High-aspect-ratio metallic surface microstructures are increasingly demanded in breakthrough applications,such as high-performance heat transfer enhancement and surface plasmon devices.However,the fast and cost-effect...High-aspect-ratio metallic surface microstructures are increasingly demanded in breakthrough applications,such as high-performance heat transfer enhancement and surface plasmon devices.However,the fast and cost-effective fabrication of high-aspect-ratio microstructures on metallic surfaces remains challenging for existing techniques.This study proposes a novel cutting-based process,namely elliptical vibration chiseling(EV-chiseling),for the high-efficiency texturing of surface microstructures with an ultrahigh aspect ratio.Unlike conventional cutting,EV-chiseling superimposes a microscale EV on a backward-moving tool.The tool chisels into the material in each vibration cycle to generate an upright chip with a high aspect ratio through material deformation.Thanks to the tool’s backward movement,the chip is left on the material surface to form a microstructure rather than falling off.Since one microstructure is generated in one vibration cycle,the process can be highly efficient using ultrafast(>1 kHz)tool vibration.A finite element analysis model is established to explore the process mechanics of EV-chiseling.Next,a mechanistic model of the microstructured surface generation is developed to describe the microstructures’aspect ratio dependency on the process parameters.Then,surface texturing tests are performed on copper to verify the efficacy of EV-chiseling.Uniformed micro ribs with a spacing of 1–10μm and an aspect ratio of 2–5 have been successfully textured on copper.Compared with the conventional EV-cutting that uses a forward-moving tool,EV-chiseling can improve the aspect ratio of textured microstructure by up to 40 times.The experimental results also verify the accuracy of the developed surface generation model of microstructures.Finally,the effects of elliptical trajectory,depth of cut,tool shape,and tool edge radius on the surface generation of micro ribs have been discussed.展开更多
This study aims to determine the environmental aspects and impacts of the phase relating to the operation of Manantali’s hydroelectric structures and facilities according to the requirements of the ISO 14001 version ...This study aims to determine the environmental aspects and impacts of the phase relating to the operation of Manantali’s hydroelectric structures and facilities according to the requirements of the ISO 14001 version 2015 standard. To do this, the mapping of the different work areas made it possible to identify all the activities within the framework of the farm. Based on the mapping, environmental measurements made including noise level, brightness, electric and magnetic fields, total particles, PM10, PM2.5, PM1 showed the work areas with the exposure limit values exceeded. The inventories of the waste produced show eighteen (18) types of waste, 67% of which are special industrial waste (SIW), 28% are ordinary industrial waste (OIW) and 5% are inert industrial waste (IIW). The identification and assessment of environmental aspects and impacts made it possible to determine sixteen (16) positive and negative significant environmental aspects (ESAs). The positive AES must be maintained, and for the negative ones, mitigation and mitigation measures must be put in place in order to manage them effectively. This will ultimately improve environmental management in the operation of hydroelectric structures and facilities.展开更多
The aspect ratio of the structure has a significant impact on the overall stability of the ultra high-rise building. A large aspect ratio of the structure increases the risk of overturning and reduces the lateral stif...The aspect ratio of the structure has a significant impact on the overall stability of the ultra high-rise building. A large aspect ratio of the structure increases the risk of overturning and reduces the lateral stiffness of the structure, leading to significant tensile and compressive stresses in the isolated bearings. To study the effect of aspect ratio on the seismic response and overturning resistance of a new staggered story isolated structure, three models with different aspect ratios were established. Nonlinear time-history analysis of the three models was conducted using ETABS finite element software. The results indicate that the overturning moment and overturning resistance moment of the superstructure in the new staggered story isolated structure increase with an increasing aspect ratio. However, the increase in the overturning moment of the superstructure is much greater than the increase in the overturning resistance moment, resulting in a decrease in the overturning resistance ratio of the superstructure with an increasing aspect ratio. The overturning moment and overturning resistance moment of the substructure in the new staggered story isolated structure decrease with an increasing aspect ratio. However, the decrease in the overturning moment of the substructure is greater than the decrease in the overturning resistance moment, leading to an increase in the overturning resistance ratio of the substructure with an increasing aspect ratio. The decrease in the overturning resistance ratio of the superstructure in the new staggered story isolated structure is much greater than the increase in the overturning resistance ratio of the substructure. Therefore, as the aspect ratio of the overall structure increases, the overturning resistance ratio of the superstructure and the entire structure decreases.展开更多
