User Emotions Research Articles

Multi-Objective Navigation Strategy for Guide Robot Based on Machine Emotion

In recent years, the rapid development of robot technology means more kinds of robots appear in life and they are applied in different fields of society. Service robots are mainly used to provide convenience for human beings. Guide robots are a kind of service robot, which can replace manual instruction and guidance. However, most of the existing studies provide a preset guidance trajectory for the guiding robot, or they let the user choose the next target point for position guidance, which is a lack of intelligence. To solve the above problems, a robot navigation strategy based on machine emotion is proposed. Firstly, the machine emotion of the guide robot is established according to the user’s emotional state and environmental information. Then, the machine emotion and current location information are used to estimate the user’s intention, i.e., the most desired next target point. Finally, the classical indoor path planning method and obstacle avoidance method are employed to calculate a passable path between the target point and the current position. Simulation results show that the proposed strategy can execute different navigation strategies according to user emotion. The navigation strategy proposed in this paper has been tested on Pepper robot and received good feedback from the subjects.

Research on Information Retrieval Effectiveness of University Scientific Researchers Based on Mental Model

The information retrieval behavior of scientific researchers is a behavior that is affected by multiple factors such as cognition, emotion, task, and user type and has its unique cognitive characteristics. In the context of scientific research, this paper conducts research on the information retrieval behavior of scientific researchers and analyzes the three levels of user mental model “user cognition, user behavior, and user emotion.” According to the mental model construction process, define the user segmentation of basic tasks, formulate recruitment plan, set interview scope, analyze data, construct mental model, product adjustment and gap analysis, export structure seven steps, construct the mental model of scientific research workers’ information retrieval, carry out research on the information retrieval efficiency of scientific researchers, describe the user portrait based on the mental model, and put forward strategies to improve the efficiency of user information retrieval.

Evaluating Users' Emotional Experience in Mobile Libraries: An Emotional Model Based on the Pleasure-Arousal-Dominance Emotion Model and the Five Factor Model.

As a part of user experience, user emotion has rarely been studied in mobile libraries. Specifically, with the proposed emotional model in combination with the Pleasure-Arousal-Dominance (PAD) Emotion Model and the Five Factor Model (FFM), we evaluate user emotions on the mobile library’s three IS features (i.e., user interface, interaction quality, and service environment). An experience procedure with three tasks has been designed to collect data. 50 participants were enrolled, and they were asked to fill in questionnaires right after the experience. The correlations among the PAD emotions were examined. Specifically, users have a low perception of pleasure (P), high perception of arousal (A), and low perception of dominance (D). However, these three emotional states were not always significantly correlated with each other. This study extends mobile library research by focusing on users’ emotional experience. Specifically, the detailed PAD emotions have been examined. This study provides a new approach for application developers and managers to evaluate the user experience of an application.

Robotic Presence: The Effects of Anthropomorphism and Robot State on Task Performance and Emotion

Robots are becoming more ubiquitous in our daily lives including in our offices and homes. As such, it is necessary to understand whether and how robotic presence affects us both emotionally and cognitively. In this letter, we investigate the effects of anthropomorphism and robot state of robotic presence on task performance and user emotion. In an online study, 113 participants completed three tasks within a virtual 3D environment in the presence of robots with varying anthropomorphism (non-anthropomorphic vs. anthropomorphic) and robot state (active vs. idle) in addition to alone and with a human. The study findings suggest that having an active moving robot can expedite the task performance especially for easy difficulty levels albeit with some decrease in accuracy, while anthropomorphism affects the dominance axis of affect and has interaction effects with the robot state. The robot state also had as much influence on the perceived anthropomorphism and animacy as the anthropomorphism of the robot. These results provide initial insights on the effects of robotic presence and the impact of the robot state and anthropomorphism.

Using Crowdsourced Big Data to Unravel Urban Green Space Utilization during COVID-19 in Guangzhou, China

Urban green spaces (UGSs) can meet the spiritual and cultural needs of citizens and provide various ecosystem services. In the context of the COVID-19 pandemic, the utilization of UGSs has been affected in various countries worldwide. This study considered 13 UGSs in Guangzhou, China, as examples. It obtained user check-in data by sampling the check-in pages of Sina Weibo locations using a Python-based web crawler program. The study was conducted for 731 days from 1 October 2019 to 30 September 2021, during different phases of the pandemic. Based on automated Chinese corpus recognition technology, statistical results were obtained after periodization and sentiment calculation. The study assessed the pandemic’s impact on the use of UGSs by analyzing the time, frequency, and emotions of residents visiting UGSs. The study concluded that the emotions of UGS users during COVID-19 tended to be positive. They tended to choose UGSs with low expected population density and visited UGSs on weekdays. Additionally, the religious attributes of UGSs also influenced their utilization.

