new type of public opinion, can be more contagious and propagate in more diverse ways than traditional public sentiments. The emotions of internet users have a rising influence on the progression of recent significant social events. In order to spark additional discussion on online sentiment modulation and online public opinion tracking, this article presented an overview of cyberemotions definition and key analytical methodologies in existing online sentiment research, as well as a synopsis of major components of cyberemotions.

A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions.

Multimodal emotion recognition has gained much traction in the field of affective computing, human-computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.