scholarly journals A Review on Sentiment Analysis in Arabic Using Document Level

2018 ◽  
Vol 7 (3.13) ◽  
pp. 128 ◽  
Author(s):  
Ibrahim Awajan ◽  
Mumtazimah Mohamad
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Web Contents ◽  
Considerable Concentration ◽  
Arabic Sentiment Analysis ◽  
Document Level

With the rapid increase of web contents and the spread of social media and microblogs such as Twitter and etc.., the focus on the sentiment analysis (SA) is deeply being studied. The research on Arabic sentiment analysis is progressing very slow in compared to English sentiment analysis. The former has recently attracted a considerable concentration of researchers. In this respect, this paper aims to present a brief review of some major works that have addressed the document-level sentiment analysis in Arabic. This review includes research and studies published during the period of 2011-2017 for different approaches of document-level sentiment analysis.   

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
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.

scholarly journals Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Hala Mulki ◽  
Hatem Haddad ◽  
Mourad Gridach ◽  
Ismail Babaoğlu
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Empirical Evaluation ◽  
Classification Performance ◽  
Analysis Framework ◽  
Named Entities ◽  
The Public ◽  
Considerable Impact ◽  
Arabic Sentiment Analysis

Social media reflects the attitudes of the public towards specific events. Events are often related to persons, locations or organizations, the so-called Named Entities (NEs). This can define NEs as sentiment-bearing components. In this paper, we dive beyond NEs recognition to the exploitation of sentiment-annotated NEs in Arabic sentiment analysis. Therefore, we develop an algorithm to detect the sentiment of NEs based on the majority of attitudes towards them. This enabled tagging NEs with proper tags and, thus, including them in a sentiment analysis framework of two models: supervised and lexicon-based. Both models were applied on datasets of multi-dialectal content. The results revealed that NEs have no considerable impact on the supervised model, while employing NEs in the lexicon-based model improved the classification performance and outperformed most of the baseline systems.

scholarly journals Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Abdullah Al-Hashedi ◽  
Belal Al-Fuhaidi ◽  
Abdulqader M. Mohsen ◽  
Yousef Ali ◽  
Hasan Ali Gamal Al-Kaf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Word Embedding ◽  
Ensemble Classifiers ◽  
Conspiracy Theories ◽  
Machine Learning Model ◽  
Arabic Sentiment Analysis ◽  
The Impact

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.

scholarly journals Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cost Savings ◽  
Mass Scale ◽  
Tourism Sector ◽  
The Social ◽  
Analytical Tools ◽  
Media Data

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.

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