Identifying comparative opinions in Arabic text in social media using machine learning techniques

Alharbi, Fatmah Rasheed; Khan, Muhammad Badruddin

doi:10.1007/s42452-019-0183-3

Cited by 19 publications

(10 citation statements)

References 22 publications

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“…The different dialects are one of the reasons for introducing many new words into any language, especially stop words [58]. Another challenge is the diacritics used in the Arabic orthographic system.…”

Section: Discussionmentioning

confidence: 99%

Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter

Alqurashi¹,

Hamoui²,

Alashaikh³

et al. 2021

Preprint

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The rapid growth of social media content during the current pandemic provides useful tools for disseminating information which has also become a root for misinformation. Therefore, there is an urgent need for fact-checking and effective techniques for detecting misinformation in social media. In this work, we study the misinformation in the Arabic content of Twitter. We construct a large Arabic dataset related to COVID-19 misinformation and gold-annotate the tweets into two categories: misinformation or not. Then, we apply eight different traditional and deep machine learning models, with different features including word embeddings and word frequency. The word embedding models (FASTTEXT and word2vec) exploit more than two million Arabic tweets related to COVID-19. Experiments show that optimizing the area under the curve (AUC) improves the models' performance and the Extreme Gradient Boosting (XGBoost) presents the highest accuracy in detecting COVID-19 misinformation online.

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Section: Discussionmentioning

confidence: 99%

Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter

Alqurashi¹,

Hamoui²,

Alashaikh³

et al. 2021

Preprint

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“…Data related research is on the increase due to the enormous potential benefit in terms of knowledge acquisition and application [15,16]. Educational data mining is the application of data mining methodologies in educationaldata related research studies toward solving education related issues [17].…”

Section: Introductionmentioning

confidence: 99%

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

Adekitan

Salau

2019

SN Appl. Sci.

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The ability to predict failure is an advantageous educational tool that can be effectively used to counsel student, and this may also be used as a tool for developing, and channelling adequate academic interventions toward preventing failure and dropout tendencies. Students are generally admitted based on their evaluated academic potentials as measured using their admission criteria scores. This study seeks to identify the relationship, if any, between the admission criteria scores and the graduation grades, and to examine the influence of ethnicity using the geopolitical zone of origin of the student on the predictive accuracy of the models developed using a Nigerian University as a case study. Data mining analyses were carried out using four classifiers on the Orange Software, and the results were verified with multiple regression analysis. The maximum classification accuracy observed is 53.2% which indicates that the pre-admission scores alone are insufficient for predicting the graduation result of students but it may serve as a useful guide. By applying over-sampling technique, the accuracy increased to 79.8%. The results establish that the ethnic background of the student is statistically insignificant in predicting their graduation results. Hence, the use of ethnicity in admission processes is therefore not ideal.

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“…MSA is used for formal writing and communication in most Arabic countries [29]. The most used form of Arabic is informal Arabic, which includes variations depending on the country and sometimes on the region [30]. Various Arabic language studies were conducted to develop several Arabic NLP (ANLP) applications and automatically analyze text in multiple domains [31].…”

Section: ) Arabic Natural Language Processingmentioning

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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Identifying comparative opinions in Arabic text in social media using machine learning techniques

Cited by 19 publications

References 22 publications

Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter

Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

Arabic Rumor Detection Using Contextual Deep Bidirectional Language Modeling

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