Hybrid Deep Learning for Sentiment Polarity Determination of Arabic Microblogs

Al-Azani, Sadam; El-Alfy, El-Sayed M.

doi:10.1007/978-3-319-70096-0_51

Cited by 42 publications

(32 citation statements)

References 18 publications

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“…State-of-the-art methods used in the comparison are listed in Table 15.CNN-base: a CNN similar to the model described in Section 3.2 trained on Twitter word embeddings (Twt-CBOW) from [58]. A random configuration is used, where parameters such as filter sizes list, number of neurons, NFCS, initialization mode, and dropout rate were set to [3, 5, 7], 150, 100, uniform, and 0.7, respectively.Combined LSTM: a model proposed by Al-Azani and El-Alfy [57], where two long short-term memory (LSTM) networks were combined using different combination methods including: summation, multiplication, and concatenation.Stacking ensemble (eclf14): a model based on stacking ensemble presented in [21], where several classifiers were included in the training. The used ensemble-learning techniques are stochastic gradient descent (SGD) and nu-support vector classification (NuSVC).NuSVC: a model is employed in [7] as a classifier on AAQ dataset.SVM(bigrams): a suport vector machine classifier trained on TF-IDF as weighting scheme through bigrams was evaluated in [52] on AJGT dataset.…”

Section: Resultsmentioning

confidence: 99%

“…A random configuration is used, where parameters such as filter sizes list, number of neurons, NFCS, initialization mode, and dropout rate were set to [3, 5, 7], 150, 100, uniform, and 0.7, respectively. Combined LSTM: a model proposed by Al-Azani and El-Alfy [ 57 ], where two long short-term memory (LSTM) networks were combined using different combination methods including: summation, multiplication, and concatenation. Stacking ensemble (eclf14): a model based on stacking ensemble presented in [ 21 ], where several classifiers were included in the training.…”

Section: Resultsmentioning

confidence: 99%

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Arabic Sentiment Classification Using Convolutional Neural Network and Differential Evolution Algorithm

Dahou

Elaziz

Zhou

et al. 2019

Computational Intelligence and Neuroscience

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In recent years, convolutional neural network (CNN) has attracted considerable attention since its impressive performance in various applications, such as Arabic sentence classification. However, building a powerful CNN for Arabic sentiment classification can be highly complicated and time consuming. In this paper, we address this problem by combining differential evolution (DE) algorithm and CNN, where DE algorithm is used to automatically search the optimal configuration including CNN architecture and network parameters. In order to achieve the goal, five CNN parameters are searched by the DE algorithm which include convolution filter sizes that control the CNN architecture, number of filters per convolution filter size (NFCS), number of neurons in fully connected (FC) layer, initialization mode, and dropout rate. In addition, the effect of the mutation and crossover operators in DE algorithm were investigated. The performance of the proposed framework DE-CNN is evaluated on five Arabic sentiment datasets. Experiments’ results show that DE-CNN has higher accuracy and is less time consuming than the state-of-the-art algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Arabic Sentiment Classification Using Convolutional Neural Network and Differential Evolution Algorithm

Dahou

Elaziz

Zhou

et al. 2019

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…It is noticed that using the word embedding with DL models helped improving the results over the linear models, such as SVM, as it is suitable for large datasets and can be computationally efficient [33,90].…”

Section: Machine Learning Approachmentioning

confidence: 99%

“…As a result, the obtained accuracy for ASA was improved on several datasets. In addition, DL method for ASA was presented in [90]. e authors investigated several combinations of skip-gram and CBOW, including CNN and LSTM evaluated on two publicly available datasets.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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“…In Al-Azani and El-Alfy [55], five architectures were investigated including CNN, CNN-LSTM, simple LSTM, stacked LSTM and combined LSTM to analyse Arabic tweets. They employed dynamic and static CBOW and SG word embeddings to train the models.…”

Section: Sentiment Analysismentioning

confidence: 99%

Deep learning in Arabic sentiment analysis: An overview

Alharbi

Taileb

Kalkatawi

2019

Journal of Information Science

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Sentiment analysis became a very motivating area in both academic and industrial fields due to the exponential increase of the online published reviews and recommendations. To solve the problem of analysing and classifying those reviews and recommendations, several techniques have been proposed. Lately, deep neural networks showed promising outcomes in sentiment analysis. The growing number of Arab users on the Internet along with the increasing amount of published Arabic reviews and comments encouraged researchers to apply deep learning to analyse them. This article is a comprehensive overview of research works that utilised the deep learning approach for Arabic sentiment analysis.

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Hybrid Deep Learning for Sentiment Polarity Determination of Arabic Microblogs

Cited by 42 publications

References 18 publications

Arabic Sentiment Classification Using Convolutional Neural Network and Differential Evolution Algorithm

Arabic Sentiment Classification Using Convolutional Neural Network and Differential Evolution Algorithm

Arabic Sentiment Analysis: A Systematic Literature Review

Deep learning in Arabic sentiment analysis: An overview

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