Objective: Describe the psychosocial aspects of male infertility at the hospital of the Sino-Guinean Friendship. Patients and method: It is a prospective study of a descriptive type covering a period of 6 months. The ...Objective: Describe the psychosocial aspects of male infertility at the hospital of the Sino-Guinean Friendship. Patients and method: It is a prospective study of a descriptive type covering a period of 6 months. The study covered 17 patients, all received for a desire to conceive after at least one year of regular sexual intercourse without contraception. The data were collected from patient interviews using a pre-established questionnaire. Results: The average age of the patients was 32.07 years with extremes of 23 years and 42 years. During this study, 64.70% of patients were no longer participating in community ceremonies. The patients’ relationships with their spouse and family deteriorated in 52.94% and 47.06%, respectively. Conversely, relations with the family of origin remained unchanged in 70.59 percent of cases. The reduction in economic activity was by 13 patients (76.48%). Conclusion: Male infertility causes a real psychic earthquake in men with its corollaries of negative feelings. The rather complex moral repercussions of male infertility affect not only the individual, his/her partner, and family, but also economic activity.展开更多
Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-base...Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.展开更多
Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspe...Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.展开更多
For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural net...For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.展开更多
The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to st...The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.展开更多
Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp Store.These online reviews about products are also becoming essential for consumers and companies as well....Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp Store.These online reviews about products are also becoming essential for consumers and companies as well.Consumers rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and services.These reviews are also a very precious source of information for requirement engineers.But companies and consumers are not very satisfied with the overall sentiment;they like fine-grained knowledge about consumer reviews.Owing to this,many researchers have developed approaches for aspect-based sentiment analysis.Most existing approaches concentrate on explicit aspects to analyze the sentiment,and only a few studies rely on capturing implicit aspects.This paper proposes a Keywords-Based Aspect Extraction method,which captures both explicit and implicit aspects.It also captures opinion words and classifies the sentiment about each aspect.We applied semantic similarity-basedWordNet and SentiWordNet lexicon to improve aspect extraction.We used different collections of customer reviews for experiment purposes,consisting of eight datasets over seven domains.We compared our approach with other state-of-the-art approaches,including Rule Selection using Greedy Algorithm(RSG),Conditional Random Fields(CRF),Rule-based Extraction(RubE),and Double Propagation(DP).Our results have shown better performance than all of these approaches.展开更多
Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely related.Howeve...Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely related.However,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline model.Pipeline solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE error.These methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of APC.Thus,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a singlemodel.This paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text representation.The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its context.Moreover,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training dataset.This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA.Extensive experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques.Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation word embedding-based method(FastText)on the combined dataset.展开更多
People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various ...People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.展开更多
With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after ...With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.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
