Mining citizen emotions to estimate the urgency of urban issues

Crowdsourcing technology offers exciting possibilities for local governments. Specifically, citizens are increasingly taking part in reporting and discussing issues related to their neighborhood and problems they encounter on a daily basis, such as overflowing trash-bins, broken footpaths and lifts, illegal graffiti, and potholes. Pervasive citizen participation enables local governments to respond more efficiently to these urban issues. This interaction between citizens and municipalities is largely promoted by civic engagement platforms, such as See-Click-Fix, FixMyStreet, CitySourced, and OpenIDEO, which allow citizens to report urban issues by entering free text describing what needs to be done, fixed or changed. In order to develop appropriate action plans and priorities, government officials need to figure out how urgent are the reported issues. In this paper we propose to estimate the urgency of urban issues by mining different emotions that are implicit in the text describing the issue. More specifically, a reported issue is first categorized according to the emotions expressed in it, and then the corresponding emotion scores are combined in order to produce a final urgency level for the reported issue. Our experiments use the SeeClickFix hackathon data and diverse emotion classification algorithms. They indicate that (i) emotions can be categorized efficiently with supervised learning algorithms, and (ii) the use of citizen emotions leads to accurate urgency estimates. Further, using additional features such as the type of issue or its author leads to no further accuracy gains.     

DEAR-MULSEMEDIA: Dataset for emotion analysis and recognition in response to multiple sensorial media

Traditionally, emotion recognition is performed in response to stimuli that engage either one (vision: image or hearing: audio) or two (vision and hearing: video) human senses. An immersive environment can be generated by engaging more than two human senses while interacting with multimedia content and is known as MULtiple SEnsorial media (mulsemedia). This study aims to create a new dataset of multimodal physiological signals to recognize emotions in response to such content. To this end, four multimedia clips are selected and synchronized with fan, heater, olfaction dispenser, and haptic vest to augment cold air, hot air, olfaction, and haptic effects respectively. Furthermore, physiological responses including electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG) are observed to analyze human emotional responses while experiencing mulsemedia content. A t-test applied using arousal and valence scores show that engaging more than two human senses evokes significantly different emotions. Statistical tests on EEG, GSR, and PPG responses also show a significant difference between multimedia and mulsemedia content. Classification accuracy of 85.18% and 76.54% is achieved for valence and arousal, respectively, using K-nearest neighbor classifier and feature-level fusion strategy.     

多智能体环境下的情绪决策模型?

建立一种基于情绪的Nash-Q决策模型,它由认知层和情绪层组成。认知层模型由Nash-Q算法实现,情绪层建立在情绪记忆和评价理论之上,由高兴、伤心、恐惧、厌烦组成情绪空间,建立相应刺激与情绪映射模型、情绪与行为动作映射模型、每种情绪下的动作信任度评价模型。将文中模型应用到两智能体网格决策实验中,结果表明情绪层的引入可加快收敛速度,同时能有效防止陷入局部最优,更好兼顾在线学习的“保守”和“探索”平衡。     

“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

Face detection and recognition of natural human emotion using Markov random fields

This paper presents an integrated system for emotion detection. In this research effort, we have taken into account the fact that emotions are most widely represented with eye and mouth expressions. The proposed system uses color images and it is consisted of three modules. The first module implements skin detection, using Markov random fields models for image segmentation and skin detection. A set of several colored images with human faces have been considered as the training set. A second module is responsible for eye and mouth detection and extraction. The specific module uses the HLV color space of the specified eye and mouth region. The third module detects the emotions pictured in the eyes and mouth, using edge detection and measuring the gradient of eyes’ and mouth’s region figure. The paper provides results from the system application, along with proposals for further research.     

基于小波变换与神经网络的表面肌电信号的情感识别

情感识别是情感计算的一个关键问题。针对表面肌电图(EMG)的非平稳性,采用小波变换方法对表面肌电信号进行分析,提取小波系数最大值和最小值构造特征矢量,分别输入用L-M算法改进的BP神经网络分类器和最近邻法分类器进行情感识别。实验表明,提取EMG的小波系数对joy、anger、sadness、pleasure四种情感进行识别,BP神经网络分类器识别效果优于最近邻法分类器。说明小波变换的方法对EMG进行分析是可行且有效的,并有很大的应用前景。     

A Multi-scale Attention-based Facial Emotion Recognition Method Based on Deep Learning

Recently, people have been paying more and more attention to mental health, such as depression, autism, and other common mental diseases. In order to achieve a mental disease diagnosis, intelligent methods have been actively studied. However, the existing models suffer the accuracy degradation caused by the clarity and oc-clusion of human faces in practical applications. This paper, thus, proposes a multi-scale feature fusion network that obtains feature information at three scales by locating the sentiment region in the image, and integrates global feature information and local feature information. In addition, a focal cross-entropy loss function is designed to improve the network''s focus on difficult samples during training, enhance the training effect, and increase the model recognition accuracy. Experimental results on the challenging RAF_DB dataset show that the proposed model exhibits better facial expression recognition accuracy than existing techniques.     

具有学习机制及情绪特征的连续进化博弈仿真

当前博弈理论的研究主要集中在合作竞争及稳定性的分析上,有关心理因素对博弈过程的影响的研究尚不多见。将情绪特征引入连续进化博弈过程,可更为真实地仿真群体博弈过程。针对不同决策风格的个体,建立个体学习机制及根据情绪特征的策略变异机制。仿真结果表明,学习能力有助于个体之间的博弈平均合作率及群体收益总量的提高;情绪对个体收益产生一定的波动性影响,但对群体平均收益的波动性影响不大。     

Negative emotional and cognitive responses to being unfriended on Facebook: An exploratory study

We consider Facebook unfriending as a form of relationship termination with negative emotional and cognitive consequences. Specifically, ruminative and negative emotional responses are examined via an online survey of adult Facebook users who were unfriended. These responses were positively related to each other and to Facebook intensity. Rumination was positively predicted by using Facebook to connect with existing contacts and was more likely when the unfriender was a close partner. Participants also responded with greater rumination and negative emotion when they knew who unfriended them, when they thought they were unfriended for Facebook-related reasons, and when participants initiated the Facebook friend request. The contribution of these exploratory findings to our growing understanding of negative relational behaviors on Facebook are discussed.     

A longitudinal study of emoticon use in text messaging from smartphones

Our goal in the present study was to understand how emoticons are used in text messaging and, in particular, how genders differed in the frequency and variety of emoticons used via this medium. Previous research has found small and sundry differences in emotive expression online suggesting that technology has closed the gender gap. However, the data collected in these studies were public. In this study, we collected real portions of private communications data from individuals’ smartphones over a 6-month period. SMS messages, in general, were not used very much overall, with only 4% of all messages containing at least one emoticon. Still, differences between genders manifested in the amount and variety of emoticons used. Females sent more messages with emoticons; however, surprisingly, males used a more diverse range of emoticons.