Microblog User Emotion Analysis Method Based on Improved Hierarchical Attention Mechanism and BiLSTM.

The goal of Chinese fine-grained emotion analysis is to identify the target words corresponding to fine-grained elements from sentences and determine the corresponding emotional polarity for the target words. Aiming at the problem that the current Sina Microblog user emotion analysis methods have low accuracy and are difficult to effectively predict and manage, a Sina Microblog user emotion analysis method based on the Bidirectional Long Short-Term Memory algorithm (BiLSTM) and improved hierarchical attention mechanism is proposed. Firstly, an emotion analysis model is constructed based on text-level analysis and subjective and objective analysis, and the dimensionality curse problem of one-hot representation is solved by integrating the weighted word vector of TF-IDF. Then, by constructing a bidirectional long short-term memory neural network, the full acquisition of context information is realized, which increases the fine-grained elements of emotion analysis. Finally, by introducing an improved hierarchical attention mechanism, the network model can focus on different parts to achieve text classification and emotion analysis. Through simulation experiments, the proposed emotion analysis method and the other two methods are compared and analyzed under the condition of using the same database. The results show that the precision, recall, and F1 value of the method proposed in this paper are the best under 7 different emotion classifications, with the highest reaching 95.8%, 95.9%, and 96.1%, respectively, and the algorithm performance is better than the other two comparisons algorithm. It is proved that the proposed model has excellent performance.

SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding.

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called “SlimMe” and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

Rôle des émotions et représentations dans le recours à la m-health chez les membres d’une communauté d’intérêt en ligne en lien avec le cancer, les Seintinelles

Virtual communities related to cancer, as with other chronic pathologies, seem to constitute a population likely to have a privileged relationship with e-health technologies. However, although many studies have sought to identify the characteristics of e-health users, none has focused specifically on members of health-related virtual communities. The objective is to identify the psychosocial determinants of the use of connected objects and health applications (COAs) among members of an online community of interest in cancer, the Seintinelles. Data collection was performed using a self-questionnaire aiming to collect sociodemographic and psychosocial data. Only a minority of participants owned at least one COA. Positive emotions and the perception of COAs as a motivator to take care of one's health favored their adoption. Considering COASs as aimed toward ill people or perceiving them as complex are barriers to their use. Participants do not seem to have a privileged relationship with COAs. Reinforcing a sense of self-efficacy and ensuring that mobile devices and health apps elicit positive emotions in potential users is an essential step in facilitating the use of m-health from a health promotion perspective among individuals who are members of such virtual communities related to cancer.

GesFabri: Exploring Affordances and Experience of Textile Interfaces for Gesture-based Interaction

Textile interfaces are of interest to ubiquitous computing as they are easy to carry and manipulate. However, interesting questions remain about what type of natural gestures people make when interacting with textile interfaces and their emotional response to this interaction. We introduce GesFabri, a set of five interactive textile interfaces with distinct textures, created to investigate the intuitive interaction gestures and accompanied the emotional experience. This research sought to (1) design textile interfaces with intuitive gesture affordance, (2) explore the emotional effects of the developed gesture-based interfaces under four feedback modes (touch-only, visual feedback, audio feedback, multisensory feedback). The experimental results verify our hypotheses that (1) textile texture could provide natural gesture affordances; (2) the GesFabri interfaces' feedback mode was the main factor in the differences of emotional valence, arousal, GSR; and (3) both gesture-based interaction on textiles and the feedback mode had an impact on user emotions. These results highlight the gesture affordances of the e-textile interfaces and contribute to a better understanding of the user experience when interacting with gesture-based textile interfaces.

How does government microblog affect tourism market value? The perspective of signaling theory

The emotion of social media users has been found to impact market value, but the mechanism of emotion transmission needs to be further explored. Based on the signaling theory, this study formulates the hypothesis that government microblog affects tourism market value by affecting consumers' emotion. A total of 10020 pieces of observation data on four famous Chinese scenic spots are analyzed with text analysis technique, and the correlations between government microblog emotion, comment emotion, and market value are confirmed. Besides, the moderating roles played by the COVID-19 epidemic and tourism attention are verified, and the crowding-out effect of tourism attention on comment emotion is validated. This study provides new understandings of signal transmission as well as practical suggestions for promoting the development of tourism through government social media in the context of COVID-19.

EmotIoT: An IoT System to Improve Users’ Wellbeing

IoT provides applications and possibilities to improve people’s daily lives and business environments. However, most of these technologies have not been exploited in the field of emotions. With the amount of data that can be collected through IoT, emotions could be detected and anticipated. Since the study of related works indicates a lack of methodological approaches in designing IoT systems from the perspective of emotions and smart adaption rules, we introduce a methodology that can help design IoT systems quickly in this scenario, where the detection of users is valuable. In order to test the methodology presented, we apply the proposed stages to design an IoT smart recommender system named EmotIoT. The system allows anticipating and predicting future users’ emotions using parameters collected from IoT devices. It recommends new activities for the user in order to obtain a final state. Test results validate our recommender system as it has obtained more than 80% accuracy in predicting future user emotions.

