Detection of COVID-19 Fake News in Online Social Networks with the Developed CNN-LSTM Based Hybrid Model

Yıldırım, Muhammed

doi:10.18280/rces.090201

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Global vector representation (GloVe) [19,20] is a famous one of the space vector representation. It is developed by Pennington in 2014, which aim to learn low-dimensional vector representations of terms.…”

Section: Glove For Arabic Sentiment Analysismentioning

confidence: 99%

An Innovative Arabic Word Embedding Representation for Enhanced Sentiment Analysis

Sahraoui¹,

Mahmoudi²,

Salem³

2023

ISI

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Despite the plethora of data generated on Arabic social media, research dedicated to this language remains comparatively scarce. Sentiment analysis, an extensively studied field in various languages, has seen limited development in Arabic. Existing approaches to Arabic sentiment analysis primarily employ machine learning, wherein word vector representations serve as features for model training. A significant challenge encountered in this approach is the substantial volume and sparsity of the matrix representation, attributable to the extensive vocabulary of the Arabic language. This paper proposes a novel word embedding that amalgamates the Bag of Roots (BoR) technique with Global Vector distributional representations (GloVe). This innovation is inspired by the characteristic of the Arabic language, where it is rare to find two or more words sharing the same root but conveying different sentiments. The impact of this innovative word embedding technique is highlighted through an evaluation using sentiment analysis. This involves the implementation of conventional classifiers, specifically Support Vector Machines (SVM) and Logistic Regression (LR). The results obtained demonstrate promising precision, recall, and F1-score metrics. Additionally, a significant reduction in processing time is observed when compared to other approaches referenced in literature. Thus, this paper contributes to the advancement of Arabic sentiment analysis, offering a potential pathway to overcoming the challenges associated with the large vocabulary and complex structure of the Arabic language.

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Section: Glove For Arabic Sentiment Analysismentioning

confidence: 99%

An Innovative Arabic Word Embedding Representation for Enhanced Sentiment Analysis

Sahraoui¹,

Mahmoudi²,

Salem³

2023

ISI

View full text Add to dashboard Cite

show abstract

Detection of COVID-19 Anti-Vaccination from Twitter Data Using Deep Learning and Feature Selection Approaches

Ertem,

Özbay

2024

Firat University Journal of Experimental and Computational Engineering

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The COVID-19 pandemic has evolved into a crisis significantly impacting health, the economy, and social life worldwide. During this crisis, anti-vaccination sentiment poses a considerable obstacle to controlling the epidemic and the effectiveness of vaccination campaigns. This study aimed to detect COVID-19 anti-vaccination sentiment from Twitter data using a combination of deep learning and feature selection approaches. The proposed method integrates a deep learning model with feature selection techniques to identify anti-vaccination sentiment by pinpointing important features in text data. Hybrid TF-IDF and N-gram methods were utilized for feature extraction, followed by Chi-square feature selection. The dataset comprises Twitter text data and two labels. The Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the labels. Long Short-Term Memory (LSTM), a deep learning architecture, was employed for the classification process. The experimental results, obtained by leveraging the proposed feature extraction, feature selection, and LSTM methods, achieved the highest accuracy value of 99.23%. These findings demonstrate the proposed methods' success in effectively detecting COVID-19 anti-vaccination sentiment in text data. The study's results can offer valuable insights for developing health policies and public information strategies, presenting a new and powerful tool for detecting anti-vaccine sentiment in planning vaccination campaigns and public health interventions.

show abstract

Detection of COVID-19 Fake News in Online Social Networks with the Developed CNN-LSTM Based Hybrid Model

Cited by 2 publications

References 24 publications

An Innovative Arabic Word Embedding Representation for Enhanced Sentiment Analysis

An Innovative Arabic Word Embedding Representation for Enhanced Sentiment Analysis

Detection of COVID-19 Anti-Vaccination from Twitter Data Using Deep Learning and Feature Selection Approaches

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