Pre-trained Word Embeddings for Arabic Aspect-Based Sentiment Analysis of Airline Tweets

Ashi, Mohammed Matuq; Siddiqui, Muazzam Ahmed; Nadeem, Farrukh

doi:10.1007/978-3-319-99010-1_22

Cited by 22 publications

(20 citation statements)

References 10 publications

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“…Albadi et al [52] collected the first HS dataset with about 6.6 K Arabic HS tweets. The support vector machine (SVM) classifier and a GRU (Gated Recurrent Unit) trained on AraVec embeddings [53] were utilized for the classification task and achieved the best performance with 79% accuracy. Ousidhoum et al [54] built a multilingual HS dataset consisting of English, French, and Arabic tweets.…”

Section: Hate and Offensive Speech Detection In Arabicmentioning

confidence: 99%

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

et al. 2021

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As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.

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Section: Hate and Offensive Speech Detection In Arabicmentioning

confidence: 99%

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

et al. 2021

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“…Pedoman PICO sangat berguna dan memudahkan dalam merumuskan pertanyaan penelitian [20]. Tabel Dataset Peneliti Jumlah Twitter [33], [34], [35], [36], [23], [24], [37], [38], [39], [22], [40], [41], [42], [43], [44], [45], [46] 17 Berita berbahasa Arab [28], [47], [48], [49], [27], [25], [50], [51] 8…”

Section: Tahap Pembuatan Rencana Awalunclassified

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.

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“…Preprocessing [17] Normalization, POS tagging [24][25][26][27] Stemming [28][29][30][31][32][33] Text cleaning [34][35][36][37][38][39] Normalization, stemming, stop words removal [40][41][42] Text cleaning, normalization, stemming, stop words removal [43][44][45] Normalization Text cleaning, normalization, tokenization, stemming, stop words removal [49][50][51][52] Normalization, tokenization [53,54] Text cleaning, normalization, tokenization [55,56] Normalization, tokenization, POS tagging [13,[57][58][59][60][61][62][63][64] Normalization, tokenization, stemming, stop words removal [65,66] Normalization, tokenization, stemming, lemmatization [67,68] Text cleaning, normalization, tokenization, stemming [69] Text cleaning, tokenization, stemming, negation detection [70]…”

Section: Referencementioning

confidence: 99%

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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Pre-trained Word Embeddings for Arabic Aspect-Based Sentiment Analysis of Airline Tweets

Cited by 22 publications

References 10 publications

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Arabic Sentiment Analysis: A Systematic Literature Review

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