Adolescents are considered one of the most vulnerable groups affected by suicide.Rapid changes in adolescents’physical and mental states,as well as in their lives,significantly and undeniably increase the risk of sui...Adolescents are considered one of the most vulnerable groups affected by suicide.Rapid changes in adolescents’physical and mental states,as well as in their lives,significantly and undeniably increase the risk of suicide.Psychological,social,family,individual,and environmental factors are important risk factors for suicidal behavior among teenagers and may contribute to suicide risk through various direct,indirect,or combined pathways.Social-emotional learning is considered a powerful intervention measure for addressing the crisis of adolescent suicide.When deliberately cultivated,fostered,and enhanced,selfawareness,self-management,social awareness,interpersonal skills,and responsible decision-making,as the five core competencies of social-emotional learning,can be used to effectively target various risk factors for adolescent suicide and provide necessary mental and interpersonal support.Among numerous suicide intervention methods,school-based interventions based on social-emotional competence have shown great potential in preventing and addressing suicide risk factors in adolescents.The characteristics of school-based interventions based on social-emotional competence,including their appropriateness,necessity,cost-effectiveness,comprehensiveness,and effectiveness,make these interventions an important means of addressing the crisis of adolescent suicide.To further determine the potential of school-based interventions based on social-emotional competence and better address the issue of adolescent suicide,additional financial support should be provided,the combination of socialemotional learning and other suicide prevention programs within schools should be fully leveraged,and cooperation between schools and families,society,and other environments should be maximized.These efforts should be considered future research directions.展开更多
Afamous psychologist or researcher,Daniel Goleman,gave a theory on the importance of Emotional Intelligence for the success of an individual’s life.Daniel Goleman quoted in the research that“The contribution of an i...Afamous psychologist or researcher,Daniel Goleman,gave a theory on the importance of Emotional Intelligence for the success of an individual’s life.Daniel Goleman quoted in the research that“The contribution of an individual’s Intelligence Quotient(IQ)is only 20%for their success,the remaining 80%is due to Emotional Intelligence(EQ)”.However,in the absence of a reliable technique for EQ evaluation,this factor of overall intelligence is ignored in most of the intelligence evaluation mechanisms.This research presented an analysis based on basic statistical tools along with more sophisticated deep learning tools.The proposed cross intelligence evaluation uses two different aspects which are similar,i.e.,EQ and SQ to estimate EQ by using a trained model over SQ Dataset.This presented analysis ensures the resemblance between the Emotional and Social Intelligence of an Individual.The research authenticates the results over standard statistical tools and is practically inspected by deep learning tools.Trait Emotional Intelligence Questionnaire-Short Form(TEIQue-SF)and Social IQ dataset are deployed over aMulti-layered Long-Short TermMemory(M-LSTM)based deep learning model for accessing the resemblance between EQ and SQ.The M-LSTM based trained deep learning model registered,the high positive resemblance between Emotional and Social Intelligence and concluded that the resemblance factor between these two is more than 99.84%.This much resemblance allows future researchers to calculate human emotional intelligence with the help of social intelligence.This flexibility also allows the use of Big Data available on social networks,to calculate the emotional intelligence of an individual.展开更多
Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks.Most existing methods are based on deep learning models,facing challenges such as complex structur...Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks.Most existing methods are based on deep learning models,facing challenges such as complex structures and too many hyperparameters.To meet these challenges,in this paper,we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach(RoBERTa)and p-norm Broad Learning(p-BL).Specifically,there are mainly three contributions in this paper.Firstly,we fine-tune the RoBERTa to adapt it to the task of negative emotion classification.Then,we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence vectors.Secondly,we adopt p-BL to construct a classifier and then predict negative emotions of texts using the classifier.Compared with deep learning models,p-BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be trained.Moreover,it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of p.Thirdly,we conduct extensive experiments on the public datasets,and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets.展开更多
文摘Adolescents are considered one of the most vulnerable groups affected by suicide.Rapid changes in adolescents’physical and mental states,as well as in their lives,significantly and undeniably increase the risk of suicide.Psychological,social,family,individual,and environmental factors are important risk factors for suicidal behavior among teenagers and may contribute to suicide risk through various direct,indirect,or combined pathways.Social-emotional learning is considered a powerful intervention measure for addressing the crisis of adolescent suicide.When deliberately cultivated,fostered,and enhanced,selfawareness,self-management,social awareness,interpersonal skills,and responsible decision-making,as the five core competencies of social-emotional learning,can be used to effectively target various risk factors for adolescent suicide and provide necessary mental and interpersonal support.Among numerous suicide intervention methods,school-based interventions based on social-emotional competence have shown great potential in preventing and addressing suicide risk factors in adolescents.The characteristics of school-based interventions based on social-emotional competence,including their appropriateness,necessity,cost-effectiveness,comprehensiveness,and effectiveness,make these interventions an important means of addressing the crisis of adolescent suicide.To further determine the potential of school-based interventions based on social-emotional competence and better address the issue of adolescent suicide,additional financial support should be provided,the combination of socialemotional learning and other suicide prevention programs within schools should be fully leveraged,and cooperation between schools and families,society,and other environments should be maximized.These efforts should be considered future research directions.
基金Researchers would like to thank the Deanship of Scientific Research,Qassim University for funding the publication of this project.
文摘Afamous psychologist or researcher,Daniel Goleman,gave a theory on the importance of Emotional Intelligence for the success of an individual’s life.Daniel Goleman quoted in the research that“The contribution of an individual’s Intelligence Quotient(IQ)is only 20%for their success,the remaining 80%is due to Emotional Intelligence(EQ)”.However,in the absence of a reliable technique for EQ evaluation,this factor of overall intelligence is ignored in most of the intelligence evaluation mechanisms.This research presented an analysis based on basic statistical tools along with more sophisticated deep learning tools.The proposed cross intelligence evaluation uses two different aspects which are similar,i.e.,EQ and SQ to estimate EQ by using a trained model over SQ Dataset.This presented analysis ensures the resemblance between the Emotional and Social Intelligence of an Individual.The research authenticates the results over standard statistical tools and is practically inspected by deep learning tools.Trait Emotional Intelligence Questionnaire-Short Form(TEIQue-SF)and Social IQ dataset are deployed over aMulti-layered Long-Short TermMemory(M-LSTM)based deep learning model for accessing the resemblance between EQ and SQ.The M-LSTM based trained deep learning model registered,the high positive resemblance between Emotional and Social Intelligence and concluded that the resemblance factor between these two is more than 99.84%.This much resemblance allows future researchers to calculate human emotional intelligence with the help of social intelligence.This flexibility also allows the use of Big Data available on social networks,to calculate the emotional intelligence of an individual.
基金This work was partially supported by the National Natural Science Foundation of China(No.61876205)the Ministry of Education of Humanities and Social Science Project(No.19YJAZH128)+1 种基金the Science and Technology Plan Project of Guangzhou(No.201804010433)the Bidding Project of Laboratory of Language Engineering and Computing(No.LEC2017ZBKT001).
文摘Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks.Most existing methods are based on deep learning models,facing challenges such as complex structures and too many hyperparameters.To meet these challenges,in this paper,we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach(RoBERTa)and p-norm Broad Learning(p-BL).Specifically,there are mainly three contributions in this paper.Firstly,we fine-tune the RoBERTa to adapt it to the task of negative emotion classification.Then,we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence vectors.Secondly,we adopt p-BL to construct a classifier and then predict negative emotions of texts using the classifier.Compared with deep learning models,p-BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be trained.Moreover,it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of p.Thirdly,we conduct extensive experiments on the public datasets,and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets.
