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.
Still common in developing countries, acute rheumatic fever (ARF) is not only a disease of children and adolescents, but can also occur in adults. At this age, the diagnosis of rheumatic flare-ups can be difficult due to the frequency of other types of joint diseases and the existence of degenerative and dystrophic valve disease. In adults, the initial rheumatic attack is marked by the predominance of joint damage over cardiac damage. However, it is often at this age that rheumatic valve disease is discovered. The revised Jones criteria also find their place in the diagnosis of AAR in adults. Objective: To study the demographic, clinical, and biological characteristics of acute rheumatic fever (ARF) in the General Medicine Department of the Siguiri Prefectural Hospital. Materials and Methods: This descriptive observational study examined the demographic, clinical and biological characteristics of acute rheumatic fever (ARF) at the Siguiri Prefectural Hospital, Guinea, between April 1 and September 31, 2021 according to Jones criteria. The data were collected on a form containing sociodemographic variables (age, sex, profession), rheumatological, cardiac, pulmonary and neurological clinical signs, biological variables and treatment. Results: Figure 1 shows the flow of the hospital frequency of the RAA in the General Medicine Department of the Prefectural Hospital of Siguiri. During the study period, 420 patients were hospitalized, of whom 161 patients had AAR, a frequency of 38.33%. Table 1 shows the distribution of patients diagnosed with ARB, by sociodemographic characteristics. The average age was 44.7 ± 19.78 years and the extremes of 14 and 90 years, the female sex dominated with a ratio of 0.75. The informal sector was in the majority in 45.34% of cases and most were illiterate, i.e. 53.42%. In our study, the incidence was lower during the dry season than during the rainy season, a hot season with high rainfall and humidity, the rainy period was a provider with a peak in August and September. The lifestyle
属性级情感分析作为一种细粒度情感分析方法,目前在许多应用场景中都具有重要作用.然而,随着社交媒体和在线评论的日益广泛以及各类新兴领域的出现,使得跨领域属性级情感分析面临着标签数据不足以及源领域与目标领域文本分布差异等挑战.目前已有许多数据增强方法试图解决这些问题,但现有方法生成的文本仍存在语义不连贯、结构单一以及特征与源领域过于趋同等问题.为了克服这些问题,提出一种基于大语言模型(large language model,LLM)数据增强的跨领域属性级情感分析方法.所提方法利用大模型丰富的语言知识,合理构建针对跨领域属性级别情感分析任务的引导语句,挖掘目标领域与源领域相似文本,通过上下文学习的方式,使用领域关联关键词引导LLM生成目标领域有标签文本数据,用以解决目标领域数据缺乏以及领域特异性问题,从而有效提高跨领域属性级情感分析的准确性和鲁棒性.所提方法在多个真实数据集中进行实验,实验结果表明,该方法可以有效提升基线模型在跨领域属性级情感分析中的表现.
Neurodevelopmental processes represent a finely tuned interplay between genetic and environmental factors,shaping the dynamic landscape of the developing brain.A major component of the developing brain that enables this dynamic is the white matter(WM),known to be affected in neurodevelopmental disorders(NDDs)(Rokach et al.,2024).WM formation is mediated by myelination,a multifactorial process driven by neuro-glia interactions dependent on proper neuronal functionality(Simons and Trajkovic,2006).Another key aspect of neurodevelopmental abnormalities involves neuronal dynamics and function,with recent advances significantly enhancing our understanding of both neuronal and glial mitochondrial function(Devine and Kittler,2018;Rojas-Charry et al.,2021).Energy homeostasis in neurons,attributed largely to mitochondrial function,is critical for proper functionality and interactions with oligodendrocytes(OLs),the cells forming myelin in the brain’s WM.We herein discuss the interplay between these processes and speculate on potential dysfunction in NDDs.
