Background:In China,the policy of rotating teachers between urban and rural schools has been implemented to reduce educational disparities and ensure equitable access to quality education.These teachers face unique pr...Background:In China,the policy of rotating teachers between urban and rural schools has been implemented to reduce educational disparities and ensure equitable access to quality education.These teachers face unique professional and emotional challenges during the rotation process,making their emotional labor a critical factor influencing their job performance.This study aimed to explore the relationship between rotating teachers’emotional labor strategies and job performance.Methods:This study conducted a cross-sectional survey among 577 rotating teachers selected through stratified random sampling from primary and secondary schools in Chinese mainland.Date were collected using the Teacher Emotional Labor Scale and the Teacher Job Performance Scale.Latent profile analysis(LPA)was employed to identify distinct categories of emotional labor strategies:indifferent,moderately engaged,naturally invested,proactively adjusted,and emotionally elevated.Results:Teachers in the naturally invested and proactively adjusted types demonstrated relatively higher job performance scores,followed by those in the emotionally elevated type.In contrast,teachers in the indifferent and moderate engagement types exhibited comparatively lower scores(F=25.858,p<0.001,η^(2)=0.153).These findings indicate a practical significance,suggestion that flexible and adaptive use emotional labor strategies is strongly associated with enhanced job performance.Conclusion:This study demonstrates that rotating teachers’job performance differs significantly across distinct emotional labor profiles,with balanced and adaptive emotional regulation emerging as a key determinant of higher performance.By identifying and characterizing individual-centered emotional labor profiles,the study advances understanding of how emotional regulation contributes to teachers’professional effectiveness.These results underscore the importance of providing systematic and personalized support to help rotating teachers develop adaptive emotional regulation skills.Targeted guidance should enable teachers to appropriately express and adjust their emotions,thereby avoiding both excessive and insufficient emotional labor and promoting sustainable professional development.展开更多
BACKGROUND Neck pain,a primary symptom of cervical spondylosis,affects patients'physical and mental health,reducing their quality of life.Pain and emotional state interact;however,their longitudinal interrelations...BACKGROUND Neck pain,a primary symptom of cervical spondylosis,affects patients'physical and mental health,reducing their quality of life.Pain and emotional state interact;however,their longitudinal interrelationship remains unclear.In this study,we applied a dual-trajectory model to assess how neck pain and emotional state evolve together over time and how clinical interventions,particularly acupuncture,influence these trajectories.AIM To investigate the longitudinal relationship between neck pain and emotional state in patients with cervical spondylosis.METHODS This prospective cohort study included 472 patients with cervical spondylosis from eight Chinese hospitals.Participants received acupuncture or medication and were followed up at baseline,and at 1,2,4,6,and 8 weeks.Neck pain and emotional distress were assessed using the Northwick Park Neck Pain Questionnaire(NPQ)and the affective subscale of the Short-Form McGill Pain Questionnaire(SF-MPQ),respectively.Group-based trajectory models and dual trajectory analysis were used to identify and correlate pain-emotion trajectories.Multivariate logistic regression identified predictors of group membership.RESULTS Three trajectory groups were identified for NPQ and SF-MPQ scores(low,medium,and high).Higher NPQ trajectory was associated with older age(OR=1.058,P<0.001)and was significantly reduced by acupuncture(OR=0.382,P<0.001).Similarly,acupuncture lowered the odds of high SF-MPQ trajectory membership(OR=0.336,P<0.001),while age increased it(OR=1.037,P<0.001).Dual-trajectory analysis revealed bidirectional associations:69.1%of patients with low NPQ had low SF-MPQ scores,and 42.6%of patients with high SF-MPQ also had high NPQ scores.Gender was a predictor for medium SF-MPQ trajectory(OR=1.629,P=0.094).Occupation and education levels differed significantly across the trajectory groups(P<0.05).CONCLUSION Over time,neck pain and emotional distress are closely associated in patients with cervical spondylosis.Acupuncture alleviates both outcomes significantly,while age is a risk factor.Integrated approaches to pain and emotional management are encouraged.展开更多
Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samp...Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,...Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.展开更多
The mass data of social media and social networks generated by users play an important role in tracking users’sentiments and opinions online.A good polarity lexicon which can effectively improve the classification re...The mass data of social media and social networks generated by users play an important role in tracking users’sentiments and opinions online.A good polarity lexicon which can effectively improve the classification results of sentiment analysis is indispensable to analyze the user’s sentiments.Inspired by social cognitive theories,we combine basic emotion value lexicon and social evidence lexicon to improve traditional polarity lexicon.The proposed method obtains significant improvement in Chinese text sentiment analysis by using the proposed lexicon and new syntactic analysis method.展开更多
