Morphology Based Arabic Sentiment Analysis of Book Reviews

Ariss, Omar El; Alnemer, Loai M.

doi:10.1007/978-3-319-77116-8_9

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Preprocessing [17] Normalization, POS tagging [24][25][26][27] Stemming [28][29][30][31][32][33] Text cleaning [34][35][36][37][38][39] Normalization, stemming, stop words removal [40][41][42] Text cleaning, normalization, stemming, stop words removal [43][44][45] Normalization Text cleaning, normalization, tokenization, stemming, stop words removal [49][50][51][52] Normalization, tokenization [53,54] Text cleaning, normalization, tokenization [55,56] Normalization, tokenization, POS tagging [13,[57][58][59][60][61][62][63][64] Normalization, tokenization, stemming, stop words removal [65,66] Normalization, tokenization, stemming, lemmatization [67,68] Text cleaning, normalization, tokenization, stemming [69] Text cleaning, tokenization, stemming, negation detection [70]…”

Section: Referencementioning

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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Section: Referencementioning

confidence: 99%

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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“…SVM-RBF achieved the highest accuracy that is 88%. In [23] and [24], both studies used NB and SVM to classify standard and dialect Arabic in book reviews and Facebook comments. Both studies concluded that NB outperformed SVM with the used datasets.…”

Section: Arabic Sentimental Analysismentioning

confidence: 99%

“…architecture of deep learning, are used. These classifiers were chosen in specific as they proved high performance and accuracy rates in many similar studies [21][22][23]26].…”

Section: Data Preparationmentioning

confidence: 99%

A two-level deep learning approach for emotion recognition in Arabic news headlines

Elsayed

Chaffar

Belhaouari

et al. 2020

International Journal of Computers and Applications

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Going online has created more opportunities for newspapers to present breaking news in a timely manner. Concentrating in spreading more bad news increases the feeling of danger and depression in the society. Some authors believe on tendency of some media to be focused on sharing the bad events in life rather than the good ones because of the impact and the attraction over the audience is more significant. Sentiment analysis work has not been recognized, proposed, or documented on Arabic news because of the challenges that Arabic raises as a language including the different Arabs' dialects and its complex grammatical structure. With the emerging flow of news that cause a global panic and anxiety worldwide, this study focuses on the serious need to identify what effective role could machine learning classifiers have in the early detection process of the psychology impact on the readers by the daily news headlines. In this work, a dataset of news headlines were gathered from top popular online Arabic news sites and were annotated to seven emotional categories: anger, disgust, fear, happiness, neutral, sadness, and surprise. A convolutional neural network-based two-level approach was proposed for sentiment classification. The performance of the proposed approach was compared to six machine learning classifiers (zeroR, k-nearest neighbor, decision trees, naïve Bayes, random forest and Support vector machine) and showed better accuracy, precision, and recall.

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