Aspects Of Classification Research Articles

Hybrid Regularizations for Multi-Aspect Category Sentiment Analysis

Aspect level sentiment classification aims to identify the sentiment polarity towards a particular aspect in a sentence. Previous attention-based methods generate an aspect-specific representation for each aspect and employ it to classify the sentiment polarity. However, normalized attention scores scatter over every word in the sentence, resulting in two issues. First, the attention may inherently introduce noise and downgrade the performance. Second, the opinion words may be “diluted” by other words, while the opinion feature should dominate for sentiment analysis. The issues become more severe in multi-aspect sentences. In this paper, we address the above two issues via hybrid regularizations, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aspect-level</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">task-level regularizations</i> . Concretely, the aspect-level regularizations constrain the attention weights to alleviate noise. Among them, orthogonal regularization is designed for multi-aspect sentences and sparse regularization is for single-aspect sentences. To extract sentiment-dominant features, task-level regularization is proposed by introducing an orthogonal auxiliary task, i.e., aspect category detection. This regularization can allocate task-oriented context information for specific downstream tasks. Extensive experimental results on three public datasets demonstrate the effectiveness of the proposed approach in both single-task and multi-task scenarios.

Verbal morphological category of aspect in the Cantonese language

Verbal morphological category of aspect in the Cantonese language

Aspectual-Actional Functions of Spatially-Orientational Meanings of Preverbs in the Ossetian Language (in Comparison with the Russian Language)

The content of this article continues the topic of aspectual inconsistency, namely, the lack of an imperfective aspect pair in a large group of Ossetian verbs. Two previous articles in thе Bulletin of the NOSU are devoted to this topic [20, pp. 169-181; 21, pр.95-108]. The material presented in the publication indicates that the reasons for the aspectual inconsistency of Ossetian verbs (perfectiva tantum) can be not only aspectual-actional meanings, meanings of certain Aktionsarts, but also the spatial-orientational semantics of certain prefixes, which, when combined with verbs expressing local manifestation actions, allow or do not allow to indicate the perceptual nature of the action (its perception, observability of its development in the middle of the flow, between the beginning and the end). Accordingly, that part of prefixed verbs, the spatial-orientational semantics of which does not allow the designation of perceptuality, does not have an imperfective pair. Meanwhile, it is the semantics of perceptuality that is the core of the specific process meaning of the imperfective verbs in the Ossetian language, as well as in the Russian language. From the point of view of typological similarities and differences, the linguistic facts analyzed in the article are interesting for general and comparative aspectology also because it is in the circle of Ossetian prefixed (limiting) verbs with the semantics of the local manifestation of the action that the formation of the specific process meaning of the imperfective verbs begins similarly to the processes of the emergence of the grammatical category of the form and in modern Russian at the junction of the categories of limiting and a certain direction of action in the group of verbs of movement-displacement. The development of the aspect category in the modern Ossetian language due to the action of such factors as the limit denoted by prefixed verbs, the possibility of expressing the perceptual action in verbs with the semantics of the local manifestation of the action, as well as the implementation of the patterns of typological development proves that the grammatical category of aspect is the property of not only Slavic languages, that in one form or another aspectual semantics is expressed in different languages of the world. Establishing the boundaries of the sphere of Ossetian verbs of only the perfect form in comparison with the Russian language with a detailed description of the reasons for their aspectual inconsistency, of course, can be used as a demanded educational material when studying the topic “Verb aspect as a grammatical category” in educational institutions of all levels, both in the North, and in South Ossetia.

연명의료결정제도 연구 동향 분석: 주제범위 문헌고찰*

This scoping review examines the “System for Decisions to Forgo Life-Sustaining Treatment” in Korea from 2018 to 2022 and is based on an analysis of 285 research studies, published in either domestic or international journals. Of the articles examined, 164 (57.5%) focused on issues related to health science, while 121 (42.5%) addressed ethical and legal aspects of the system. Articles in the former category of health science focused on advance care plans, contemplation on death, decision-making, end-of-life care, biomedical ethics, and psychosocial factors. Articles in the latter category of the ethical and legal aspects of the system focused on legal challenges, the right to self-determination, death with dignity, surrogate decision-making, contemplation of death, biomedical ethics, and legal frameworks. To ensure the system’s stable integration into society during this transitional period, it is crucial to continuously assess its operation in clinical settings. Efforts to establish a societal consensus on dignified end-of-life experiences and to conduct future multidisciplinary research should persist.

