Sentiment Analysis of Arabic COVID-19 Tweets

Ahmed, Dena; Salloum, Said A.; Shaalan, Khaled

doi:10.1007/978-3-030-85990-9_50

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…When compared to the other classifiers, the LR produced datasets with a higher accuracy rate. After creating the feature vector in Ahmed D. et al ( 2021 ), using TF-IDF, the authors applied LR, SVM, RF, NB, and K-NN. The best accuracy performance was presented by SVM.…”

Section: Related Workmentioning

confidence: 99%

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory

Alosaimi,

Saleh,

Hamzah

et al. 2024

Front. Artif. Intell.

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Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

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Section: Related Workmentioning

confidence: 99%

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory

Alosaimi,

Saleh,

Hamzah

et al. 2024

Front. Artif. Intell.

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“…Ahmed et al [12] analyzed Arabic tweets about COVID-19 for sentiments using five different ML models, namely, support vector machine (SVM), Naïve Bayes, random forest, logistic regression and K-Nearest neighbor. They evaluated the five models using Arabic Sentiment Twitter Corpus (ASTC) [13].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

Al-Khazaleh

Alian

Biltawi³

et al. 2023

Int. J. Onl. Eng.

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The emergence of social media platforms, which contributed in activating the patterns of connection between individuals, leads to the availability of a huge amount of content such as text, images, and videos. Twitter is one of the most popular platforms of social media that encourage researchers to investigate people’s feelings and opinions among through sentiment analysis studies that elicited the interest of researchers in natural language processing field. Many techniques related to machine learning and deep learning models could be used to improve the efficiency and performance of sentiment analysis, especially in complex classification problems. In this paper, different models of long short-term memory recurrent neural network are used for the sentiment classification task. The input text was represented as vectors using Arabic pre-trained word embedding (Aravec). Experiments were conducted using different dimensions of Aravec on 15779 tweets about COVID-19 collected and labeled as positive and negative. The experimental results show an accuracy value of 98%.

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The Relationship Between Functional Empowerment and Creative Behavior of Workers During the COVID-19 Pandemic in the UAE

AlZaabi

Abualrejal

2022

The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022)

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Sentiment Analysis of Arabic COVID-19 Tweets

Cited by 4 publications

References 22 publications

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory

Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

The Relationship Between Functional Empowerment and Creative Behavior of Workers During the COVID-19 Pandemic in the UAE

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