Arabic sentiment analysis of YouTube comments

Al-Tamimi, Abdel-Karim; Shatnawi, Anas; Bani-Issa, Esraa

doi:10.1109/aeect.2017.8257766

Cited by 22 publications

(21 citation statements)

References 26 publications

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“…Naïve Bayes is a supervised machine learning classifier based on the statistical method, used for classification problems, by finding the probabilities of attributes [ 32 ]. It has several uses and applications, such as diagnostic classifications, classifying texts and documents, sorting and identifying spam emails, and predictive models [ 19 , 27 ]. NB classifiers work by finding properties, assuming that features are independent of each other.…”

Section: Methodsmentioning

confidence: 99%

“…Several machine learning classifiers were applied in the work presented in Al-Tamimi et al [ 19 ], which aimed at analyzing sentiments of 5986 Arabic YouTube comments, classified to positive and negative sentiments. SVM based Radial Basis Function (RBF), K-nearest neighbors (KNN), and Bernoulli NB models were used to classify raw and normalized datasets.…”

Section: Related Workmentioning

confidence: 99%

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A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

Aljameel

Alabbad

Alzahrani

et al. 2020

IJERPH

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In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual’s awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency–Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

Aljameel

Alabbad

Alzahrani

et al. 2020

IJERPH

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“…The researchers of [23] used tweets written in modern standard Arabic (MSA) about terrorism and political events that occurred in the Arab countries and classified them manually. In [24], a Machine Learning model to evaluate Arabic tweets using two machine learning algorithms Naïve Bayes and Decision Tree was built, and around 8053 Arabic YouTube comments were collected and labeled manually by [9] and some volunteering graduate students.…”

Section: Related Workmentioning

confidence: 99%

“…Since Arabic is the fourth spoken language and the biggest language from the Semitic dialect family and one of the fastest growing languages on the Internet, the number of Arab users has grown by a rate of 6.6% yearly and Arabic sentiment analysis was identified as an important research part in the field of SA [9]. Two types of Arabic languages are used nowadays: 1) Formal written Arabic: which consists of Modern Standard Arabic (MSA) as well as the Classical Arabic, and 2) Informal Arabic (day-to-day spoken Arabic): this type does not follow any grammatical rules or spelling standards like that of the formal types.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

2021

IJATCSE

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The increased usage of the Internet and social networks allowed and enabled people to express their views, which have generated an increasing attention lately. Sentiment Analysis (SA) techniques are used to determine the polarity of information, either positive or negative, toward a given topic, including opinions. In this research, we have introduced a machine learning approach based on Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) classifiers, to find and classify extreme opinions in Arabic reviews. To achieve this, a dataset of 1500 Arabic reviews was collected from Google Play Store. In addition, a two-stage Classification process was applied to classify the reviews. In the first stage, we built a binary classifier to sort out positive from negative reviews. In the second stage, however we applied a binary classification mechanism based on a set of proposed rules that distinguishes extreme positive from positive reviews, and extreme negative from negative reviews. Four major experiments were conducted with a total of 10 different sub experiments to fulfill the two-stage process using different X-validation schemas and Term Frequency-Inverse Document Frequency feature selection method. Obtained results have indicated that SVM was the best during the first stage classification with 30% testing data, and NB was the best with 20% testing data. The results of the second stage classification indicated that SVM has scored better results in identifying extreme positive reviews when dealing with the positive dataset with an overall accuracy of 68.7% and NB showed better accuracy results in identifying extreme negative reviews when dealing with the negative dataset, with an overall accuracy of 72.8%.

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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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Arabic sentiment analysis of YouTube comments

Cited by 22 publications

References 26 publications

A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

Arabic Sentiment Analysis: A Systematic Literature Review

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