Background:Knowledge around emotional intelligence originated in the 1990s from research regarding thoughts,emotions and abilities.The concept of emotional intelligence has evolved over the last 25 years;however,the u...Background:Knowledge around emotional intelligence originated in the 1990s from research regarding thoughts,emotions and abilities.The concept of emotional intelligence has evolved over the last 25 years;however,the understanding and use is still unclear.Despite this,emotional intelligence has been a widely-considered concept within professions such as business,management,education,and within the last 10 years has gained traction within nursing practice.Aims and objectives:The aim of this concept review is to clarify the understanding of the concept emotional intelligence,what attributes signify emotional intelligence,what are its antecedents,consequences,related terms and implications to advance nursing practice.Method:A computerized search was guided by Rodger's evolutional concept analysis.Data courses included:CINAHL,PyschINFO,Scopus,EMBASE and ProQuest,focusing on articles published in Canada and the United Stated during 1990e2017.Results:A total of 23 articles from various bodies of disciplines were included in this integrative concept review.The analysis reveals that there are many inconsistencies regarding the description of emotional intelligence,however,four common attributes were discovered:self-awareness,self-management,social awareness and social/relationship management.These attributes facilitate the emotional well-being among advance practice nurses and enhances the ability to practice in a way that will benefit patients,families,colleagues and advance practice nurses as working professionals and as individuals.Conclusion:The integration of emotional intelligence is supported within several disciplines as there is consensus on the impact that emotional intelligence has on job satisfaction,stress level,burnout and helps to facilitate a positive environment.Explicit to advance practice nursing,emotional intelligence is a concept that may be central to nursing practice as it has the potential to impact the quality of patient care and outcomes,decision-making,critical thinking and overall the well-being of practicing nurses.展开更多
As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practica...As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.展开更多
In this paper,we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system,by using Wakamono Kotoba example sentences as features.The proposed method applies Earth Mover's D...In this paper,we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system,by using Wakamono Kotoba example sentences as features.The proposed method applies Earth Mover's Distance(EMD) to vector of words.As a result of the evaluation experiment using 14 440 sentences,higher estimation accuracy is obtained by considering emotional distance between words-an approach that had not been used in the conventional research-than by using only word importance value.展开更多
In recent years,renewable energy technologies have been developed vigorously,and related supporting policies have been issued.The developmental trend of different energy sources directly affects the future development...In recent years,renewable energy technologies have been developed vigorously,and related supporting policies have been issued.The developmental trend of different energy sources directly affects the future developmental pattern of the energy and power industry.Energy trend research can be quantified through data statistics and model calculations;however,parameter settings and optimization are difficult,and the analysis results sometimes do not reflect objective reality.This paper proposes an energy and power information analysis method based on emotion mining.This method collects energy commentary news and literature reports from many authoritative media around the world and builds a convolutional neural network model and a text analysis model for topic classification and positive/negative emotion evaluation,which helps obtain text evaluation matrixes for all collected texts.Finally,a long-short-term memory model algorithm is employed to predict the future development prospects and market trends for various types of energy based on the analyzed emotions in different time spans.Experimental results indicate that energy trend analysis based on this method is consistent with the real scenario,has good applicability,and can provide a useful reference for the development of energy and power resources and of other industry areas as well.展开更多
In this paper,we describe a method of emotion analysis on social big data.Social big data means text data that is emerging on Internet social networking services.We collect multilingual web corpora and annotated emoti...In this paper,we describe a method of emotion analysis on social big data.Social big data means text data that is emerging on Internet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis.Because these data are constructed by manual annotation,their quality is high but their quantity is low.If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data,we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications,which we could not know before.In this paper,we create an emotion analysis model that integrate the highquality emotion corpus and the automaticconstructed corpus that we created in our past studies,and then analyze a large-scale corpus consisting of Twitter tweets based on the model.As the result of time-series analysis on the large-scale corpus and the result of model evaluation,we show the effectiveness of our proposed method.展开更多