AraMAMS: Arabic Multi-Aspect, Multi-Sentiment Restaurants Reviews Corpus for Aspect-Based Sentiment Analysis

The abundance of data on the internet makes analysis a must. Aspect-based sentiment analysis helps extract valuable information from textual data. Because of limited Arabic resources, this paper enriches the Arabic dataset landscape by creating AraMA, the first and largest Arabic multi-aspect corpus. AraMA comprises 10,750 Google Maps reviews for restaurants in Riyadh, Saudi Arabia. It covers four aspect categories—food, environment, service, and price—along with four sentiment polarities: positive, negative, neutral, and conflict. All AraMA reviews are labeled with at least two aspect categories. A second version, named AraMAMS, includes reviews labeled with at least two different sentiments, making it the first Arabic multi-aspect, multi-sentiment dataset. AraMAMS has 5312 reviews covering the same four aspect categories and sentiment polarities. Both corpora were evaluated using naïve biased (NB), support vector classification (SVC), linear SVC, and stochastic gradient descent (SGD) models. In the AraMA corpus, the aspect categories task achieved a 91.41% F1 measure result using the SVC model, while in the AraMAMS corpus, the best F1 measure result for aspect categories task reached 91.70% using the linear SVC model.

The Correlation of Oral Health Knowledge and Affective with Caries Rate in Rural Communities

The results of Riskesdas (Baseline Health Research) by the Indonesian Ministry of Health in 2018 revealed that the caries rate in rural areas was higher than in urban areas. However, the number of villagers receiving dental and oral health care was lower than in urban communities. Karangtengah Village was located in the Baturraden District, Banyumas Regency, which is socio-demographically rural. This study aims to determine the correlation between knowledge and affective of oral health on the caries rate in Karangtengah Village. The study was observational analytical research. Subjects were selected using a purposive sampling technique. The total subjects were 388 residents. The knowledge and affective data were collected using a questionnaire, and the caries rate was scored using DMF-T. Data were analyzed using the Pearson correlation test (SPSS 22). The results revealed that the oral health knowledge and affective of the majority of Karangtengah Village residents were in the moderate category (48.7% and 66.8%, respectively), while the average DMF-T score was 13.12 (very high). The statistical analysis demonstrated no significant correlation between knowledge and DMF-T score and between the affective aspect of oral health and DMF-T score (p0.5). The moderate category of oral health knowledge and affective aspect in rural areas may not have been applied as oral health behaviors contribute to high dental caries rates.

Aspect Based Sentiment Analysis using Various Supervised Classification Techniques: An Overview

The Sentiment Analysis (SA) work is concerned with identifying aspect terms and categories and categorising emotions (positive, negatively, conflict, and neutral) in ratings and reviews. When it comes to subjectivity, it's typical to divide sentences into objective phrases that include accurate information and subjective statements that include express ideas, beliefs, and perspectives on a given topic. Various existing researchers have already done a lot of work in sentiment analysis with various methods, including aspect extraction. This paper proposed a systematic literature analysis of numerous sentiment analysis using supervised and unsupervised classification techniques. We investigate a few features extraction Natural language Processing (NLP) techniques used to identify aspects of machine learning for the detection of sentiment. An extensive experiment analysis, we discuss the findings of the study, challenges of the current and define the problem statement for the future direction

Breaking down linguistic complexities: A structured approach to aspect-based sentiment analysis

Aspect-based sentiment analysis refers to the task of determining the sentiment polarity associated with particular aspects mentioned in a sentence or document. Previous studies have used attention-based neural network models to connect aspect terms with context words, but these models often perform poorly due to limited interaction between aspect terms and opinion words. Furthermore, these models typically focus only on explicitly stated aspect objects, which can be overly restrictive in certain scenarios. Current sentiment analysis methods that rely on aspect categories also often fail to consider the implicit placement of aspect-category information within the context. While existing models may produce strong results, they often lack domain knowledge. To address these issues, this study proposes an Aspect-position and Entity-oriented Knowledge Convolutional Graph (APEKCG) consisting of two modules: the Aspect position-aware module (APA) and the Entity oriented Knowledge Dependency Convolutional Graph (EKDCG). The APA module is designed to integrate aspect-specific sentiment features for sentiment classification by incorporating information about aspect categories into different parts of the context. The EKDCG module incorporates entity-oriented knowledge, dependency labels, and syntactic path using a dependence graph. Experimental results on five benchmarks Natural Language Processing (NLP) datasets of the English language demonstrate the effectiveness of the proposed APEKCG framework. Furthermore, the APEKCG outperformed previous state-of-the-art models with its accuracy, achieving 89.13%, 84.32%, 89.02%, 79.64%, and 90.22% on the MAMS, Laptop, Restaurant, AWARE, and SemEval-15&16 datasets, respectively.

