Sentiment analysis in Arabic: A review of the literature

Boudad, Naaima; Faizi, Rdouan; Thami, Rachid Oulad Haj; Chiheb, Raddouane

doi:10.1016/j.asej.2017.04.007

Cited by 204 publications

(116 citation statements)

References 42 publications

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“…For correct pronunciation, the diacritical marks are used and are placed around the letters. Arabic can appear in different forms: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic [73]. Ibrahim et al [74] claimed that the main dialects in the Arabic word are: Gulf, Iraqi, Moroccan, Levantine, Yemeni, and Egyptian.…”

Section: Resultsmentioning

confidence: 99%

A Review on Event-Based Epidemic Surveillance Systems that Support the Arabic Language

Alruily¹

2018

ijacsa

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With the revolution of the internet, many eventbased systems have been developed for monitoring epidemic threats. These systems rely on unstructured data gathered from various online sources. Moreover, some systems are able to handle more than one language to cover all news reports related to disease outbreaks worldwide. The aim of this paper is to examine existing systems in terms of supporting the Arabic language. The 28 identified systems were evaluated based on different criteria. The results of this evaluation show that only 5 systems support the Arabic language using translation tools; hence, disease outbreaks in news reports written in Arabic are not directly processed. In other words, no existing event-based system in the literature has yet been developed specifically for Arabic health news reports to monitor epidemic diseases.

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Section: Resultsmentioning

confidence: 99%

A Review on Event-Based Epidemic Surveillance Systems that Support the Arabic Language

Alruily¹

2018

ijacsa

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“…The unsupervised approach includes machine learning problems that do not require a predefined, labeled set as in the clustering problems. Lexicon-based sentiment analysis techniques are considered an unsupervised technique [14]. Little attention has been given to lexiconsbased approach for comparative sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis of Arabic comparative opinions

et al. 2019

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The rapid development of social media platforms allowed opinion mining research to increase significantly. Opinion mining/sentiment analysis is the process of extracting subjective information from opinions that imply single sentiment. Comparative opinion mining is a sub-field of opinion mining that deals with multi-sentiment opinions. Such opinions are expressed by comparing several entities to each other. The sentiment of a comparative relation is recognized by identifying the relation's direction and thus the preferred entity. This paper proposes an unsupervised sentiment analysis technique for Arabic comparative opinions to identify the preferred entity. The proposed technique considers three main elements when analyzing comparative opinions: The type of comparative keywords, the existence of features in the opinion, and the entities' position to the comparative keyword. Five main categories are proposed for classifying comparative keywords, which facilitates the analysis of each comparative sentence. The proposed technique limits the need for human interference to the initial steps of preparing the lexicons, collecting and categorizing comparative keywords. Furthermore, the proposed technique handles opinions that do not contain features at all. The results are very promising with a total average of 96.5% f-measure of correctly identified sentiment.

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“…Limited research has been conducted on Arabic sentiment analysis, so this is a field that is still in its early stages [6]. However, Boudad et al [7], [8] reviewed the challenges and open issues that need to be addressed and explored in more depth to improve Arabic sentiment analysis, finding that these include domain, method of sentiment classification, data preprocessing, and level of sentiment analysis. They show that, in contrast to work on the English language, work on Arabic sentiment analysis is still in the early stages, and there are a lot of potential approaches and techniques that have not yet been explored.…”

Section: Arabic Sentiment Analysismentioning

confidence: 99%

Techniques for Improving the Labelling Process of Sentiment Analysis in the Saudi Stock Market

AL-Rubaiee¹,

Qiu²,

Alomar³

et al. 2018

ijacsa

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Sentiment analysis is utilised to assess users' feedback and comments. Recently, researchers have shown an increased interest in this topic due to the spread and expansion of social networks. Users' feedback and comments are written in unstructured formats, usually with informal language, which presents challenges for sentiment analysis. For the Arabic language, further challenges exist due to the complexity of the language and no sentiment lexicon is available. Therefore, labelling carried out by hand can lead to mislabelling and misclassification. Consequently, inaccurate classification creates the need to construct a relabelling process for Arabic documents to remove noise in labelling. The aim of this study is to improve the labelling process of the sentiment analysis. Two approaches were utilised. First, a neutral class was added to create a framework of reliable Twitter tweets with positive, negative, or neutral sentiments. The second approach was improving the labelling process by relabelling. In this study, the relabelling process applied to only seven random features (positive or negative): "earnings" ‫,)ارباح(‬ "losses" ‫,)خسائر(‬ "green colour" ‫,)باللون_االخضر(‬ "growing" ‫,)زياده(‬ "distribution" ‫,)توزيع(‬ "decrease" ‫,)اوخفاض(‬ "financial penalty" ‫,)غرامة(‬ and "delay" ‫.)تاجيل(‬ Of the 48 tweets documented and examined, 20 tweets were relabelled and the classification error was reduced by 1.34%.

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Sentiment analysis in Arabic: A review of the literature

Cited by 204 publications

References 42 publications

A Review on Event-Based Epidemic Surveillance Systems that Support the Arabic Language

A Review on Event-Based Epidemic Surveillance Systems that Support the Arabic Language

Sentiment analysis of Arabic comparative opinions

Techniques for Improving the Labelling Process of Sentiment Analysis in the Saudi Stock Market

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