Spatial-temporal analysis of emotions in society has become popular in many studies integrating geography with the humanities,and has shown its influence on social sensing and geo-computation for social sciences.Emoti...Spatial-temporal analysis of emotions in society has become popular in many studies integrating geography with the humanities,and has shown its influence on social sensing and geo-computation for social sciences.Emotions in society are often volatile,irrational,and vulnerable to the social environment.A critical challenge is to analyze changes in long-term and large-scale emotions in society.In this paper,we propose exploiting this challenge by using spatial-temporal analysis.After extracting emotional,temporal,and spatial information,a spatial standardization approach based on adataset of administrative district changes addresses the problem of Chinese toponym changes.Finally,over 1.7 million news data from the People’s Daily from 1956 to 2014 were collected to explore the changes,spatial distribution,and driving factors of emotions in society using spatial-temporal analysis.The experimental results found that the spatial-temporal analysis of emotions in society in the news is consistent with the results of related sociological research.展开更多
Facial expressions are the straight link for showing human emotions. Psychologists have established the universality of six prototypic basic facial expressions of emotions which they believe are consistent among cultu...Facial expressions are the straight link for showing human emotions. Psychologists have established the universality of six prototypic basic facial expressions of emotions which they believe are consistent among cultures and races. However, some recent cross-cultural studies have questioned and to some degree refuted this cultural universality. Therefore, in order to contribute to the theory of cultural specificity of basic expressions, from a composite viewpoint of psychology and HCI (Human Computer Interaction), this paper presents a methodical analysis of Western-Caucasian and East-Asian prototypic expressions focused on four facial regions: forehead, eyes-eyebrows, mouth and nose. Our analysis is based on facial expression recognition and visual analysis of facial expression images of two datasets composed by four standard databases CK+, JAFFE, TFEID and JACFEE. A hybrid feature extraction method based on Fourier coefficients is proposed for the recognition analysis. In addition, we present a cross-cultural human study applied to 40 subjects as a baseline, as well as one comparison of facial expression recognition performance between the previous cross-cultural tests from the literature. With this work, it is possible to clarify the prior considerations for working with multicultural facial expression recognition and contribute to identifying the specific facial expression differences between Western-Caucasian and East-Asian basic expressions of emotions.展开更多
Analysis of customers＇ satisfaction provides a guarantee to improve the service quality in call centers.In this paper,a novel satisfaction recognition framework is introduced to analyze the customers＇ satisfaction.I...Analysis of customers＇ satisfaction provides a guarantee to improve the service quality in call centers.In this paper,a novel satisfaction recognition framework is introduced to analyze the customers＇ satisfaction.In natural conversations,the interaction between a customer and its agent take place more than once.One of the difficulties insatisfaction analysis at call centers is that not all conversation turns exhibit customer satisfaction or dissatisfaction. To solve this problem,an intelligent system is proposed that utilizes acoustic features to recognize customers＇ emotion and utilizes the global features of emotion and duration to analyze the satisfaction. Experiments on real-call data show that the proposed system offers a significantly higher accuracy in analyzing the satisfaction than the baseline system. The average F value is improved to 0. 701 from 0. 664.展开更多
A nonlinear method named detrended fluctuation analysis(DFA)was utilized to investigate the scaling behavior of the human electroencephalogram(EEG)in three emotional music conditions(fear,happiness,sadness)and a rest ...A nonlinear method named detrended fluctuation analysis(DFA)was utilized to investigate the scaling behavior of the human electroencephalogram(EEG)in three emotional music conditions(fear,happiness,sadness)and a rest condition(eyes-closed).The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponentsβ1 andβ2 which represented the complexity of higher and lower frequency activity besidesαband respectively.As the emotional intensity decreased the value ofβ1 increased and the value ofβ2 decreased.The change ofβ1 was weakly correlated with the'approach-withdrawal'model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition.The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.展开更多
To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conven...To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conventional linear discriminant analysis(LDA),uncertainties of the noisy or distorted input data ae employed in order to estimate maximaiy discriminant directions.The effectiveness of the proposed uncertain LDA(ULDA)is demonstrated in the Uyghur speech emotion recognition task.The emotional features of Uyghur speech,especially,the fundamental fequency and formant,a e analyzed in the collected emotional data.Then,ULDA is employed in dimensionality reduction of emotional features and better performance is achieved compared with other dimensionality reduction techniques.The speech emotion recognition of Uyghur is implemented by feeding the low-dimensional data to support vector machine(SVM)based on the proposed ULDA.The experimental results show that when employing a appropriate uncertainty estimation algorithm,uncertain LDA outperforms the conveetional LDA counterpart on Uyghur speech emotion recognition.展开更多
Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints.A parallel computational environment pro-vided by Apache Hadoop can distribute and process the data over differen...Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints.A parallel computational environment pro-vided by Apache Hadoop can distribute and process the data over different desti-nation systems.In this paper,the Hadoop cluster with four nodes integrated with RHadoop,Flume,and Hive is created to analyze the tweets gathered from the Twitter stream.Twitter stream data is collected relevant to an event/topic like IPL-2015,cricket,Royal Challengers Bangalore,Kohli,Modi,from May 24 to 30,2016 using Flume.Hive is used as a data warehouse to store the streamed tweets.Twitter analytics like maximum number of tweets by users,the average number of followers,and maximum number of friends are obtained using Hive.The network graph is constructed with the user’s unique screen name and men-tions using‘R’.A timeline graph of individual users is generated using‘R’.Also,the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized sup-port vector neural network(OSVNN)classification model.To attain better classi-fication accuracy,the performance of SVNN is enhanced using a chimp optimization algorithm(ChOA).Extracting the users’emotions toward an event is beneficial for prediction,but when coupled with visualizations,it becomes more powerful.Bar-chart and wordcloud are generated to visualize the emotional analysis results.展开更多
In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education ...In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education is based on emotional tendency analysis of psychological adjustment function model.Design emotional tendency analysis of music education psychological adjustment function architecture,music teaching goal as psychological adjust-ment function architecture building orientation,music teaching content as a foundation for psychological adjust-ment function architecture and music teaching process as a psychological adjustment function architecture building,music teaching evaluation as the key of building key regulating function architecture,Establish a core literacy oriented evaluation system.Different evaluation methods were used to obtain the evaluation results.Four levels of psychological adjustment function model of music education are designed,and the psychological adjust-ment function of music education is put forward,thus completing the construction of psychological adjustment function model of music education.The experimental results show that the absolute value of the data acquisition error of the designed model is minimum,which is not more than 0.2.It is less affected by a bad coefficient and has good performance.It can quickly converge to the best state in the actual prediction process and has a strong con-vergence ability.展开更多
In this case study, we hypothesized that sympathetic nerve activity would be higher during conversation with PALRO robot, and that conversation would result in an increase in cerebral blood flow near the Broca’s area...In this case study, we hypothesized that sympathetic nerve activity would be higher during conversation with PALRO robot, and that conversation would result in an increase in cerebral blood flow near the Broca’s area. The facial expressions of a human subject were recorded, and cerebral blood flow and heart rate variability were measured during interactions with the humanoid robot. These multimodal data were time-synchronized to quantitatively verify the change from the resting baseline by testing facial expression analysis, cerebral blood flow, and heart rate variability. In conclusion, this subject indicated that sympathetic nervous activity was dominant, suggesting that the subject may have enjoyed and been excited while talking to the robot (normalized High Frequency < normalized Low Frequency: 0.22 ± 0.16 < 0.78 ± 0.16). Cerebral blood flow values were higher during conversation and in the resting state after the experiment than in the resting state before the experiment. Talking increased cerebral blood flow in the frontal region. As the subject was left-handed, it was confirmed that the right side of the brain, where the Broca’s area is located, was particularly activated (Left < right: 0.15 ± 0.21 < 1.25 ± 0.17). In the sections where a “happy” facial emotion was recognized, the examiner-judged “happy” faces and the MTCNN “happy” results were also generally consistent.展开更多
OBJECTIVE：To elucidate the distribution of abnormally activated brain regions in depressive patients during emotional perception processing using activation likelihood estimation,a quantitative meta-analytic techniqu...OBJECTIVE：To elucidate the distribution of abnormally activated brain regions in depressive patients during emotional perception processing using activation likelihood estimation,a quantitative meta-analytic technique.DATA SOURCES：Computer-based online retrieval was conducted using the PscyINFO,Pubmed and CNKI databases,searching literature from the establishment of each database until June 2010.STUDY SELECTION：Inclusion criteria：（1） studies examined emotion perception tasks using func-tional magnetic resonance imaging; （2） studies indicated regional brain activation abnormalities in depressive patients compared with controls in standard Talairach or MNI coordinates.Any analyzed coordinates based on the MNI system were converted to Talairach space with icbm2tal software.The map of activation likelihood estimation was finally created through the Gaussian smooth （full-width half-maximum=8 mm）,permutation test and corrected for multiple comparisons using the false discovery rate method （q=0.05） with Ginger-ALE 2.0 software.MAIN OUTCOME MEASURES：Activation of brain regions in patients.RESULTS：Seventeen studies were identified,involving a total of 261 patients,273 healthy controls and 201 foci.Meta-analysis revealed a dysfunctional emotion regulation loop in depressive patients,comprised of the prefrontal cortex,the basal ganglia and the limbic lobe,in which the amygdala was a key component.During emotion processing,the left prefrontal cortex and basal ganglia were hy-poactive among depressive patients,whereas the opposite change was found in the limbic lobe.CONCLUSION：Abnormal connections among the prefrontal cortex,basal ganglia and limbic lobe may be involved in the pathophysiology of depressive disorder.展开更多
基金funded by the Postdoctoral Fellowship Program of CPSF(China),grant number GZ20232369.No part of the study(design,data collection,and curation analysis,manuscript preparation or publication)was influenced by the funder.
