Arabic aspect based sentiment analysis using bidirectional GRU based models

Abdelgwad, Mohammed M.; Soliman, Taysir Hassan A.; Taloba, Ahmed I.; Farghaly, Mohamed

doi:10.1016/j.jksuci.2021.08.030

Cited by 51 publications

(39 citation statements)

References 33 publications

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“…Tables 1 and 2 show the accuracy of the proposed Fusion Attention algorithm and its assessment with the existing methods like Res-Target (10) , VGG16 (15) , Resnet50 (17) and Alexnet (20) . Considering the existing VGG-16 networks, it possesses 13 convolutional layers as well as 3 fully connected layers.…”

Section: Resultsmentioning

confidence: 99%

“…Considering larger datasets, fully supervised learning of CNNs was possible without over fitting additional measure of parameters (12) . Recent studies revealed that, the restrictions of CNN that was trained for large datasets, for instance, ILSVRC shall be used in object recognition or image classification tasks when the information was restricted, resulting in better execution of usual representations (13)(14)(15) . Very few research works were done on sentimental analysis methods dependent on CNN for visual sentimental predictions.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Fusion Attention Algorithm for Sentimental Image Analysis

Roseline¹,

Chellam²

2022

IJST

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Objectives: To implement a novel and hybrid methodology for finding out the positive features when using convolutional neural networks (CNNs) for visual sentiment analysis. To achieve increased accuracy, precision and recall by using this proposed fusion attention methodology. Methods: This study proposes a modified methodology encompassing spatial attention, channel attention as well as squeeze excitation modules. An enhanced approach on the basis of convolutional neural networks was used here which utilizes convolution operators by combining both spatial and channel-based data. Moreover, we have incorporated three considerations like spatial, channel as well as squeeze and excitation at various levels for attaining optimal results. Findings: The accuracy of the existing approaches was 59.88%, 60.06%, 59.28% and 62.89%, but the proposed fusion attention method showed increased accuracy of 64.15%. Similarly, the F1 score of existing approaches are 0.464804, 0.250164, 0.474129 and 0.2574, but the proposed method revealed increased F1 score of 0.512933. Furthermore, the proposed algorithm showed precision and recall of 0.560896 and 0.472526 which were better when compared with the existing approaches like Res-Target, Resnet50, Alexnet and VGG16. Novelty: The novel feature of this proposed fusion attention algorithm was that it incorporates a hybrid approach in which the image together with convolution passes through channel attention, spatial attention as well as squeeze and excitation so as to attain increased accuracy, but most of the existing approaches have used only channel attention and spatial attention modules. In this proposed method, the algorithm performs convolution in 64-bit, 128-bit and 256-bit respectively together in which the three attentions were interchanged in each convolution, which were not prevalent in the existing approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Fusion Attention Algorithm for Sentimental Image Analysis

Roseline¹,

Chellam²

2022

IJST

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“…Recall is described as the amount of positive class predictions that are made of all positive examples in the dataset [ 31 – 33 ]. The fraction of right events among all events is known as recall.…”

Section: Resultsmentioning

confidence: 99%

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Chawla

Balaji²,

Alabdali

et al. 2022

Journal of Healthcare Engineering

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A variety of receptor and donor characteristics influence long-and short-term kidney graft survival. It is critical to predict the effectiveness of kidney transplantation to optimise organ allocation. This would allow patients to choose the best accessible kidney donor and the optimal immunosuppressive medication. Several studies have attempted to identify factors that predispose to graft rejection, but the results have been contradictory. As a result, the goal of this paper is to use the African buffalo-based artificial neural network (AB-ANN) approach to uncover predictive risk variables related to kidney graft. These two feature selection approaches combine to provide a novel hybrid feature selection technique that could select the most important elements to improve prediction accuracy. The feature analysis revealed that clinical features have varied effects on transplant survival. The collected data is processed in both training and testing methods. The prediction model's performance, in terms of accuracy, precision, recall, and F-measure, was examined, and the results were compared with those of other existing systems, including naive Bayesian, random forest, and J48 classifier. The results suggest that the proposed approach can forecast graft survival in kidney recipients' next visits in a creative manner and with more accuracy compared with other classifiers. This proposed method is more efficient for predicting kidney graft survival. Incorporating those clinical tools into outpatient clinics’ everyday workflows could help physicians make better and more personalised decisions.

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“…In [65], the authors used an attention mechanism for AE, and the performance improved with a 72.8 F-score. In [66], a BiGRU was used. For AE, the result was close to [65].…”

Section: B Arabic Languagementioning

confidence: 99%

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

2022

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The exponential growth of the internet and a multi-fold increase in social media users in the last decade have resulted in a massive growth of unstructured data. Aspect-Based Sentiment Analysis (ABSA) is challenging because it performs a fine-grain analysis; it is a text analysis technique where the opinions group is based on the aspect. The Aspect Extraction (AE) task is one of the core subtasks of ABSA; it helps to identify aspect terms in the text, comments, or reviews. The challenge of the Arabic AE task increases due to the complexity of the Arabic language. This work aims to develop the Arabic AE task by proposing transfer learning using State-of-art pre-trained contextual language models. We concatenate the Bidirectional Encoder Representation from Transformers (BERT) language model and contextualize string embedding (Flair embedding) as a stacked embeddings layer for better word representation for Arabic language. Then, we extend it with different deep learning network architectures. For Arabic AE, the model is developed by concatenating the Arabic contextual language model, AraBERT, and Flair embedding as a contextual stacked embeddings layer with an extended layer, BiLSTM-CRF or BiGRU-CRF, for sequence labeling. Our proposed models are called BF-BiLSTM-CRF and BF-BiGRU-CRF. The proposed model is evaluated using the Arabic Hotel's reviews dataset. For performance evaluation, we used the F1 score. The experimental results show that the proposed BF-BiLSTM-CRF configuration outperformed the baseline and other models by achieving an F1score of 79.7%.

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Arabic aspect based sentiment analysis using bidirectional GRU based models

Cited by 51 publications

References 33 publications

A Novel Fusion Attention Algorithm for Sentimental Image Analysis

A Novel Fusion Attention Algorithm for Sentimental Image Analysis

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

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