Extracting Opinion Targets Using Attention-Based Neural Model

Al-Dabet, Saja; Tedmori, Sara; Al-Smadi, Mohammad

doi:10.1007/s42979-020-00270-4

Cited by 13 publications

(10 citation statements)

References 43 publications

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“…We compare the proposed models with baseline [6] and previous models that used traditional deep learning, RNN [63], BiLSTM-CRF with Word2vec/fastText as word embedding [64], and an attention-based neural model [65].…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…The result showed enhancement over baseline research on both tasks (39% for task1 and 6% for task2). 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.…”

Section: B Arabic Languagementioning

confidence: 99%

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

2022

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“…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

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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.

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“…Most studies involved in Arabic ABSA evaluate the ATE and ASC subtasks independently, ignoring the relatedness and dependency of the two subtasks [2,3,4,5,6,7]. Some studies are either only extracting aspects from a given sentence (ATE) [8,9,10] or predicting sentiment polarities (ASC) assuming that aspect entities are preidentified input features to the model, which is not the case in real-world scenario [11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Arabic Aspect-Based Sentiment Analysis Using End-to-End Model

Shafiq,

Hamza,

Alrahmawy

et al. 2023

IEEE Access

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The majority of research on the Aspect-Based Sentiment Analysis (ABSA) tends to split this task into two subtasks: one for extracting aspects, Aspect Term Extraction (ATE), and another for identifying sentiments toward particular aspects, Aspect Sentiment Classification (ASC). Although these subtasks are closely related, they are performed independently; while performing the Aspect Sentiment Classification task, it is assumed that the aspect terms are pre-identified, which ignores the practical interaction required to properly perform the ABSA. This study addresses these limitations using a unified End-to-End (E2E) approach, which combines the two subtasks into a single sequence labeling task using a unified tagging schema. The proposed model was evaluated by fine-tuning the Arabic version of the Bidirectional Encoder Representations from Transformers (AraBERT) model with a Conditional Random Fields (CRF) classifier for enhanced target-polarity identification. The experimental results demonstrated the efficiency of the proposed fine-tuned AraBERT-CRF model, which achieved an overall F1 score of 95.11% on the SemEval-2016 Arabic Hotel Reviews dataset. The model's predictions are then subjected to additional processing, and the results indicate the superiority of the proposed model, achieving an F1 score of 97.78% for the ATE task and an accuracy of 98.34% for the ASC task, outperforming previous studies.

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Extracting Opinion Targets Using Attention-Based Neural Model

Cited by 13 publications

References 43 publications

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

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

Enhancing Arabic Aspect-Based Sentiment Analysis Using End-to-End Model

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