Schizophrenia is a severe mental illness which is associated with significant consequences for both the patients and their relatives. Due to chronicity of the illness, the relatives of patients of schizophrenia have t...Schizophrenia is a severe mental illness which is associated with significant consequences for both the patients and their relatives. Due to chronicity of the illness, the relatives of patients of schizophrenia have to bear the main brunt of the illness. Studies across the world have evaluated various aspects of caregiving and caregivers such as burden, coping, quality of life, social support, expressed emotions, and psychological morbidity. In general the research has looked at caregiving as a negative phenomenon, however, now it is increasingly recognised that caregiving is not only associated with negative consequences only, also experience subjective gains and satisfaction. This review focus on the conceptual issues, instruments available to assess the positive aspects of caregiving and the various correlates of positive aspects of caregiving reported in relation to schizophrenia. The positive aspect of caregiving has been variously measured as positive caregiving experience, caregiving satisfaction, caregiving gains and finding meaning through caregiving scale and positive aspects of caregiving experience. Studies suggests that caregivers of patients with schizophrenia and psychotic disorders experience caregiving gains(in the form of becoming more sensitive to persons with disabilities, clarity about their priorities in life and a greater sense of inner strength), experience good aspects of relationship with the patient, do have personal positive experiences. Some of the studies suggest that those who experience greater negative caregiving experience also do experience positive caregiving experience.展开更多
Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,p...Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,prior methodologies widely utilize either word embedding or tree-based rep-resentations.Meanwhile,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss.Generally,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence.Besides,the tree-based structure conserves the grammatical and logical dependencies of context.In addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted sentence.Therefore,knowledge of the position-oriented information of words in a sentence has been considered significant.In this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or not.In the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification process.In this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position informa-tion.These three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their polarities.In addition,these terms have been further filtered with the attention mechanism,which determines the most significant words.The empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard datasets.The experimental results represent our approach outperforms in the F1 measure with an overall achievement of 94%in identifying aspects and 92%in the task of sentiment classification.展开更多
文摘Schizophrenia is a severe mental illness which is associated with significant consequences for both the patients and their relatives. Due to chronicity of the illness, the relatives of patients of schizophrenia have to bear the main brunt of the illness. Studies across the world have evaluated various aspects of caregiving and caregivers such as burden, coping, quality of life, social support, expressed emotions, and psychological morbidity. In general the research has looked at caregiving as a negative phenomenon, however, now it is increasingly recognised that caregiving is not only associated with negative consequences only, also experience subjective gains and satisfaction. This review focus on the conceptual issues, instruments available to assess the positive aspects of caregiving and the various correlates of positive aspects of caregiving reported in relation to schizophrenia. The positive aspect of caregiving has been variously measured as positive caregiving experience, caregiving satisfaction, caregiving gains and finding meaning through caregiving scale and positive aspects of caregiving experience. Studies suggests that caregivers of patients with schizophrenia and psychotic disorders experience caregiving gains(in the form of becoming more sensitive to persons with disabilities, clarity about their priorities in life and a greater sense of inner strength), experience good aspects of relationship with the patient, do have personal positive experiences. Some of the studies suggest that those who experience greater negative caregiving experience also do experience positive caregiving experience.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.3418].
文摘Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,prior methodologies widely utilize either word embedding or tree-based rep-resentations.Meanwhile,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss.Generally,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence.Besides,the tree-based structure conserves the grammatical and logical dependencies of context.In addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted sentence.Therefore,knowledge of the position-oriented information of words in a sentence has been considered significant.In this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or not.In the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification process.In this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position informa-tion.These three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their polarities.In addition,these terms have been further filtered with the attention mechanism,which determines the most significant words.The empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard datasets.The experimental results represent our approach outperforms in the F1 measure with an overall achievement of 94%in identifying aspects and 92%in the task of sentiment classification.