Aspect-Based Sentiment Analysis for Arabic Food Delivery Reviews

Business customers and consumers share their reviews online on social platforms such as Twitter. Therefore, Twitter data sentiment analysis is extremely useful for both research and commercial purposes. Manually analyzing reviews takes a long time and effort, hence, automatic sentiment analysis is required. In this article, we address aspect-based sentiment analysis for Arabic food delivery reviews using several deep learning approaches. In particular, we propose to use Transformer-based models (GigaBERT and AraBERT), Bi-LSTM-CRF, and LSTM, as well as a classical machine learning algorithm (SVM). We also present our dataset of food delivery service reviews, which we collected from Twitter. We annotated them and used them for training and evaluating our approaches. The experiments show that both GigaBERT and AraBERT outperformed the other models in all the tasks. The Transformer-based models received F1-scores of 77% in the aspect terms detection task, 82% in the Aspect category detection task, and 81% in the aspect polarity detection task, gaining 2%, 4%, and 4% over Bi-LSTM-CRF and LSTM in the first, second, and third tasks, respectively.

Evaluating graphing quadratic worksheet on visual thinking classification: A confirmatory analysis

Applying a graphing quadratic worksheet as a medium for learning the concept of a Quadratic Function clearer is an alternative instrument to accommodate the needs of developing students' mathematical visual thinking. In implementing graphing quadratic worksheet should show details of the dominant and recessive visual thinking classification aspects that develop in students. Classification of dominant and recessive aspects of visual thinking needs to be completed to determine stages in improving the worksheet and learning instructions that are applied especially to recessive aspects. Therefore, there is a need to evaluate the factors and trace the classification aspects of visual thinking that developed in students after practicing the graphing quadratic worksheet. The purpose of this research is to determine the categorization aspects of visual thinking in graphing quadratic worksheet items that develop and do not develop in students. Confirmatory factor analysis was employed as a research method on 12 sub-variables from the three classifications of visual thinking. As research data, 93 student records were used. Four main factors were formed as a result of the confirmatory factor analysis procedure, with the top two factors, namely factors 1 and 2, completely representing each aspect of the visual thinking classification and achieving the factor loading significance criteria. The implication is that the variables developed in the graphing quadratic worksheet for each aspect of the visual thinking classification have a strong relationship with the visual thinking ability overall. Enhanced by a cumulative variance value for factors 1 and 2 specifically 56.88% of the total 81.78% for all factors. Thereby it can be said that the categorization aspect of visual thinking that develops in students after implementing a graphing quadratic worksheet is achieved sensibly.

Осетинские глаголы perfectiva tantum: влияние универсальных и идиоэтнических акциональных смыслов (в сопоставлении с русским языком) (Часть вторая)

The article describes that part of the Ossetian verbal lexicon, the aspectual inconsistency of which, namely its single aspectual character (perfectiva tantum), is explained by the presence in the semantics of these verbs of idioethnic actional meanings in combination with certain universal actional meanings. The analysis of the Ossetian linguistic facts in comparison with their functionally translated Russian counterparts is a continuation of the study, the results of which were presented in the Bulletin of the NOSU [10, pp. 169-181]. Determining the boundaries of the sphere of use of verbs of only the perfect form is undoubtedly important in the study of the functional-semantic field (FSF) of the aspectuality of the modern Ossetian language, since it allows you to determine the connection between the modes of action of the Ossetian verb and the aspect category, or rather, to establish the boundaries of the action of the perfect form and imperfect form. The results presented in the article indicate that the perfect aspect in the Ossetian language has a much wider area of action than the imperfect aspect. There is a typologically similar picture in the Russian language, but the factors that result in the uneven size of the spheres of use of prefixed perfect forms and prefixed imperfect forms in the compared languages are different. At the same time, the zone of the Russian imperfect form is much wider than the zone of functioning of the Ossetian imperfect form, expressed only by prefixed formations with the infix -tsæy-, and the zone of the prefixed verbs perfectiva tantum in the Ossetian language is more voluminous, as it seems to us, than in Russian, since the single-species nature of this group of Ossetian verbs are influenced by a greater number of aspectual-actional factors than in modern Russian.