文摘Background:In China,the policy of rotating teachers between urban and rural schools has been implemented to reduce educational disparities and ensure equitable access to quality education.These teachers face unique professional and emotional challenges during the rotation process,making their emotional labor a critical factor influencing their job performance.This study aimed to explore the relationship between rotating teachers’emotional labor strategies and job performance.Methods:This study conducted a cross-sectional survey among 577 rotating teachers selected through stratified random sampling from primary and secondary schools in Chinese mainland.Date were collected using the Teacher Emotional Labor Scale and the Teacher Job Performance Scale.Latent profile analysis(LPA)was employed to identify distinct categories of emotional labor strategies:indifferent,moderately engaged,naturally invested,proactively adjusted,and emotionally elevated.Results:Teachers in the naturally invested and proactively adjusted types demonstrated relatively higher job performance scores,followed by those in the emotionally elevated type.In contrast,teachers in the indifferent and moderate engagement types exhibited comparatively lower scores(F=25.858,p<0.001,η^(2)=0.153).These findings indicate a practical significance,suggestion that flexible and adaptive use emotional labor strategies is strongly associated with enhanced job performance.Conclusion:This study demonstrates that rotating teachers’job performance differs significantly across distinct emotional labor profiles,with balanced and adaptive emotional regulation emerging as a key determinant of higher performance.By identifying and characterizing individual-centered emotional labor profiles,the study advances understanding of how emotional regulation contributes to teachers’professional effectiveness.These results underscore the importance of providing systematic and personalized support to help rotating teachers develop adaptive emotional regulation skills.Targeted guidance should enable teachers to appropriately express and adjust their emotions,thereby avoiding both excessive and insufficient emotional labor and promoting sustainable professional development.
基金Supported by 2022 Chinese Medicine Scientific Research Project of Hebei Administration of Traditional Chinese Medicine,No.20221572025 Annual Scientific Research Project of Higher Education Institutions in Hebei Province,No.QN2025654.
文摘BACKGROUND Neck pain,a primary symptom of cervical spondylosis,affects patients'physical and mental health,reducing their quality of life.Pain and emotional state interact;however,their longitudinal interrelationship remains unclear.In this study,we applied a dual-trajectory model to assess how neck pain and emotional state evolve together over time and how clinical interventions,particularly acupuncture,influence these trajectories.AIM To investigate the longitudinal relationship between neck pain and emotional state in patients with cervical spondylosis.METHODS This prospective cohort study included 472 patients with cervical spondylosis from eight Chinese hospitals.Participants received acupuncture or medication and were followed up at baseline,and at 1,2,4,6,and 8 weeks.Neck pain and emotional distress were assessed using the Northwick Park Neck Pain Questionnaire(NPQ)and the affective subscale of the Short-Form McGill Pain Questionnaire(SF-MPQ),respectively.Group-based trajectory models and dual trajectory analysis were used to identify and correlate pain-emotion trajectories.Multivariate logistic regression identified predictors of group membership.RESULTS Three trajectory groups were identified for NPQ and SF-MPQ scores(low,medium,and high).Higher NPQ trajectory was associated with older age(OR=1.058,P<0.001)and was significantly reduced by acupuncture(OR=0.382,P<0.001).Similarly,acupuncture lowered the odds of high SF-MPQ trajectory membership(OR=0.336,P<0.001),while age increased it(OR=1.037,P<0.001).Dual-trajectory analysis revealed bidirectional associations:69.1%of patients with low NPQ had low SF-MPQ scores,and 42.6%of patients with high SF-MPQ also had high NPQ scores.Gender was a predictor for medium SF-MPQ trajectory(OR=1.629,P=0.094).Occupation and education levels differed significantly across the trajectory groups(P<0.05).CONCLUSION Over time,neck pain and emotional distress are closely associated in patients with cervical spondylosis.Acupuncture alleviates both outcomes significantly,while age is a risk factor.Integrated approaches to pain and emotional management are encouraged.
基金The National Natural Science Foundation of China(No.61231002,61273266)the Ph.D.Programs Foundation of Ministry of Education of China(No.20110092130004)
文摘Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金This work is partially supported by the National Natural Science Foundation of China under Grant Nos.61876205 and 61877013the Ministry of Education of Humanities and Social Science project under Grant Nos.19YJAZH128 and 20YJAZH118+1 种基金the Science and Technology Plan Project of Guangzhou under Grant No.201804010433the Bidding Project of Laboratory of Language Engineering and Computing under Grant No.LEC2017ZBKT001.
文摘Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.
