A Survey of Semantic Analysis Approaches

Salloum, Said A.; Khan, Rehan Ali; Shaalan, Khaled

doi:10.1007/978-3-030-44289-7_6

Cited by 64 publications

(23 citation statements)

References 29 publications

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“…The effective use of E-learning tools is dependent on the perspective of the users, as well as their knowledge and competences with respect to both computer and mobile usage [6,[8][9][10]. It has been found that these significant factors have an impact on the early acceptance [11] of the users of computer technology and their behavior in the future [6,[12][13][14][15] with respect to the use of web-based learning systems [16]. Since the technology acceptance model (TAM) has satisfactory explanatory power and its widespread popularity, it was used in various studies in the technology acceptance and adoption literature in the domain of IS implementation [17], particularly in terms of E-learning [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Investigating a theoretical framework for e-learning technology acceptance

Kurdi

Alshurideh

Salloum

2020

IJECE

Self Cite

131

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E-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. A theoretical framework was developed based on the technology acceptance model (TAM). To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. The results revealed that “social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use” are the strongest and most important predictors in the intention of and students towards E-learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems.

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Section: Introductionmentioning

confidence: 99%

Investigating a theoretical framework for e-learning technology acceptance

Kurdi

Alshurideh

Salloum

2020

IJECE

Self Cite

131

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“…Due to the large amounts of information during the past two decades, there emerged a need for technical methods of classifying huge texts as statistical and manual methods had never become useless in this field [35][36][37][38][39][40][41][42][43][44][45][46][47][48]. Therefore, machine learning classification techniques appeared which aim to classify unstructured texts and documents based on certain algorithms designed for this purpose.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…Algorithms of neural networks give very accurate results in the area of NLP and support deep learning very significantly as they solve some errors and problems related to the variance of data resulting from the process of deep learning [36][37][38][39]. Neural networks are used in the classification of texts to address linear and non-linear problems.…”

Section: Neural Networkmentioning

confidence: 99%

A systematic review of text classification research based on deep learning models in Arabic language

Wahdan

Hantoobi

Salloum

et al. 2020

IJECE

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Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.

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“…Exploring this data plays a vital role in consumer retention by improving the quality of services [6,7]. Social media sites such as Instagram, Facebook, and Twitter offer valuable data that can be used by business owners not only to track and analyse customers' opinions about their businesses but also that of their competitors [8][9][10][11]. Moreover, these valuable data attracted decision-makers who seek to improve the services provided [8,9,12,13].…”

Section: Introductionmentioning

confidence: 99%