Identification of Opinions in Arabic Texts Using Ontologies

Lazhar, Farek; Tlili, Yamina

doi:10.4172/2165-7866.1000108

Cited by 8 publications

(1 citation statement)

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“…An average accuracy of 79% was claimed to be achieved. Ontologies were also used in References [93,103,135,280,377]. Entries of the ArSeLEX lexicon were used along with a set of linguistically and syntactically motivated features, including contextual intensifiers, contextual shifters, and negation particles, to train a SVM sentiment classifier and achieved high performances on small Twitter, comments, and reviews datasets, written in both MSA and Egyptian dialect [250].…”

Section: Feature Engineering "Supervised" Approachesmentioning

confidence: 99%

A Survey of Opinion Mining in Arabic

Badaro

Baly

Hajj

et al. 2019

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

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Section: Feature Engineering "Supervised" Approachesmentioning

confidence: 99%