Arabic Sentiment Analysis Using BERT Model

Chouikhi, Hasna; Chniter, Hamza; Jarray, Fethi

doi:10.1007/978-3-030-88113-9_50

Cited by 27 publications

(10 citation statements)

References 19 publications

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“…We chose one of the pre-trained models based on the dataset type to provide us with better-contextualized weights to initialize the model. Our model outperforms the state-of-the-art models AraBERT [13] and Choukhi et al [14]. While both models use BERT architecture like the proposed approach, the main difference is that Choukhi et al [14] uses BERT model medium architecture containing eight encoder layers without a text cleaning step.…”

Section: Resultsmentioning

confidence: 85%

“…Several recent studies [12], [13] have trained the bidirectional encoder representations from transformers (BERT) model on Wikipedia and Oscar datasets for the Arabic language. In addition to that, several recent studies [14], [15] have fine-tuned the Arabic BERT model [13] for downstream task SA. the drawback identified from the analysis of existing literature are: i) models not tested on different datasets; ii) some models ignore the context meaning of the sentence; iii) the model using context like BERT fined-tuned using general pre-trained models that affect models performance; and iv) there is room for improvement for reported prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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ArSentBERT: fine-tuned bidirectional encoder representations from transformers model for Arabic sentiment classification

2023

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Sentiment analysis in the Arabic language is challenging because of its linguistic complexity. Arabic is complex in words, paragraphs, and sentence structure. Moreover, most Arabic documents contain multiple dialects, writing alphabets, and styles (e.g., Franco-Arab). Nevertheless, fine-tuned bidirectional encoder representations from transformers (BERT) models can provide a reasonable prediction accuracy for Arabic sentiment classification tasks. This paper presents a fine-tuning approach for BERT models for classifying Arabic sentiments. It uses Arabic BERT pre-trained models and tokenizers and includes three stages. The first stage is text preprocessing and data cleaning. The second stage uses transfer-learning of the pre-trained models’ weights and trains all encoder layers. The third stage uses a fully connected layer and a drop-out layer for classification. We tested our fine-tuned models on five different datasets that contain reviews in Arabic with different dialects and compared the results to 11 state-of-the-art models. The experiment results show that our models provide better prediction accuracy than our competitors. We show that the choice of the pre-trained BERT model and the tokenizer type improves the accuracy of Arabic sentiment classification.

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Section: Resultsmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

ArSentBERT: fine-tuned bidirectional encoder representations from transformers model for Arabic sentiment classification

2023

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“…This can be fine-tuned to capture context for various NLP tasks such as question answering, sentiment analysis, text classification, sentence embedding, interpreting ambiguity in the text etc. [ 25 , 33 , 90 , 148 ]. Earlier language-based models examine the text in either of one direction which is used for sentence generation by predicting the next word whereas the BERT model examines the text in both directions simultaneously for better language understanding.…”

Section: Datasets In Nlp and State-of-the-art Modelsmentioning

confidence: 99%

Natural language processing: state of the art, current trends and challenges

Khurana

Koli

Khatter

et al. 2022

Multimed Tools Appl

398

134

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Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of N atural L anguage G eneration followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.

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“…In the sentiment analysis domain, authors in [46] proposed an approach using the transformer-based BERT model, which integrates an Arabic BERT tokenizer instead of a basic BERT tokenizer. They tested the technique using five public datasets; the best accuracy was 0.96.…”

Section: B Arabic Text Classificationmentioning

confidence: 99%

Arabic Rumor Detection Using Contextual Deep Bidirectional Language Modeling

et al. 2022

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In today's world, news outlets have changed dramatically; newspapers are obsolete, and radio is no longer in the picture. People look for news online and on social media, such as Twitter and Facebook. Social media contributors share information and trending stories before verifying their truthfulness, thus, spreading rumors. Early identification of rumors from social media has attracted many researchers. However, a relatively smaller number of studies focused on other languages, such as Arabic. In this study, an Arabic rumor detection model is proposed. The model was built using transformer-based deep learning architecture. According to the literature, transformers are neural networks with outstanding performance in natural language processing tasks. Two transformers-based models, AraBERT and MARBERT, were employed, tested, and evaluated using three recently developed Arabic datasets. These models are extensions to the BERT, Bidirectional Encoder Representations from Transformers, a deep learning model that uses transformer architecture to learn the text representations and leverages the attention mechanism. We have also mitigated the challenges introduced by the imbalanced training datasets by employing two sampling techniques. The experimental results of our proposed approaches achieved 0.97 accuracy. This result demonstrated the effectiveness of the proposed method and outperformed other existing Arabic rumor detection methods.

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Arabic Sentiment Analysis Using BERT Model

Cited by 27 publications

References 19 publications

ArSentBERT: fine-tuned bidirectional encoder representations from transformers model for Arabic sentiment classification

ArSentBERT: fine-tuned bidirectional encoder representations from transformers model for Arabic sentiment classification

Natural language processing: state of the art, current trends and challenges

Arabic Rumor Detection Using Contextual Deep Bidirectional Language Modeling

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