基金the National Natural Science Foundation of China(No.61303094)the Doctoral Fund ofMinistry of Education of China(No.20123108120027)+2 种基金the Program of Science and Technology Commission of Shanghai Municipality(No.14511107100)the Shanghai Leading Academic Discipline Project(No.J50103)the Innovation Program of Shanghai Municipal Education Commission(No.14YZ024)
文摘The mass data of social media and social networks generated by users play an important role in tracking users’sentiments and opinions online.A good polarity lexicon which can effectively improve the classification results of sentiment analysis is indispensable to analyze the user’s sentiments.Inspired by social cognitive theories,we combine basic emotion value lexicon and social evidence lexicon to improve traditional polarity lexicon.The proposed method obtains significant improvement in Chinese text sentiment analysis by using the proposed lexicon and new syntactic analysis method.
文摘Background:Knowledge around emotional intelligence originated in the 1990s from research regarding thoughts,emotions and abilities.The concept of emotional intelligence has evolved over the last 25 years;however,the understanding and use is still unclear.Despite this,emotional intelligence has been a widely-considered concept within professions such as business,management,education,and within the last 10 years has gained traction within nursing practice.Aims and objectives:The aim of this concept review is to clarify the understanding of the concept emotional intelligence,what attributes signify emotional intelligence,what are its antecedents,consequences,related terms and implications to advance nursing practice.Method:A computerized search was guided by Rodger's evolutional concept analysis.Data courses included:CINAHL,PyschINFO,Scopus,EMBASE and ProQuest,focusing on articles published in Canada and the United Stated during 1990e2017.Results:A total of 23 articles from various bodies of disciplines were included in this integrative concept review.The analysis reveals that there are many inconsistencies regarding the description of emotional intelligence,however,four common attributes were discovered:self-awareness,self-management,social awareness and social/relationship management.These attributes facilitate the emotional well-being among advance practice nurses and enhances the ability to practice in a way that will benefit patients,families,colleagues and advance practice nurses as working professionals and as individuals.Conclusion:The integration of emotional intelligence is supported within several disciplines as there is consensus on the impact that emotional intelligence has on job satisfaction,stress level,burnout and helps to facilitate a positive environment.Explicit to advance practice nursing,emotional intelligence is a concept that may be central to nursing practice as it has the potential to impact the quality of patient care and outcomes,decision-making,critical thinking and overall the well-being of practicing nurses.
基金This paper was supported by the 2018 Science and Technology Breakthrough Project of Henan Provincial Science and Technology Department(No.182102310694).
文摘As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.
基金supported by the Ministry of Education,Science,Sports and Culture,Grant-in-Aid for Scientific Research under Grants No.22240021,No.21300036the Grant-in-Aid for Young Scientists under Grant No.23700252
文摘In this paper,we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system,by using Wakamono Kotoba example sentences as features.The proposed method applies Earth Mover's Distance(EMD) to vector of words.As a result of the evaluation experiment using 14 440 sentences,higher estimation accuracy is obtained by considering emotional distance between words-an approach that had not been used in the conventional research-than by using only word importance value.
基金funded by the technical project of Global Energy Internet Group Co.,Ltd.:Research on Global Energy Internet Big Data Collection and Analysis Modeling and the National Key Research and Development Plan of China under Grant(2018YFB0905000)
文摘In recent years,renewable energy technologies have been developed vigorously,and related supporting policies have been issued.The developmental trend of different energy sources directly affects the future developmental pattern of the energy and power industry.Energy trend research can be quantified through data statistics and model calculations;however,parameter settings and optimization are difficult,and the analysis results sometimes do not reflect objective reality.This paper proposes an energy and power information analysis method based on emotion mining.This method collects energy commentary news and literature reports from many authoritative media around the world and builds a convolutional neural network model and a text analysis model for topic classification and positive/negative emotion evaluation,which helps obtain text evaluation matrixes for all collected texts.Finally,a long-short-term memory model algorithm is employed to predict the future development prospects and market trends for various types of energy based on the analyzed emotions in different time spans.Experimental results indicate that energy trend analysis based on this method is consistent with the real scenario,has good applicability,and can provide a useful reference for the development of energy and power resources and of other industry areas as well.
文摘In this paper,we describe a method of emotion analysis on social big data.Social big data means text data that is emerging on Internet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis.Because these data are constructed by manual annotation,their quality is high but their quantity is low.If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data,we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications,which we could not know before.In this paper,we create an emotion analysis model that integrate the highquality emotion corpus and the automaticconstructed corpus that we created in our past studies,and then analyze a large-scale corpus consisting of Twitter tweets based on the model.As the result of time-series analysis on the large-scale corpus and the result of model evaluation,we show the effectiveness of our proposed method.
