Arabic Sentiment Analysis towards Feelings among Covid-19 Outbreak Using Single and Ensemble Classifiers

Sentiment analysis has recently become increasingly important with a massive increase in online content. It is associated with the analysis of textual data generated by social media that can be easily accessed, obtained, and analyzed. With the emergence of COVID-19, most published studies related to COVID-19’s conspiracy theories were surveys on the people's sentiments and opinions and studied the impact of the pandemic on their lives. Just a few studies utilized sentiment analysis of social media using a machine learning approach. These studies focused more on sentiment analysis of Twitter tweets in the English language and did not pay more attention to other languages such as Arabic. This study proposes a machine learning model to analyze the Arabic tweets from Twitter. In this model, we apply Word2Vec for word embedding which formed the main source of features. Two pretrained continuous bag-of-words (CBOW) models are investigated, and Naïve Bayes was used as a baseline classifier. Several single-based and ensemble-based machine learning classifiers have been used with and without SMOTE (synthetic minority oversampling technique). The experimental results show that applying word embedding with an ensemble and SMOTE achieved good improvement on average of F1 score compared to the baseline classifier and other classifiers (single-based and ensemble-based) without SMOTE.

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BERT Multilingual and Capsule Network for Arabic Sentiment Analysis

2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) ◽

10.1109/iccceee49695.2021.9429568 ◽

2021 ◽

Author(s):

Zeinab Obied ◽

Aiman Solyman ◽

Atta Ullah ◽

Ahmed Fat'hAlalim ◽

Alhag Alsayed

Keyword(s):

Sentiment Analysis ◽

Arabic Sentiment Analysis

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Arabic Sentiment Analysis Using Deep Learning and Ensemble Methods

Arabian Journal for Science and Engineering ◽

10.1007/s13369-021-05475-0 ◽

2021 ◽

Author(s):

Amal Alharbi ◽

Manal Kalkatawi ◽

Mounira Taileb

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Ensemble Methods ◽

Arabic Sentiment Analysis

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Arabic Sentiment Analysis Approaches: An Overview

2020 X International Conference on Virtual Campus (JICV) ◽

10.1109/jicv51605.2020.9375763 ◽

2020 ◽

Author(s):

Youssra Zahidi ◽

Yacine EL Younoussi ◽

Yassine AL-Amrani

Keyword(s):

Sentiment Analysis ◽

Arabic Sentiment Analysis

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Polarity Classification of Arabic Sentiments

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2016070103 ◽

2016 ◽

Vol 11 (3) ◽

pp. 32-49 ◽

Cited By ~ 5

Author(s):

Mohammed N. Al-Kabi ◽

Heider A. Wahsheh ◽

Izzat M. Alsmadi

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Operating Characteristic ◽

Opinion Mining ◽

Online Social Network ◽

The Social ◽

Polarity Classification ◽

Arabic Sentiment Analysis ◽

Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

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