A Generic Approach for Extracting Aspects and Opinions of Arabic Reviews

Ismail, Shimaa; Alsammak, Abdelwahab K.; El-Shishtawy, Tarek

doi:10.1145/2908446.2908467

Cited by 20 publications

(8 citation statements)

References 19 publications

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“…Abd-Elhamid et al [22] proposed a feature-based SA approach for mining Arabic user generated reviews; the sentiments and features were extracted automatically from a set of unannotated reviews using a set of linguistic rules. Ismail et al [23] proposed a generic approach for extracting the aspects of entities and their opinions, this approach relies on the idea that the entities aspects and their opinion-bearing words are usually correlative; these words are used to orient the process of extracting the entity aspects, for this, the authors added sentiment tags on the roots and patterns of an Arabic lexicon.…”

Section: Related Workmentioning

confidence: 99%

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

Behdenna¹,

Belalem²,

Barigou³

2022

IJCDS

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Currently, one of the most important and difficult areas of research is Arabic Sentiment analysis. Aspect extraction is the most important task in aspect-based sentiment analysis. In this work, we propose an aspect-based sentiment analysis approach for Arabic language. The basic idea of the proposed approach is to use the description logics to identify explicit aspects as well as their polarities. This approach was evaluated using the publicly available human annotated Arabic dataset (HAAD) based on Semantic Evaluation 2014 workshop (SemEval-2014) along with its baseline experiments conducted by HAAD providers. Experimental results showed that the proposed approach significantly improves the performance compared with baseline approach. The F-measure was improved by 19% and 55% for the aspect terms extraction and aspect term polarity tasks respectively.

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Section: Related Workmentioning

confidence: 99%

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

Behdenna¹,

Belalem²,

Barigou³

2022

IJCDS

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“…Most studies focused on ASA applications in a limited set of domains, such as politics [15,48,62,89], hotel [79,113], business and economy [12,20,129], arts and books [29,32,92], entertainment and movies [71,73,99], and sport [81,96] Several papers were published [12,74,83] to study ASA for several purposes such as building Arabic senti-lexicon, designing a framework for ASA, and comparing two free online SA tools that support Arabic. ese studies involved collecting small datasets with size less than 3000 tweets that are relevant to several domains such as education, sports, and politics.…”

Section: Arabic Sentiment Analysis Applicationsmentioning

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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“…This framework uses the semantic of ontology and lexi-cons in the identification of opinion target and their polarity. In (Ismail et al, 2016) pro-posed a generic approach that extracts the entity aspects and their attitudes for reviews written in modern standard Arabic. A two-stage method for annotating targets of opin-ions was developed in ( [Farra et al, 2015) by using the crowdsourcing tool Amazon Mechanical Turk.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Arabic Tweets: Opinion Target Extraction

Behdenna¹,

Barigou²,

Belalem³

2018

Journal of Digital Information Management

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Due to the increased volume of Arabic opinionated posts on different social media, Arabic sentiment analysis is viewed as an important research field. Identifying the target or the topic on which opinion has been expressed is the aim of this work. Opinion target identification is a problem that was generally very little treated in Arabic text. In this paper, an opinion target extraction method from Arabic tweets is proposed. First, as a preprocessing phase, several feature forms from tweets are extracted to be examined. The aim of these forms is to evaluate their impacts on accuracy. Then, two classifiers, SVM and Naïve Bayes are trained. The experiment results show that, with 500 tweets collected and manually tagged, SVM gives the highest precision and recall (86%).

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A Generic Approach for Extracting Aspects and Opinions of Arabic Reviews

Cited by 20 publications

References 19 publications

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

Arabic Sentiment Analysis: A Systematic Literature Review

Sentiment Analysis of Arabic Tweets: Opinion Target Extraction

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