Towards Arabic aspect-based sentiment analysis: a transfer learning-based approach

Bensoltane, Rajae; Zaki, Taher

doi:10.1007/s13278-021-00794-4

Cited by 40 publications

(14 citation statements)

References 28 publications

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“…Their approach used 501 Indonesian amusement park reviews for testing and reported an accuracy of 79.9% and F1 score of 73.8%. Bensoltane and Zaki [50] compared variants of BERT against bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) for aspect extraction using 2265 Arabic news posts (retrieved from Facebook about the 2014 Gaza attack). They showed that the combination of BERT, bidirectional gated recurrent unit (BiGRU), and CRF achieved the highest performance for aspect term extraction with an F1 score (88%).…”

Section: Related Workmentioning

confidence: 99%

Comparison of Topic Modelling Approaches in the Banking Context

et al. 2023

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Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions. The use of traditional approaches such as Latent Dirichlet Allocation (LDA) for topic discovery has shown great performances, however, they are not consistent in their results as these approaches suffer from data sparseness and inability to model the word order in a document. Thus, this study presents the use of Kernel Principal Component Analysis (KernelPCA) and K-means Clustering in the BERTopic architecture. We have prepared a new dataset using tweets from customers of Nigerian banks and we use this to compare the topic modelling approaches. Our findings showed KernelPCA and K-means in the BERTopic architecture-produced coherent topics with a coherence score of 0.8463.

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Section: Related Workmentioning

confidence: 99%

Comparison of Topic Modelling Approaches in the Banking Context

et al. 2023

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“…A few works used fine-tuning BERT with linear classification for Arabic aspect polarity classification [67]. Bensoltan et al [68] , proposed Bert-BiLSTM-CRF model for AE from News dataset that outperform the previous works on this dataset.…”

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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“…Bensoltane and Zaki [42] attempted to investigate the modeling power of BERT in aspect-extraction and aspect-category identification tasks. In addition to examining the effects of adding stronger layers on top of BERT while dealing with the ATE task.…”

Section: Related Workmentioning

confidence: 99%

Arabic aspect sentiment polarity classification using BERT

2022

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Aspect-based sentiment analysis (ABSA) is a textual analysis methodology that defines the polarity of opinions on certain aspects related to specific targets. The majority of research on ABSA is in English, with a small amount of work available in Arabic. Most previous Arabic research has relied on deep learning models that depend primarily on context-independent word embeddings (e.g. word2vec), where each word has a fixed representation independent of its context. This article explores the modeling capabilities of contextual embeddings from pre-trained language models, such as BERT, and making use of sentence pair input on Arabic aspect sentiment polarity classification task. In particular, we develop a simple but effective BERT-based neural baseline to handle this task. Our BERT architecture with a simple linear classification layer surpassed the state-of-the-art works, according to the experimental results on three different Arabic datasets. Achieving an accuracy of 89.51% on the Arabic hotel reviews dataset, 73.23% on the Human annotated book reviews dataset, and 85.73% on the Arabic news dataset.

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Towards Arabic aspect-based sentiment analysis: a transfer learning-based approach

Cited by 40 publications

References 28 publications

Comparison of Topic Modelling Approaches in the Banking Context

Comparison of Topic Modelling Approaches in the Banking Context

Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning

Arabic aspect sentiment polarity classification using BERT

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