Sentiment analysis of arabic social media content: a comparative study

Zhu, Liyun; Li, Tingting; Liu, Jinhuai

doi:10.1109/icist.2013.6747520

Cited by 8 publications

(10 citation statements)

References 9 publications

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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%

“…Since its introduction since a decade, ASA has become one of the most popular forms of information extraction from the reviews. ese reviews contributed in many benefits, such as showing the product brand or service valuable insights [12][13][14][15], identifying potential product advocates or social media influencers [16][17][18][19], and detecting e-mail spam [20]. Consequently, ASA has been studied in various contexts, and a large number of studies has been published on the topic.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Arabic Sentiment Analysis Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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“…Unfortunately, these corpora are not easily found or not designed for Arabic Text Categorization such as Al-Dostor newspapers [1]. In other words, the existing corpora ( [2], [3] and [4]) need modification before the usage. For example, increasing the number of classes, performing preprocessing techniques and providing the corpus with specific formats to facilitate the integration of the data.…”

Section: Introductionmentioning

confidence: 99%

NADA: New Arabic Dataset for Text Classification

Alalyani¹,

Marie-Sainte²

2018

ijacsa

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In the recent years, Arabic Natural Language Processing, including Text summarization, Text simplification, Text Categorization and other Natural Language-related disciplines, are attracting more researchers. Appropriate resources for Arabic Text Categorization are becoming a big necessity for the development of this research. The few existing corpora are not ready for use, they require preprocessing and filtering operations. In addition, most of them are not organized based on standard classification methods which makes unbalanced classes and thus reduced the classification accuracy. This paper proposes a New Arabic Dataset (NADA) for Text Categorization purpose. This corpus is composed of two existing corpora OSAC and DAA. The new corpus is preprocessed and filtered using the recent state of the art methods. It is also organized based on Dewey decimal classification scheme and Synthetic Minority Over-Sampling Technique. The experiment results show that NADA is an efficient dataset ready for use in Arabic Text Categorization.

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“…Another sentiment analysis for Arabic social media is presented in [17]. In this research, Arabic sentiment analysis is conducted using a small dataset consisting of 1,000 Arabic reviews and comments collected from Facebook and Twitter social network websites.…”

Section: Arabic Setiment Analysismentioning

confidence: 99%

An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data

Mostafa¹

2017

IJCA

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Sentiment analysis is a recent advance in text mining applications for analyzing textual data according to orientation of human comments to determine whether they are positive, negative, or neutral. Different data mining techniques and algorithms such as support vector machine, naïve Bayes, decision tree, k-nearest neighbor and other techniques are used for analyzing textual data. These techniques are evaluated based on Arabic language due to its richness and diversity that can lead to difficulties in analyzing and mining large number of morphological and linguistic words that can lead to different meaning. This research provides sophisticated categorization of most or all recent articles according to the algorithms used in analyzing sentiment data. A comparison table for the proposed algorithms is presented that explains each algorithm and its use in mining and analysis of Arabic textual data and provides different evaluation for each sentiment analysis and classification algorithm according to different categories such as sentiment type, feature selection, sentiment polarity, domain oriented , data scope and data source, algorithm used in the sentiment analysis or classification, and the best algorithm result during the analysis and mining process. The experimental results explain that support vector machine algorithm presents high accuracy with approximately 77% when compared to other text mining algorithms. Different algorithms of sentiment analysis and classifications are evaluated based on their use in Arabic language which has not been evaluated before.

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Sentiment analysis of arabic social media content: a comparative study

Cited by 8 publications

References 9 publications

Arabic Sentiment Analysis: A Systematic Literature Review

Arabic Sentiment Analysis: A Systematic Literature Review

NADA: New Arabic Dataset for Text Classification

An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data

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