Sentiment Analysis in Arabic Social Media Using Deep Learning Models

Yafoz, Ayman; Mouhoub, Malek

doi:10.1109/smc52423.2021.9659245

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…In a study by [24], examined the sentiments in datasets containing reviews of cars and real estate in Arabic online. They used the Bi-LSTM (Bidirectional Long Short-Term Memory), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), CNN (Convolutional Neural Networks), and CNN-GRU deep learning algorithms in combination with the BERT word embedding model.…”

Section: Sentiment Analysis Using Deep Learningmentioning

confidence: 99%

Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning

Tan

Chow

Kanesan

et al. 2023

Wireless Pers Commun

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Social media platforms such as Twitter and Facebook have become popular channels for people to record and express their feelings, opinions, and feedback in the last decades. With proper extraction techniques such as sentiment analysis, this information is useful in many aspects, including product marketing, behavior analysis, and pandemic management. Sentiment analysis is a technique to analyze people’s thoughts, feelings and emotions, and to categorize them into positive, negative, or neutral. There are many ways for someone to express their feelings and emotions. These sentiments are sometimes accompanied by sarcasm, especially when conveying intense emotion. Sarcasm is defined as a positive sentence with underlying negative intention. Most of the current research work treats them as two distinct tasks. To date, most sentiment and sarcasm classification approaches have been treated primarily and standalone as a text categorization problem. In recent years, research work using deep learning algorithms have significantly improved performance for these standalone classifiers. One of the major issues faced by these approaches is that they could not correctly classify sarcastic sentences as negative. With this in mind, we claim that knowing how to spot sarcasm will help sentiment classification and vice versa. Our work has shown that these two tasks are correlated. This paper proposes a multi-task learning-based framework utilizing a deep neural network to model this correlation to improve sentiment analysis’s overall performance. The proposed method outperforms the existing methods by a margin of 3%, with an F1-score of 94%.

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Section: Sentiment Analysis Using Deep Learningmentioning

confidence: 99%

Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning

Tan

Chow

Kanesan

et al. 2023

Wireless Pers Commun

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“…Beracha et al (2019) produced sentiment scores (positive or negative) by analyzing real estate news from The Wall Street Journal and successfully predicted the performance of the US commercial real estate market. Yafoz and Mouhoub (2021) analyzed sentiments in online real estate forums; a prominent feature of this study is that they analyzed texts written in Arabic. Xu and Hsu (2022) analyzed news-related keywords and predicted agricultural exports.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring land lot shapes for property valuation

Lee

2023

DTA

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PurposeUnstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models.Design/methodology/approachImagery data containing land lot shapes are fed into a convolutional neural network, and the shape of land lots is classified into two categories, regular and irregular-shaped. Then, the intermediate output (regularity score) is utilized in four downstream models to estimate land prices: random forest, gradient boosting, support vector machine and regression models.FindingsQuantification of the land lot shapes and their exploitation in valuation led to an improvement in the predictive accuracy for all subsequent models.Originality/valueThe study findings are expected to promote the adoption of elusive price determinants such as the shape of a land lot, appearance of a house and the landscape of a neighborhood in property appraisal practices.

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Performance Evaluation of Contextualized Arabic Embeddings: The Arabic Sentiment Analysis Task

Dakalbab¹,

Elnagar²

2023

Lecture Notes in Networks and Systems

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Sentiment Analysis in Arabic Social Media Using Deep Learning Models

Cited by 6 publications

References 12 publications

Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning

Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning

Measuring land lot shapes for property valuation

Performance Evaluation of Contextualized Arabic Embeddings: The Arabic Sentiment Analysis Task

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