Aspect-based sentiment analysis is the most important research topic conducted to extract and categorize aspect-terms from online reviews. Recent efforts have shown that topic modelling is vigorously used for this task. In this paper, we integrated word embedding into collapsed Gibbs sampling in Latent Dirichlet Allocation (LDA). Specifically, the conditional distribution in the topic model is improved using the word embedding model that was trained against (customer review) training dataset. Semantic similarity (cosine measure) was leveraged to distribute the aspect-terms to their related aspect-category cognitively. The experiment was conducted to extract and categorize the aspect terms from SemEval 2014 dataset.
Autorías: Naji Kadhim Ali, Saleh Radhi Amish, Wameedh Shamil Kamil. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 4, 2022. Artículo de Revista en Dialnet.
Multimedia is one of the most important elements of modern educational media and must be used in educational websites in order to disseminate knowledge on a large scale and should be used to provide scientific information to all, as the current research tried to explore the possibilities of employing them in the design of educational websites and highlight their role in promoting the scientific aspects of the user. This study included four axes, the first of which was devoted to the introduction which includes the problem of research, its importance, objectives and its objective, temporal and spatial limitations, which were limited to the study of the main pages of Arabic educational websites published in 2019. The second axis cont
... Show MoreProblem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a
... Show MoreThis study investigates the phonotactics of English obstruent clusters in the word-final position from a markedness theory perspective among Iraqi EFL College Students whose native language, Arabic, prefers only two-member word-final obstruent cluster as a maximum. The markedness of clusters is measured depending on Iraqi EFL College Students’ utilization of the simplification strategies. This study tries to answer whether or not word-final obstruent clusters are marked or unmarked for Iraqi EFL College Students, and whether or not the markedness of the obstruent cluster increases as to its length. In order to answer these questions, a test has been distributed among 60 Iraqi EFL Fourth-Year College students, Department of English, Colleg
... Show MoreThe charge species plays a vital role in changing the field in direct current discharge (DC). This article introduces a numerical modeling in one dimension of the inner electrode diameter of oxygen-fed negative corona discharge in coaxial electrodes geometry. The properties of negative corona plasma in a concentric cylindrical electrodes (wire-cylinder) were simulated by COMSOL Multiphysics software. Various diameters of negative corona electrode, namely 0.01, 0.025, 0.05, 0.075, and 0.125 mm, were applied, where the diameter of the outer cylindrical electrode was taken as 15 mm. The model was run at atmospheric pressure and the applied negative voltage was
-10 KV. Moreover, oxygen gas was used to fill the inter-electrodes dist
Building numerical reservoir simulation model with a view to model actual case requires enormous amount of data and information. Such modeling and simulation processes normally require lengthy time and different sets of field data and experimental tests that are usually very expensive. In addition, the availability, quality and accessibility of all necessary data are very limited, especially for the green field. The degree of complexities of such modelling increases significantly especially in the case of heterogeneous nature typically inherited in unconventional reservoirs. In this perspective, this study focuses on exploring the possibility of simplifying the numerical simulation pr
The study aimed to clarify the meanings learned and inferred from reading books، letters and messages in Surat Al-Qur’an. The inductive method، the analytical method، and the deductive method، One of the most prominent results of the research: that the multiplicity of Qur’anic readings produces a variety and expansion in the meaning that has a clear impact on the interpretation by clarifying the meaning of the verse.
Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l
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