基金National Natural Science Foundation of China(No.41971337)。
文摘Spatial-temporal analysis of emotions in society has become popular in many studies integrating geography with the humanities,and has shown its influence on social sensing and geo-computation for social sciences.Emotions in society are often volatile,irrational,and vulnerable to the social environment.A critical challenge is to analyze changes in long-term and large-scale emotions in society.In this paper,we propose exploiting this challenge by using spatial-temporal analysis.After extracting emotional,temporal,and spatial information,a spatial standardization approach based on adataset of administrative district changes addresses the problem of Chinese toponym changes.Finally,over 1.7 million news data from the People’s Daily from 1956 to 2014 were collected to explore the changes,spatial distribution,and driving factors of emotions in society using spatial-temporal analysis.The experimental results found that the spatial-temporal analysis of emotions in society in the news is consistent with the results of related sociological research.
文摘Facial expressions are the straight link for showing human emotions. Psychologists have established the universality of six prototypic basic facial expressions of emotions which they believe are consistent among cultures and races. However, some recent cross-cultural studies have questioned and to some degree refuted this cultural universality. Therefore, in order to contribute to the theory of cultural specificity of basic expressions, from a composite viewpoint of psychology and HCI (Human Computer Interaction), this paper presents a methodical analysis of Western-Caucasian and East-Asian prototypic expressions focused on four facial regions: forehead, eyes-eyebrows, mouth and nose. Our analysis is based on facial expression recognition and visual analysis of facial expression images of two datasets composed by four standard databases CK+, JAFFE, TFEID and JACFEE. A hybrid feature extraction method based on Fourier coefficients is proposed for the recognition analysis. In addition, we present a cross-cultural human study applied to 40 subjects as a baseline, as well as one comparison of facial expression recognition performance between the previous cross-cultural tests from the literature. With this work, it is possible to clarify the prior considerations for working with multicultural facial expression recognition and contribute to identifying the specific facial expression differences between Western-Caucasian and East-Asian basic expressions of emotions.
基金Supported by the National Natural Science Foundation of China(61473041,61571044,11590772)
文摘Analysis of customers＇ satisfaction provides a guarantee to improve the service quality in call centers.In this paper,a novel satisfaction recognition framework is introduced to analyze the customers＇ satisfaction.In natural conversations,the interaction between a customer and its agent take place more than once.One of the difficulties insatisfaction analysis at call centers is that not all conversation turns exhibit customer satisfaction or dissatisfaction. To solve this problem,an intelligent system is proposed that utilizes acoustic features to recognize customers＇ emotion and utilizes the global features of emotion and duration to analyze the satisfaction. Experiments on real-call data show that the proposed system offers a significantly higher accuracy in analyzing the satisfaction than the baseline system. The average F value is improved to 0. 701 from 0. 664.
基金This work was supported by the National Nature Science Foundation of China under Grant No.60571019 and No.30525030.
文摘A nonlinear method named detrended fluctuation analysis(DFA)was utilized to investigate the scaling behavior of the human electroencephalogram(EEG)in three emotional music conditions(fear,happiness,sadness)and a rest condition(eyes-closed).The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponentsβ1 andβ2 which represented the complexity of higher and lower frequency activity besidesαband respectively.As the emotional intensity decreased the value ofβ1 increased and the value ofβ2 decreased.The change ofβ1 was weakly correlated with the'approach-withdrawal'model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition.The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.
基金The National Natural Science Foundation of China(No.61673108,61231002)
文摘To achieve efficient a d compact low-dimensional features for speech emotion recognition,a novel featurereduction method using uncertain linear discriminant analysis is proposed.Using the same principles as for conventional linear discriminant analysis(LDA),uncertainties of the noisy or distorted input data ae employed in order to estimate maximaiy discriminant directions.The effectiveness of the proposed uncertain LDA(ULDA)is demonstrated in the Uyghur speech emotion recognition task.The emotional features of Uyghur speech,especially,the fundamental fequency and formant,a e analyzed in the collected emotional data.Then,ULDA is employed in dimensionality reduction of emotional features and better performance is achieved compared with other dimensionality reduction techniques.The speech emotion recognition of Uyghur is implemented by feeding the low-dimensional data to support vector machine(SVM)based on the proposed ULDA.The experimental results show that when employing a appropriate uncertainty estimation algorithm,uncertain LDA outperforms the conveetional LDA counterpart on Uyghur speech emotion recognition.
