Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts

Muaad, Abdullah Y.; Hanumanthappa, J.; Benifa, J. V. Bibal; Alabrah, Amerah; Saif, Mufeed Ahmed Naji; Pushpa, D.; Al-antari, Mugahed A.; Alfakih, Taha

doi:10.1155/2022/7937667

Cited by 30 publications

(11 citation statements)

References 28 publications

(26 reference statements)

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“…Deep learning approaches, particularly several CNNs, have recently emerged as the dominant techniques for image classification [ 52 ]. CNNs carry out convolution operations between kernels and tensors.…”

Section: Methodsmentioning

confidence: 99%

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

Ullah

Heyat

Akhtar

et al. 2022

Computational Intelligence and Neuroscience

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Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart’s rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors’ workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.

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Section: Methodsmentioning

confidence: 99%

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

Ullah

Heyat

Akhtar

et al. 2022

Computational Intelligence and Neuroscience

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“…There are various techniques to convert string data into numerical data such as Bag of words (BoW), Term Frequency -Inverse Document Frequency (TFIDF), Word2Vec, and Bidirectional Encoder Representations from Transformers (BERT). In the following section, some of these techniques will be explained [33] [13].…”

Section: Text Representation/ Feature Engineeringmentioning

confidence: 99%

“…The k-nearest neighbors (KNN) algorithm is a straightforward, easy-to-implement supervised ML algorithm that is applicable for both classification and regression [33]. K-NN supposes the likeness between the recent and the known cases.…”

Section: ) K-nearest Neighbor (K-nn)mentioning

confidence: 99%

A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

Masadeh¹,

Davanager²,

Muaad³

2022

IJACSA

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Social media platforms generate a huge amount of data every day. However, liberty of speech through these networks could easily help in spreading hatred. Hate speech is a severe concern endangering the cohesion and structure of civil societies. With the increase in hate and sarcasm among the people who contact others over the internet in this era, there is a dire need for utilizing artificial intelligence (AI) technology innovation that would face this problem. The rampant spread of hate can dangerously break society and severely damage marginalized people or groups. Thus, the identification of hate speech is essential and becoming more challenging, where the recognition of hate speech on time is crucial in stopping its dissemination. The capacity of the Arabic morphology and the scarcity of resources for the Arabic language makes the task of distinguishing hate speech even more demanding. For fast identification of Arabic hate speech in social network comments, this work presents a comprehensive framework with eight machine learning (ML) and deep learning (DL) algorithms, namely Gradient Boosting (GB), K-Nearest Neighbor (K-NN), Logistic Regression (LR), Naive Bayes (NB), Passive Aggressive Classifier (PAC), Support Vector Machine (SVM), Ara-BERT, and BERT-AJGT are implemented. Two representation techniques have been used in the proposed framework in order to extract features: a bag of words followed by BERT-based context text representations. Based on the result and discussion part, context text representation techniques with Ara-BERT and BERT-AJGT outperform all other ML models and related work with accuracy equal to 79% for both models.

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“…Not only do people use standard languages, such as German, Spanish, and English, but they also try to be more advanced by using emotion icons otherwise called hashtags #, URLs, emoticons, etc. [3].…”

Section: Introductionmentioning

confidence: 99%

Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks

et al. 2022

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Sarcasm detection has received considerable interest in online social media networks due to the dramatic expansion in Internet usage. Sarcasm is a linguistic expression of dislikes or negative emotions by using overstated language constructs. Recently, detecting sarcastic posts on social networking platforms has gained popularity, especially since sarcastic comments in the form of tweets typically involve positive words that describe undesirable or negative characteristics. Simultaneously, the emergence of machine learning (ML) algorithms has made it easier to design efficacious sarcasm detection techniques. This study introduces a new Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection and Classification (HCOA-SACDC) model. The presented HCOA-SACDC model predominantly focuses on the detection and classification of sarcasm in the OSN environment. To achieve this, the HCOA-SACDC model pre-processes input data to make them compatible for further processing. Furthermore, the term frequency–inverse document frequency (TF-IDF) model is employed for the useful extraction of features. Moreover, the stacked autoencoder (SAE) model is utilized for the recognition and categorization of sarcasm. Since the parameters related to the SAE model considerably affect the overall classification performance, the HCO algorithm is exploited to fine-tune the parameters involved in the SAE, showing the novelty of the work. A comprehensive experimental analysis of a benchmark dataset is performed to highlight the superior outcomes of the HCOA-SACDC model. The simulation results indicate that the HCOA-SACDC model accomplished enhanced performance over other techniques.

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Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts

Cited by 30 publications

References 28 publications

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks

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