Background:Social distancing may affect athletes,training,causing negative effects on mental and physical health.Objective:This study therefore aimed to characterize the perception of Brazilian athletes about their ph...Background:Social distancing may affect athletes,training,causing negative effects on mental and physical health.Objective:This study therefore aimed to characterize the perception of Brazilian athletes about their physical and psychosocial aspects,sleep quality and coping strategies during the quarantine of the coronavirus disease 2019(COVID-19)pandemic.Methods:This was a cross-sectional study with online survey,performed with Brazilian athletes(amateur and professional)over 18 years.The main outcomes measures assessed were physical and psychosocial aspects,sleep quality and coping strategies.Results:A total of 214 athletes were included.The average weekly hours of training during the quarantine was 4.71±3.71 h,of which 64.5%athletes(138/214)were oriented by medical staff during training.For 52.8%(113/214)of athletes,training intensity during the quarantine was different/very different from the intensity before the quarantine.79.4%athletes(170/214)reported moderate to extreme difficulties in keeping the same level of training during the quarantine.77.1%athletes(165/214)had moderate to extreme anxiety and each of the athletes had concern about his or her athletic career future,including return to the sport.72.9%athletes(156/214)reported change in sleep schedule during the quarantine period.Conclusion:The quarantine period during COVID-19 pandemic negatively affected the athlete^perception about training routine,since athletes reported reduction in training hours and training intensity.Overall,the athletes reported that they were moderately to extremely anxious.They also had concerns about their career in the future,as well as concerns regarding return to sport.展开更多
Introduction: Biermer’s disease is an autoimmune disease characterized by a lack of absorption of vitamin B12 in connection with the production of antibodies (A) destroying the intrinsic factor (IF) which allows the ...Introduction: Biermer’s disease is an autoimmune disease characterized by a lack of absorption of vitamin B12 in connection with the production of antibodies (A) destroying the intrinsic factor (IF) which allows the absorption of vitamin B12 (cobalamin). These clinical manifestations are polymorphic and severe in our context. The objective of this work is to identify the epidemiological-clinical, therapeutic and evolutionary characteristics of Biermer’s disease in Guinean population. Materials and methods: This was a retrospective of patient files followed for Biermer’s disease at the internal medicine department of Donka National Hospital from January 2012 to December 2021. Results: Eight patients were included including 5 women and 3 men. The average age of the patients was 48 years old. The diagnostic delay was 3.6 years on average. All our patients had bioclinical anemia (8 cases, i.e. 100%) followed by epigastralgia in 4 cases (50%), neurological damage such as sensitive polyneuropathy in 3 cases (37.5%). Four patients had acquired melanoderma (50%). Hypovitaminosis B12 was found in 4 patients. The myelogram performed in three patients (37.5%) found medullary megaloblastosis. One patient had Hashimoto’s disease associated with Biermer’s disease in endoscopy, (FOGD) found fundica trophy on macroscopy in 4 cases (50%). Treatment consisted of B12 vitamin therapy in all cases with a favorable clinical and biological outcome. Conclusion: Biermer’s disease remains common in Africa and is characterized at a younger age in addition to the severity of clinical and biological manifestations. The care consists of taking vitamin B12 which remains accessible in our context.展开更多
基金support for this research provided by the National Natural Science Foundation of China(Grant No.52105458)Beijing Natural Science Foundation(Grant No.3222009)+1 种基金Huaneng Group Science and Technology Research Project(No:HNKJ22-H105)China Postdoctoral Science Foundation(Grant No.2022M711807)。
文摘High-aspect-ratio metallic surface microstructures are increasingly demanded in breakthrough applications,such as high-performance heat transfer enhancement and surface plasmon devices.However,the fast and cost-effective fabrication of high-aspect-ratio microstructures on metallic surfaces remains challenging for existing techniques.This study proposes a novel cutting-based process,namely elliptical vibration chiseling(EV-chiseling),for the high-efficiency texturing of surface microstructures with an ultrahigh aspect ratio.Unlike conventional cutting,EV-chiseling superimposes a microscale EV on a backward-moving tool.The tool chisels into the material in each vibration cycle to generate an upright chip with a high aspect ratio through material deformation.Thanks to the tool’s backward movement,the chip is left on the material surface to form a microstructure rather than falling off.Since one microstructure is generated in one vibration cycle,the process can be highly efficient using ultrafast(>1 kHz)tool vibration.A finite element analysis model is established to explore the process mechanics of EV-chiseling.Next,a mechanistic model of the microstructured surface generation is developed to describe the microstructures’aspect ratio dependency on the process parameters.Then,surface texturing tests are performed on copper to verify the efficacy of EV-chiseling.Uniformed micro ribs with a spacing of 1–10μm and an aspect ratio of 2–5 have been successfully textured on copper.Compared with the conventional EV-cutting that uses a forward-moving tool,EV-chiseling can improve the aspect ratio of textured microstructure by up to 40 times.The experimental results also verify the accuracy of the developed surface generation model of microstructures.Finally,the effects of elliptical trajectory,depth of cut,tool shape,and tool edge radius on the surface generation of micro ribs have been discussed.