文摘Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints.A parallel computational environment pro-vided by Apache Hadoop can distribute and process the data over different desti-nation systems.In this paper,the Hadoop cluster with four nodes integrated with RHadoop,Flume,and Hive is created to analyze the tweets gathered from the Twitter stream.Twitter stream data is collected relevant to an event/topic like IPL-2015,cricket,Royal Challengers Bangalore,Kohli,Modi,from May 24 to 30,2016 using Flume.Hive is used as a data warehouse to store the streamed tweets.Twitter analytics like maximum number of tweets by users,the average number of followers,and maximum number of friends are obtained using Hive.The network graph is constructed with the user’s unique screen name and men-tions using‘R’.A timeline graph of individual users is generated using‘R’.Also,the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized sup-port vector neural network(OSVNN)classification model.To attain better classi-fication accuracy,the performance of SVNN is enhanced using a chimp optimization algorithm(ChOA).Extracting the users’emotions toward an event is beneficial for prediction,but when coupled with visualizations,it becomes more powerful.Bar-chart and wordcloud are generated to visualize the emotional analysis results.
基金supported by Shandong Provincial Social Science Planning Research Project“Research on Inheritance and Innovation of Shandong Wooden Clappers Culture”(20CCXJ26).
文摘In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education is based on emotional tendency analysis of psychological adjustment function model.Design emotional tendency analysis of music education psychological adjustment function architecture,music teaching goal as psychological adjust-ment function architecture building orientation,music teaching content as a foundation for psychological adjust-ment function architecture and music teaching process as a psychological adjustment function architecture building,music teaching evaluation as the key of building key regulating function architecture,Establish a core literacy oriented evaluation system.Different evaluation methods were used to obtain the evaluation results.Four levels of psychological adjustment function model of music education are designed,and the psychological adjust-ment function of music education is put forward,thus completing the construction of psychological adjustment function model of music education.The experimental results show that the absolute value of the data acquisition error of the designed model is minimum,which is not more than 0.2.It is less affected by a bad coefficient and has good performance.It can quickly converge to the best state in the actual prediction process and has a strong con-vergence ability.
文摘In this case study, we hypothesized that sympathetic nerve activity would be higher during conversation with PALRO robot, and that conversation would result in an increase in cerebral blood flow near the Broca’s area. The facial expressions of a human subject were recorded, and cerebral blood flow and heart rate variability were measured during interactions with the humanoid robot. These multimodal data were time-synchronized to quantitatively verify the change from the resting baseline by testing facial expression analysis, cerebral blood flow, and heart rate variability. In conclusion, this subject indicated that sympathetic nervous activity was dominant, suggesting that the subject may have enjoyed and been excited while talking to the robot (normalized High Frequency < normalized Low Frequency: 0.22 ± 0.16 < 0.78 ± 0.16). Cerebral blood flow values were higher during conversation and in the resting state after the experiment than in the resting state before the experiment. Talking increased cerebral blood flow in the frontal region. As the subject was left-handed, it was confirmed that the right side of the brain, where the Broca’s area is located, was particularly activated (Left < right: 0.15 ± 0.21 < 1.25 ± 0.17). In the sections where a “happy” facial emotion was recognized, the examiner-judged “happy” faces and the MTCNN “happy” results were also generally consistent.
基金the National Natural Science Foundation of China,No.30870686Science and Technology Research and Development Program of Shaanxi Province,No.2009K1601Innovation Funds of the First Affiliated Hospital of School of Medicine,Xi'an Jiaotong University,No.2009YK7
文摘OBJECTIVE：To elucidate the distribution of abnormally activated brain regions in depressive patients during emotional perception processing using activation likelihood estimation,a quantitative meta-analytic technique.DATA SOURCES：Computer-based online retrieval was conducted using the PscyINFO,Pubmed and CNKI databases,searching literature from the establishment of each database until June 2010.STUDY SELECTION：Inclusion criteria：（1） studies examined emotion perception tasks using func-tional magnetic resonance imaging; （2） studies indicated regional brain activation abnormalities in depressive patients compared with controls in standard Talairach or MNI coordinates.Any analyzed coordinates based on the MNI system were converted to Talairach space with icbm2tal software.The map of activation likelihood estimation was finally created through the Gaussian smooth （full-width half-maximum=8 mm）,permutation test and corrected for multiple comparisons using the false discovery rate method （q=0.05） with Ginger-ALE 2.0 software.MAIN OUTCOME MEASURES：Activation of brain regions in patients.RESULTS：Seventeen studies were identified,involving a total of 261 patients,273 healthy controls and 201 foci.Meta-analysis revealed a dysfunctional emotion regulation loop in depressive patients,comprised of the prefrontal cortex,the basal ganglia and the limbic lobe,in which the amygdala was a key component.During emotion processing,the left prefrontal cortex and basal ganglia were hy-poactive among depressive patients,whereas the opposite change was found in the limbic lobe.CONCLUSION：Abnormal connections among the prefrontal cortex,basal ganglia and limbic lobe may be involved in the pathophysiology of depressive disorder.