A Self-Attention-Based Multi-Level Fusion Network for Aspect Category Sentiment Analysis

A Self-Attention-Based Multi-Level Fusion Network for Aspect Category Sentiment Analysis

Функционально-грамматический максимализм инфинитивной поэзии Иосифа Бродского

This paper is focused on the phenomenon of functional-grammatical maximalism of the independent infinitive, exemplified by Joseph Brodsky’s poetic texts. The expression in them of three predicative categories of the infinitive – time, person and mood – is analysed. The specific features of this expression include: a unique temporal plane of action, expressed in a literary text by the infinitive through its internal time, i.e. the aspect; a composite image of the subject of an infinitive text, i.e. a meta-subject, in which the Self of the speaker merges with the Self of the listener, represented by both the second and the third person or by the seeming absence of the person; and meditative modality characteristic of Brodsky’s infinitive writing, gravitating towards semantic hyperbolism, i.e. the theme of another, unattainable existence. The author used the functional-grammatical, semantic and contextual analyses, generally applied in poetic grammar. The analysis found a combination of homogeneity, formal minimalism and functional maximalism of the infinitive. In addition, the following lexical-grammatical methods of expressing this phenomenon were identified in Brodsky’s poems: the category of aspect and semantics of infinitives, which contribute to changing the nature of time in a poetic text and to expressing the subject’s attitude to the action of debitive, epistemic and meditative modality; additional temporal indicators in the form of secondary parts of the sentence helping to create a time cycle in the text; personal pronouns indicating the presence of persons in contact with the subject of the infinitive action. The phenomenon analysed in this paper can be explained by the nature of the infinitive, which is indefinite and ambiguous and, therefore, has high functional potential. This makes the topic under discussion interesting for linguists and calls for an in-depth study of this linguistic unit using different types of texts.

A semi supervised approach to Arabic aspect category detection using Bert and teacher-student model.

Aspect-based sentiment analysis tasks are well researched in English. However, we find such research lacking in the context of the Arabic language, especially with reference to aspect category detection. Most of this research is focusing on supervised machine learning methods that require the use of large, labeled datasets. Therefore, the aim of this research is to implement a semi-supervised self-training approach which utilizes a noisy student framework to enhance the capability of a deep learning model, AraBERT v02. The objective is to perform aspect category detection on both the SemEval 2016 hotel review dataset and the Hotel Arabic-Reviews Dataset (HARD) 2016. The four-step framework firstly entails developing a teacher model that is trained on the aspect categories of the SemEval 2016 labeled dataset. Secondly, it generates pseudo labels for the unlabeled HARD dataset based on the teacher model. Thirdly, it creates a noisy student model that is trained on the combined datasets (∼1 million sentences). The aim is to minimize the combined cross entropy loss. Fourthly, an ensembling of both teacher and student models is carried out to enhance the performance of AraBERT. Findings indicate that the ensembled teacher-student model demonstrates a 0.3% improvement in its micro F1 over the initial noisy student implementation, both in predicting the Aspect Categories in the combined datasets. However, it has achieved a 1% increase over the micro F1 of the teacher model. These results outperform both baselines and other deep learning models discussed in the related literature.

Graph Enhanced Transformer for Aspect Category Detection

Graph Enhanced Transformer for Aspect Category Detection

Redispensing of expensive oral anticancer medicines: A practical application.

Increasing use of expensive oral anticancer medicines comes with the downside of a financial and environmental burden, partially caused by unused medication. Returned oral anticancer medicine to the pharmacy could be considered for redispensing providing guaranteed quality. This study aimed to identify and implement quality aspects and criteria for redispensing oral anticancer medicine in daily pharmacy practice. A systematic analysis was conducted to determine the eligibility of oral anticancer medicine for redispensing. Over a one-year period, the number of returned oral anticancer medicine accepted for redispensing was quantified, and the reduction in financial waste and environmental burden calculated based on this assessment. Four categories of quality aspects were identified for determining the eligibility of oral anticancer medicine for redispensing: Product presentation suitability (stability characteristics, storage requirements), physical condition (unopened or opened secondary or primary packaging, visual appearance), authentication (Falsified Medicines Directive, confirmation of initial dispense, recall), and additional aspects (remaining shelf life, period of storage in uncontrolled conditions). A standardized procedure for redispensing was implemented in daily pharmacy practice. During the study period, 10,415 oral anticancer medicine dose units out of 13,210 returns (79%) were accepted for redispensing. The total value of oral anticancer medicine accepted for redispensing was €483,301, accounting for 0.9% of the total value dispensed during this period. Furthermore, the potential reduction in environmental burden was estimated at 1132.1 g of potent active pharmaceutical ingredient. By implementing strict procedures considering all relevant quality aspects, redispensing of oral anticancer medicine can be successfully implemented into daily pharmacy practice, resulting in a significant reduction in financial waste and environmental burden.

