Transfer Learning Across Arabic Dialects for Offensive Language Detection

Husain, Fatemah; Uzuner, Özlem

doi:10.1109/ialp57159.2022.9961263

Cited by 8 publications

(5 citation statements)

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“…The process of understanding users' attitudes and emotions is called sentiment analysis [19]. It has two main measurements.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Collocations refer to terms of a sequence of two or more words based on a common or traditional way of saying them [33]. In our experiments, we apply the same collocation extraction approach defined by [34] in which they adopt five methods without applying part-ofspeech pattern filtering. The five methods are the frequencybased, the t-test, the chi-square test, the likelihood ratio, and the Pairwise Mutual Information (PMI).…”

Section: B Exploratory Data Analysismentioning

confidence: 99%

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Bridging the Kuwaiti Dialect Gap in Natural Language Processing

Husain,

Alostad,

Omar

2024

IEEE Access

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The available dialectal Arabic linguistic resources are very limited in their coverage of Arabic dialects, particularly the Kuwaiti dialect. This shortage in linguistic resources creates struggles for researchers in the Natural Language Processing (NLP) field and limits the development of advanced linguistic analytical and processing tools for the Kuwaiti dialect. Many other low-resource Arabic dialects are still not explored in research due to the challenges faced during the annotators' recruitment process for dataset labeling. This paper proposes a weak supervised classification system to solve the problem of recruiting human annotators called "q8SentiLabeler". In addition, we developed a large dataset consisting of over 16.6k posts serving sentiment analysis for the Kuwaiti dialect, covering several themes and timeframes to remove any bias that might affect its content. Furthermore, we evaluate our dataset using multiple traditional machine-learning classifiers and advanced deep-learning language models to test its performance. Results demonstrate the positive potential of "q8SentiLabeler" to replace human annotators with a 93% for the pairwise percent agreement and a 0.87 for Cohen's kappa coefficient. Using the ARBERT model on our dataset, we were able to achieve 89% accuracy in the system's performance.

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“…The process of understanding users' attitudes and emotions is called sentiment analysis [19]. It has two main measurements.…”

Section: Sentiment Analysismentioning

confidence: 99%

Section: B Exploratory Data Analysismentioning

confidence: 99%

Bridging the Kuwaiti Dialect Gap in Natural Language Processing

Husain,

Alostad,

Omar

2024

IEEE Access

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show abstract

“…28 articles [49], [50], [51], [9], [52], [28], [53], [54], [55], [30], [31], [33], [56], [34], [35], [36], [57], [37], [11], [58], [39], [40], [59], [43], [60], [46], [61], [48] MARBERT 0 9 9 articles [50], [62], [27], [53], [34], [43], [46], [63], [64] ArabicBERT 5 articles [34], [58], [38], [40], […”

Section: Bert Modelsmentioning

confidence: 99%

“…To support dialectical Arabic, the model was further pretrained on a corpus of 12,000 sentences written in different Arabic dialects. This customized version of AraBERT was called SalamBERT [59].…”

mentioning

confidence: 99%

BERT Models for Arabic Text Classification: A Systematic Review

Alammary

2022

Applied Sciences

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Bidirectional Encoder Representations from Transformers (BERT) has gained increasing attention from researchers and practitioners as it has proven to be an invaluable technique in natural languages processing. This is mainly due to its unique features, including its ability to predict words conditioned on both the left and the right context, and its ability to be pretrained using the plain text corpus that is enormously available on the web. As BERT gained more interest, more BERT models were introduced to support different languages, including Arabic. The current state of knowledge and practice in applying BERT models to Arabic text classification is limited. In an attempt to begin remedying this gap, this review synthesizes the different Arabic BERT models that have been applied to text classification. It investigates the differences between them and compares their performance. It also examines how effective they are compared to the original English BERT models. It concludes by offering insight into aspects that need further improvements and future work.

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“…For the community of sentiment analysts, it is quickly becoming one of the most difficult and important tasks. Numerous elements, such as context awareness, cultural considerations, and personality qualities, are said to contribute to the difficulty of sardonic content detection (Husain & Uzuner, 2021). Incorrect polarity resolution in sentences is brought on by sarcasm.…”

Section: Introductionmentioning

confidence: 99%

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Singh

2024

ADCAIJ

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One of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcasm can be difficult because it conveys feelings that are diametrically contrary to the literal meaning expressed in the text. There are several different models for sarcasm detection. To identify humorous news headlines, this article assessed vectorization algorithms and several machine learning models. The recommended hybrid technique using the bag-of-words and TF-IDF feature vectorization models is compared experimentally to other machine learning approaches. In comparison to existing strategies, experiments demonstrate that the proposed hybrid technique with the bag-of-word vectorization model offers greater accuracy and F1-score results.

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Transfer Learning Across Arabic Dialects for Offensive Language Detection

Cited by 8 publications

References 0 publications

Bridging the Kuwaiti Dialect Gap in Natural Language Processing

Bridging the Kuwaiti Dialect Gap in Natural Language Processing

BERT Models for Arabic Text Classification: A Systematic Review

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

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