True feelings but False meanings: Emotional Performance and Individual Confrontation in the Dissemination of Misinformation

In the process of spreading rumors, emotion is regarded as a social resource that can be mobilized. Existing studies have described the characteristics and influence mechanism of the spread of misinformation based on the text, but this process ignores the important role of emotion in the spread of misinformation. This study uses the grounded theory research method to conduct in-depth interviews with users who have participated in emotional dissemination of misinformation on social media platforms, and explores how user emotions play a role in the dissemination of misinformation combined with the coding results. It is found that emotion sets up a unique alternative application strategy of confrontation and compliance when users deal with misinformation through three forms of driving attention, gaining recognition and achieving goals.

Application of Virtual Reality Technology in Visual Optimization of Product Appearance Design

Virtual reality technology is used in this research to perform an in-depth investigation and the analysis of visual optimization of product appearance design, as well as to adapt evolving technology to practical design. The breadth of virtual display technology may be further investigated, and software abilities can be enhanced by studying the virtual display of intelligent goods. The performance indicators of the product may be exhibited in an all-around manner via the virtual display, giving customers an intuitive experience and thereby stimulating consumers’ desire to purchase. To achieve the research objectives of this paper, we first researched the professional positions of designers, operators, and product planners in related industries, and used the survey analysis method to compile valid data from the research results. And we conducted research and analysis on consumers of different age levels to derive different functional requirements of wearable products for different age levels, and compare and analyze the advantages and shortcomings of various types of smart wearable products in terms of their product presentation. The method refers to the product appearance design, making the product’s functional attributes, user emotions, and other factors as the starting point, and taking the visual effect of color as the center of the design scheme conception, the designer first conceives the visual effect of color to meet the requirements of function and structure, constructs a three-dimensional form that echoes it, and then explores the comprehensive effect of color and form, and further considers and designs the color, material, and so on from the perspective of users, product function, and structure. Then, we explore the comprehensive effect of color and form, and further think and design color and form from the perspective of users, product function, structure, and material, and finally get the color and form matching and reasonable product appearance. This design method can break through the limitation of “filling colors with shapes” and design a new, individual, and emotional product image.

Application of an Improved LSTM Model to Emotion Recognition

The rise of artificial intelligence technology has promoted the development of human-computer interaction and other fields. In human-computer interaction, in order to enable the machine to accurately perceive and understand the user’s emotion in real time, thereby improving the service quality of the machine, user emotion recognition has been widely studied. In real life, because voice output not only is convenient, but also contains rich emotional information, human-computer interaction is mainly carried out in the form of voice. Speech carries a wealth of linguistic, paralinguistic, and nonlinguistic information that is essential for human-computer interaction. Understanding language information alone will not allow a computer to fully comprehend the speaker’s purpose. For computers to behave like humans, speech recognition systems must be able to process nonverbal information, such as the emotional state of the speaker. As a result, developing machine understanding of human emotions requires speech-based emotion recognition. This paper proposes an improved long short-term memory network (ILSTM) for emotion recognition. Because the initial LSTM only analyzes the preceding moment’s input, it will miss out on a lot of information for the full context scene. In this way, all the features in the speech segment can be extracted. In order to be able to select the feature that can express emotion the most among the many features, this paper also introduces the attention mechanism. Experiments are carried out on public datasets, and the experimental results show that the ILSTM used in this paper is very effective in classifying speech emotion data and the classification accuracy can reach more than 0.6. This fully shows that this research can be applied to actual products and has certain feasibility and reference value.

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.

HUMAN FACIAL EMOTION RECOGNITION USING CNN

Human beings express emotions in everyday interactions. Understanding their emotions and knowing how to react to these expressions greatly enhances the interaction. An automatic Facial Expression Recognition system must try to solve the following problems: detection and location of faces ina cluttered scene, facial feature extraction, and facial expression classification. Knowing the user emotion, the system can adapt to the user. Facial expressions play an important role in recognition of emotions and are used in the process of non-verbal communication. They are very important in daily emotional communication, just next to the tone of voice. They are also an indicator of feelings, allowing a man to express an emotional state. The main motivation behind this project is to detect mental health of an individual.