Semi-Supervised Model for Aspect Sentiment Detection

Advancements in text representation have produced many deep language models (LMs), such as Word2Vec and recurrent-based LMs. However, there are scarce works that focus on detecting implicit sentiments with a small amount of labelled data because there are many different review areas. Deep learning techniques are suitable to automate the representation learning process. Hence, we proposed a semi-supervised aspect-based sentiment analysis (ABSA) model for online review to predict explicit and implicit sentiment in three domains (laptop, restaurant, and hotel). The datasets of this study, S1 and S2, were obtained from a standard SemEval online competition and Amazon review datasets. The proposed models outperform the previous baseline models regarding the F1-score of aspect category detection and accuracy of sentiment detection. This study finds more relevant aspects and accurate sentiment for ABSA by developing more stable and robust models. The accuracy of sentiment detection is 84.87% in the restaurant domain on the first dataset. For the second dataset, the proposed method achieved 84.43% in the laptop domain, 85.21% in the restaurant domain, and 85.57% in the hotel domain. The novelty is the proposed new semi-supervised model for aspect sentiment detection with embedded aspect inspired by the encoder–decoder architecture in the neural machine translation (NMT) model.

SBERTiment: A New Pipeline to Solve Aspect Based Sentiment Analysis in the Zero-Shot Setting

The field of Natural Language Processing is gaining increased attention for the Aspect Based Sentiment Analysis task due to its ability to provide fine-grained information. This paper introduces SBERTiment, a novel approach to perform Aspect Based Sentiment Analysis. The method extracts relevant topics along with their sentiments from the input text by using a 2-step pipeline. In the first step, a token classification model is used to identify the relevant aspect terms and their sentiments. In the second step, a Sentence-BERT embedding model maps each aspect term to a predefined aspect category. Our approach has been tested on benchmark datasets and has achieved scores that are comparable to the best-performing methods. The pipeline is also able to perform zero-shot classification, which means it can extract information in unseen domains without additional training. When evaluated on a dataset with unseen aspect categories, SBERTiment achieved the best score among benchmark approaches.

Механизмы концептуализации аспектуально-темпоральных значений грамматических конструкций

The study is aimed at attempting to reconstruct the cognitive mechanisms involved in the conceptualisation of meanings of nuclear tense-aspect grammatical constructions of the English language. The scientific novelty lies in identifying the integrative image schemas reflecting the deep mechanisms of conceptualisation of the categories of aspect and tense in the formation of meanings of grammatical units. As a result, it has been shown that different levels of abstraction are involved in the process of grammatical conceptualisation of aspectual-temporal characteristics. The initial stage of metaphorisation is represented by the basic system-forming conceptual metaphors, characterised by a high degree of abstraction. Reflecting sensory thinking, the metaphors record the interaction of concepts such as time, space, orientation and movement. The second stage of abstraction, reflecting more detailed logical thinking, is represented by conceptual schemes and their constituent components, presented in the paper in the form of graphic image schemas. The conceptual schemes reflect the interaction and features of a systemic distribution of aspectual-temporal characteristics represented in the meanings of verb forms.

Preverbs: Their syntax and semantics in West Africa

Preverbs are positionally-delimited grammatical forms that remain understudied. We examine their semantic classes in West Benue Congo (WBC) and its minor language Emai, which until recently was undocumented. Preverb classes in Emai display a subset of semantic categories identified in Dixon (1991, 2006, 2010) and Nuyts (2001, 2005, 2006, 2016). There are eight semantic classes for 30 odd preverb forms. They are apportioned according to their qualitative or quantitative character. Preverbs do not include traditional auxiliary categories of aspect, tense, and modality, which exhibit distinct diachronic and synchronic character. Common to preverbs is their orientation to grammatical subject, rather than utterance speaker. Members of each class ascribe a property to clausal subject. Preliminary evidence suggests that preverbs of similar semantic character exist in other West African languages.