Applications of Deep Learning in Arabic Sentiment Analysis: Research Perspective

Fouadi, Hassan; Moubtahij, Hicham El; Lamtougui, Hicham; Satori, Khalid; Yahyaouy, Ali

doi:10.1109/iraset48871.2020.9092163

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…In 2020, Hassan and al. published the work described in detail the field of (ASA) [6]. They presented the different difficulties and challenges related to this field and then specified the main steps needed from corpus construction to the interpretation of the results.…”

Section: Sentiment Analysis For the Arabic Languagementioning

confidence: 99%

Sentiment Analysis of Arabic Comments Using Machine Learning and Deep Learning Models

Fouadi¹,

Moubtahij²,

Lamtougui³

et al. 2022

INDJCSE

Self Cite

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Sentiment analysis is a field of research that consists in analyzing the sensations, attitudes, and emotions of individuals towards entities such as products, services, and economic organizations. Likewise, the demand for Arabic Sentiment Analysis has grown rapidly due to the extensive use of the Arabic language in social media networks and has generated considerable interest from the research community. Arabic is one of the widely used languages on social networks. However, its morphological complexities, its dialect varieties, and its relatively few resources make it a challenging language for sentiment analysis. The main goal of our study is to implement and compare the performance of ASA by exploiting machine learning and deep learning models to automatically determine the sentiments by classifying them as positive or negative. For that, this study implements and evaluates a deep learning model namely the long short-term memory (LSTM) model, and three machine learning algorithms: Support vector machines (SVM), Logistic Regression (LR), K-Nearest neighbours (KNN). These classifiers are applied on the Arabic-Review (ARev) database that is manually annotated and collected from many Arabic resources. The results show that SVM and LR models are the best performing classifiers with an accuracy of 92% and 93% respectively.

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Section: Sentiment Analysis For the Arabic Languagementioning

confidence: 99%

Sentiment Analysis of Arabic Comments Using Machine Learning and Deep Learning Models

Fouadi¹,

Moubtahij²,

Lamtougui³

et al. 2022

INDJCSE

Self Cite

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show abstract

“…Deep learning is a subcategory of machine learning that exploits artificial neural networks. In the last decade, it has achieved impressive performance in the automatic processing of text [4] [5] and images [6], particularly in the medical field [7] [8]. In contrast to machine learning, deep learning does not require a pre-processing step for feature extraction [9] and hypothesis domain selection.…”

Section: Introductionmentioning

confidence: 99%

Detection of Lung and Colon Cancer from Histopathological Images: Using Convolutional Networks and Transfer Learning

Oubaalla,

ElMoubtahij,

EL AKKAD

2024

IJCDS

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Analyzing histopathology images to detect the presence of cancer cells is a very important task during cancer treatment. This task has traditionally been largely done by manual methods. Therefore, the results of these analyzes are highly dependent on the pathologist's skills and professional experience, wasting time and manpower. Automating this task using deep learning techniques will speed up the early detection of cancer cells. Interestingly, these techniques have led to impressive advances in image processing in various fields, including the medical field. In this paper, we first attempt to highlight the importance of using deep learning techniques to classify histopathological images, and have cited studies using LC25000 datasets to accomplish this task. We then compared twelve models based on pretrained VGG-16, ResNet, DenseNet and NasNet models. The overall accuracy in this study ranged from 95.99% to 99.98%, reaching 100% for some categories. The purpose of this article is to compare pretrained models, examine the impact of the number of layers on the performance of built models, and highlight the importance of using transfer learning techniques.

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“…In addition to that, CNN can also provide satisfactory performance with 1-dimentional data [4], [5], [6], [7], [8]. When working with time series data, Long Short-Term Memory (LSTM) algorithm can also learn easily temporal patterns and dependencies using memory cells and gates [9], [10], [11], [12], [13]. To find the most effective architecture, two different 1-Dimensional Convolutional Neural Network training strategies are evaluated and tested : training from scratch strategy and transfer learning strategy.…”

Section: Introductionmentioning

confidence: 99%

Ethereum Cryptocurrency Entry Point and Trend Prediction using Bitcoin Correlation and Multiple Data Combination

Zaar¹,

Benaya²,

Moubtahij³

et al. 2023

IJACSA

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Deep learning methods have achieved significant success in various applications, including trend signal prediction in financial markets. However, most existing approaches only utilize price action data. In this paper, we propose a novel system that incorporates multiple data sources and market correlations to predict the trend signal of Ethereum cryptocurrency. We conduct experiments to investigate the relationship between price action, candlestick patterns, and Ethereum-Bitcoin correlation, aiming to achieve highly accurate trend signal predictions. We evaluate and compare two different training strategies for Convolutional Neural Networks (CNNs), one based on transfer learning and the other on training from scratch. Our proposed 1-Dimensional CNN (1DCNN) model can also identify inflection points in price trends during specific periods through the analysis of statistical indicators. We demonstrate that our model produces more reliable predictions when utilizing multiple data representations. Our experiments show that by combining different types of data, it is possible to accurately identify both inflection points and trend signals with an accuracy of 98%.

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Applications of Deep Learning in Arabic Sentiment Analysis: Research Perspective

Cited by 8 publications

References 11 publications

Sentiment Analysis of Arabic Comments Using Machine Learning and Deep Learning Models

Sentiment Analysis of Arabic Comments Using Machine Learning and Deep Learning Models

Detection of Lung and Colon Cancer from Histopathological Images: Using Convolutional Networks and Transfer Learning

Ethereum Cryptocurrency Entry Point and Trend Prediction using Bitcoin Correlation and Multiple Data Combination

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