文摘This study aims to determine the environmental aspects and impacts of the phase relating to the operation of Manantali’s hydroelectric structures and facilities according to the requirements of the ISO 14001 version 2015 standard. To do this, the mapping of the different work areas made it possible to identify all the activities within the framework of the farm. Based on the mapping, environmental measurements made including noise level, brightness, electric and magnetic fields, total particles, PM10, PM2.5, PM1 showed the work areas with the exposure limit values exceeded. The inventories of the waste produced show eighteen (18) types of waste, 67% of which are special industrial waste (SIW), 28% are ordinary industrial waste (OIW) and 5% are inert industrial waste (IIW). The identification and assessment of environmental aspects and impacts made it possible to determine sixteen (16) positive and negative significant environmental aspects (ESAs). The positive AES must be maintained, and for the negative ones, mitigation and mitigation measures must be put in place in order to manage them effectively. This will ultimately improve environmental management in the operation of hydroelectric structures and facilities.
文摘The aspect ratio of the structure has a significant impact on the overall stability of the ultra high-rise building. A large aspect ratio of the structure increases the risk of overturning and reduces the lateral stiffness of the structure, leading to significant tensile and compressive stresses in the isolated bearings. To study the effect of aspect ratio on the seismic response and overturning resistance of a new staggered story isolated structure, three models with different aspect ratios were established. Nonlinear time-history analysis of the three models was conducted using ETABS finite element software. The results indicate that the overturning moment and overturning resistance moment of the superstructure in the new staggered story isolated structure increase with an increasing aspect ratio. However, the increase in the overturning moment of the superstructure is much greater than the increase in the overturning resistance moment, resulting in a decrease in the overturning resistance ratio of the superstructure with an increasing aspect ratio. The overturning moment and overturning resistance moment of the substructure in the new staggered story isolated structure decrease with an increasing aspect ratio. However, the decrease in the overturning moment of the substructure is greater than the decrease in the overturning resistance moment, leading to an increase in the overturning resistance ratio of the substructure with an increasing aspect ratio. The decrease in the overturning resistance ratio of the superstructure in the new staggered story isolated structure is much greater than the increase in the overturning resistance ratio of the substructure. Therefore, as the aspect ratio of the overall structure increases, the overturning resistance ratio of the superstructure and the entire structure decreases.
文摘Objective: Describe the psychosocial aspects of male infertility at the hospital of the Sino-Guinean Friendship. Patients and method: It is a prospective study of a descriptive type covering a period of 6 months. The study covered 17 patients, all received for a desire to conceive after at least one year of regular sexual intercourse without contraception. The data were collected from patient interviews using a pre-established questionnaire. Results: The average age of the patients was 32.07 years with extremes of 23 years and 42 years. During this study, 64.70% of patients were no longer participating in community ceremonies. The patients’ relationships with their spouse and family deteriorated in 52.94% and 47.06%, respectively. Conversely, relations with the family of origin remained unchanged in 70.59 percent of cases. The reduction in economic activity was by 13 patients (76.48%). Conclusion: Male infertility causes a real psychic earthquake in men with its corollaries of negative feelings. The rather complex moral repercussions of male infertility affect not only the individual, his/her partner, and family, but also economic activity.
文摘Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.
文摘Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.
基金Supported by the National Key Research and Development Program of China(No.2018YFB1702601).
文摘For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.
文摘The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.
