Arabic aspect sentiment polarity classification using BERT

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

doi:10.1186/s40537-022-00656-6

Cited by 30 publications

(12 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, increased attention has been paid to the use of large pre-trained language models, such as BERT and its variations, as it achieves superior results for a variety of NLP tasks. [39] proposed a BERT with a simple linear classification layer to accomplish T2 only. Experiments on three Arabic datasets, hotel reviews, book reviews, and Arabic news, showed that the proposed model accuracies were 89.51%, 73.23%, and 85.73%, respectively.…”

Section: ) Deep Learning Approachesmentioning

confidence: 99%

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

Alshaikh,

Almatrafi,

Abushark

2024

IEEE Access

View full text Add to dashboard Cite

Educational institutions typically gather feedback from beneficiaries through formal surveys.Offering open-ended questions allows students to express their opinions about matters that may not have been measured directly in closed-ended questions. However, responses to open-ended questions are typically overlooked due to the time and effort required. Aspect-based sentiment analysis is used to automate the process of extracting fine-grained information from texts. This study aims to 1) examine the performance of different BERT-based models for aspect term extraction for Arabic text sourced from educational institution surveys; 2) develop a system that automates the ABSA process in a way that will automatically label survey responses. An end-to-end system was developed as a case study to extract aspect terms, identify their polarity, map extracted aspects to their respective categories, and aggregate category polarity. To accomplish this, the models were evaluated using an in-house dataset. The result showed that FAST-LCF-ATEPC, a multilingual checkpoint, outperformed other models including AraBERT, MARBERT, and QARiB, in the aspect-term extraction task, with an F1 score of 0.58. Hence, it was used for aspect-term polarity classification, showing an F1 score of 0.86. Mapping aspects to their respective categories using a predefined list yielded an average F1 score of 0.98. Furthermore, the polarities of the categories were aggregated to summarize the overall polarity for each category. The developed system can support Arabic educational institutions in harnessing valuable information in responses to open-ended survey questions, allowing decision-makers to better allocate resources, and improve facilities, services, and students' learning experiences.

show abstract

Section: ) Deep Learning Approachesmentioning

confidence: 99%

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

Alshaikh,

Almatrafi,

Abushark

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To validate the effectiveness of multi-task model, we compared the best multi-task model (AR-LCF-ATEPC-Fusion) with state-of-the-art Deep-based and transformer-based approaches that used the same benchmark dataset: RNN-BiLSTM-CRF [69], BiGRU [70], attention mechanism with neural network [71], BERT [72], and Bert-Flair-BiLSTM/BiGRU-CRF [75], Sequence to Sequence mode for preprocessing and BERT for classification (Seq-seq BERT) [76] and BERT with liner layer (Bert-linerpair) [77]. The results demonstrated that LCF-ATEPC model outperformed other comparable models.…”

Section: Performance Of Proposed Multi-task Model On the Original Dat...mentioning

confidence: 99%

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Fadel¹,

Abulnaja²,

Saleh³

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical for real-world scenarios where the ATE task is the base task for APC, and its result impacts the accuracy of APC. Thus, in this study, we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a single model. This paper integrates the multi-task model, namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification (LCF-ATEPC) and Arabic Bidirectional Encoder Representation from Transformers (AraBERT) as a shred layer for Arabic contextual text representation. The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism (LCF) to capture the interactive information between an aspect and its context. Moreover, data augmentation techniques are proposed based on state-of-the-art augmentation techniques (word embedding substitution with constraints and contextual embedding (AraBERT)) to increase the diversity of the training dataset. This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA. Extensive experiments were conducted on the original and combined datasets (merging the original and augmented datasets). Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques. Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer (AR-LCF-ATEPC-Fusion) and the proposed data augmentation word embedding-based method (FastText) on the combined dataset.

show abstract

“…Generally, autoregressive models perform better on text generation tasks, whereas autoencoder models perform better on language comprehension tasks. Abdelgwad et al [ 46 ] proposed an aspect-level sentiment analysis method based on BERT for the Arabic sentiment-polarity classification task and achieved good results. Choudrie et al [ 47 ] developed a multi-class sentiment classifier system based on RoBERTa and transfer learning, applied to the study of sentiment analysis of COVID-19.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification

Cui

Han

Duan

et al. 2023

Entropy

View full text Add to dashboard Cite

In recent years, social network sentiment classification has been extensively researched and applied in various fields, such as opinion monitoring, market analysis, and commodity feedback. The ensemble approach has achieved remarkable results in sentiment classification tasks due to its superior performance. The primary reason behind the success of ensemble methods is the enhanced diversity of the base classifiers. The boosting method employs a sequential ensemble structure to construct diverse data while also utilizing erroneous data by assigning higher weights to misclassified samples in the next training round. However, this method tends to use a sequential ensemble structure, resulting in a long computation time. Conversely, the voting method employs a concurrent ensemble structure to reduce computation time but neglects the utilization of erroneous data. To address this issue, this study combines the advantages of voting and boosting methods and proposes a new two-stage voting boosting (2SVB) concurrent ensemble learning method for social network sentiment classification. This novel method not only establishes a concurrent ensemble framework to decrease computation time but also optimizes the utilization of erroneous data and enhances ensemble performance. To optimize the utilization of erroneous data, a two-stage training approach is implemented. Stage-1 training is performed on the datasets by employing a 3-fold cross-segmentation approach. Stage-2 training is carried out on datasets that have been augmented with the erroneous data predicted by stage 1. To augment the diversity of base classifiers, the training stage employs five pre-trained deep learning (PDL) models with heterogeneous pre-training frameworks as base classifiers. To reduce the computation time, a two-stage concurrent ensemble framework was established. The experimental results demonstrate that the proposed method achieves an F1 score of 0.8942 on the coronavirus tweet sentiment dataset, surpassing other comparable ensemble methods.

show abstract

Arabic aspect sentiment polarity classification using BERT

Cited by 30 publications

References 48 publications

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification

Contact Info

Product

Resources

About