Emotional state recognition from text data using machine learning and deep learning algorithm

AbstractEmotional state recognition is a process to identify user's feelings and emotions for various purposes. Emotional state examination from text comprises of extricating data about feelings, opinions, and even feelings passed on by scholars toward subjects of interest. Web‐based media is producing a tremendous measure of assessment rich information as remarks, notices, blog entries, and so forth. It is trying to comprehend the most recent patterns and rundowns the state or general feelings about items because of the enormous variety and size of web‐based media information, and this makes the need of computerized and ongoing conclusion extraction and mining. Sentiment analysis is difficult because of the existence of bad or abusive language with misspellings words. One of the major natural language processing research area is inclined toward understanding human emotions. Emotional state analysis acts like an amazing treasure and powerful tool, which renders its service to the field of deep learning. It can help service providers to fetch the requisite information to collect and identify the sentiments of the database. Principle issues that exist in the current procedures are: powerlessness to perform well in various areas, deficient exactness and execution in assessment examination dependent on lacking named information, inadequacy to manage complex sentences that require more than emotional words and basic examining. It is as yet hard for a greater part of instruments to decisively assess what genuinely is a negative, unbiased and a positive articulation. It is not advanced enough to successfully deal with sarcasm or context. So there is requirement to develop a machine learning algorithm to analyze the text data and give more better and accurate results.

Artificial Emotions and Love and Sex Doll Service Workers

Realistic looking humanoid love and sex dolls have been available on a somewhat secretive basis for at least three decades. But today the industry has gone mainstream with North American, European, and Asian producers using mass customization and competing on the bases of features, realism, price, and depth of product lines. As a result, realistic life size artificial companions are becoming more affordable to purchase and more feasible to patronize on a service basis. Sexual relations may be without equal when it comes to emotional intimacy. Yet, the increasingly vocal and interactive robotic versions of these dolls, feel nothing. They may nevertheless induce emotions in users that potentially surpass the pleasure of human-human sexual experiences. The most technologically advanced love and sex robots are forecast to sense human emotions and gear their performances of empathy, conversation, and sexual activity accordingly. I offer a model of how this might be done to provide a better service experience. I compare the nature of resulting “artificial emotions” by robots to natural emotions by humans. I explore the ethical issues entailed in offering love and sex robot services with artificial emotions and offer a conclusion and recommendations for service management and for further research.

Emotion Recognizing by a Robotic Solution Initiative (EMOTIVE Project).

Background: Emotion recognition skills are predicted to be fundamental features in social robots. Since facial detection and recognition algorithms are compute-intensive operations, it needs to identify methods that can parallelize the algorithmic operations for large-scale information exchange in real time. The study aims were to identify if traditional machine learning algorithms could be used to assess every user emotions separately, to relate emotion recognizing in two robotic modalities: static or motion robot, and to evaluate the acceptability and usability of assistive robot from an end-user point of view. Methods: Twenty-seven hospital employees (M = 12; F = 15) were recruited to perform the experiment showing 60 positive, negative, or neutral images selected in the International Affective Picture System (IAPS) database. The experiment was performed with the Pepper robot. Concerning experimental phase with Pepper in active mode, a concordant mimicry was programmed based on types of images (positive, negative, and neutral). During the experimentation, the images were shown by a tablet on robot chest and a web interface lasting 7 s for each slide. For each image, the participants were asked to perform a subjective assessment of the perceived emotional experience using the Self-Assessment Manikin (SAM). After participants used robotic solution, Almere model questionnaire (AMQ) and system usability scale (SUS) were administered to assess acceptability, usability, and functionality of robotic solution. Analysis wasperformed on video recordings. The evaluation of three types of attitude (positive, negative, andneutral) wasperformed through two classification algorithms of machine learning: k-nearest neighbors (KNN) and random forest (RF). Results: According to the analysis of emotions performed on the recorded videos, RF algorithm performance wasbetter in terms of accuracy (mean ± sd = 0.98 ± 0.01) and execution time (mean ± sd = 5.73 ± 0.86 s) than KNN algorithm. By RF algorithm, all neutral, positive and negative attitudes had an equal and high precision (mean = 0.98) and F-measure (mean = 0.98). Most of the participants confirmed a high level of usability and acceptability of the robotic solution. Conclusions: RF algorithm performance was better in terms of accuracy and execution time than KNN algorithm. The robot was not a disturbing factor in the arousal of emotions.

Virtual nexus: Female players’ emotional attachment in a role-playing video game

Casual gaming has become an increasingly important part of leisure in the last decade, and role-playing games (RPGs) are one of the most popular genres. RPGs prompt players to develop emotional attachments, so exploring players’ user emotions toward RPGs is of academic significance. This study applied the qualitative method to examine the development of female user emotional attachment in the RPG Onmyoji. The resulting model of developing user emotions between female players and the game and the three stages of the development are structured. The results indicate that female players can form emotional connections with the game, and they generate an emotional ‘virtual nexus’ in the process.