文摘Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp Store.These online reviews about products are also becoming essential for consumers and companies as well.Consumers rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and services.These reviews are also a very precious source of information for requirement engineers.But companies and consumers are not very satisfied with the overall sentiment;they like fine-grained knowledge about consumer reviews.Owing to this,many researchers have developed approaches for aspect-based sentiment analysis.Most existing approaches concentrate on explicit aspects to analyze the sentiment,and only a few studies rely on capturing implicit aspects.This paper proposes a Keywords-Based Aspect Extraction method,which captures both explicit and implicit aspects.It also captures opinion words and classifies the sentiment about each aspect.We applied semantic similarity-basedWordNet and SentiWordNet lexicon to improve aspect extraction.We used different collections of customer reviews for experiment purposes,consisting of eight datasets over seven domains.We compared our approach with other state-of-the-art approaches,including Rule Selection using Greedy Algorithm(RSG),Conditional Random Fields(CRF),Rule-based Extraction(RubE),and Double Propagation(DP).Our results have shown better performance than all of these approaches.
文摘Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely related.However,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline model.Pipeline solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE error.These methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of APC.Thus,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a singlemodel.This paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text representation.The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its context.Moreover,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training dataset.This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA.Extensive experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques.Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation word embedding-based method(FastText)on the combined dataset.
文摘People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.
文摘With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.
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.
文摘Background:Social distancing may affect athletes,training,causing negative effects on mental and physical health.Objective:This study therefore aimed to characterize the perception of Brazilian athletes about their physical and psychosocial aspects,sleep quality and coping strategies during the quarantine of the coronavirus disease 2019(COVID-19)pandemic.Methods:This was a cross-sectional study with online survey,performed with Brazilian athletes(amateur and professional)over 18 years.The main outcomes measures assessed were physical and psychosocial aspects,sleep quality and coping strategies.Results:A total of 214 athletes were included.The average weekly hours of training during the quarantine was 4.71±3.71 h,of which 64.5%athletes(138/214)were oriented by medical staff during training.For 52.8%(113/214)of athletes,training intensity during the quarantine was different/very different from the intensity before the quarantine.79.4%athletes(170/214)reported moderate to extreme difficulties in keeping the same level of training during the quarantine.77.1%athletes(165/214)had moderate to extreme anxiety and each of the athletes had concern about his or her athletic career future,including return to the sport.72.9%athletes(156/214)reported change in sleep schedule during the quarantine period.Conclusion:The quarantine period during COVID-19 pandemic negatively affected the athlete^perception about training routine,since athletes reported reduction in training hours and training intensity.Overall,the athletes reported that they were moderately to extremely anxious.They also had concerns about their career in the future,as well as concerns regarding return to sport.
文摘Introduction: Biermer’s disease is an autoimmune disease characterized by a lack of absorption of vitamin B12 in connection with the production of antibodies (A) destroying the intrinsic factor (IF) which allows the absorption of vitamin B12 (cobalamin). These clinical manifestations are polymorphic and severe in our context. The objective of this work is to identify the epidemiological-clinical, therapeutic and evolutionary characteristics of Biermer’s disease in Guinean population. Materials and methods: This was a retrospective of patient files followed for Biermer’s disease at the internal medicine department of Donka National Hospital from January 2012 to December 2021. Results: Eight patients were included including 5 women and 3 men. The average age of the patients was 48 years old. The diagnostic delay was 3.6 years on average. All our patients had bioclinical anemia (8 cases, i.e. 100%) followed by epigastralgia in 4 cases (50%), neurological damage such as sensitive polyneuropathy in 3 cases (37.5%). Four patients had acquired melanoderma (50%). Hypovitaminosis B12 was found in 4 patients. The myelogram performed in three patients (37.5%) found medullary megaloblastosis. One patient had Hashimoto’s disease associated with Biermer’s disease in endoscopy, (FOGD) found fundica trophy on macroscopy in 4 cases (50%). Treatment consisted of B12 vitamin therapy in all cases with a favorable clinical and biological outcome. Conclusion: Biermer’s disease remains common in Africa and is characterized at a younger age in addition to the severity of clinical and biological manifestations. The care consists of taking vitamin B12 which remains accessible in our context.
