Sentence-Level and Document-Level Sentiment Mining for Arabic Texts

Farra, Noura; Challita, Elie; Assi, Rawad Abou; Hajj, Hazem

doi:10.1109/icdmw.2010.95

Cited by 142 publications

(95 citation statements)

References 5 publications

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“…Even though they emphasised that it is a Natural Language Processing approach (NLP) that works relatively well for other languages, the main limitation of this approach is that it is not applicable to the Arabic language. The main reason for that is the morphological richness of Arabic as a language (Pang and Lee, 2008;Cambria et al, 2013;Duwairi et al, 2014;Farghaly and Shaalan, 2009;Farra et al, 2010). In fact, the Arabic informal (colloquial) language lacks structure and is difficult to standardise.…”

Section: The Uniqueness Of the Arabic Languagementioning

confidence: 99%

Sentiment Analysis of Arabic Tweets in e-Learning

AL-Rubaiee

Qiu

Alomar

et al. 2016

Journal of Computer Science

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Abstract:In this study, we present the design and implementation of Arabic text classification in regard to university students' opinions through different algorithms such as Support Vector Machine (SVM) and Naive Bayes (NB). The aim of the study is to develop a framework to analyse Twitter "tweets" as having negative, positive or neutral sentiments in education or, in other words, to illustrate the relationship between the sentiments conveyed in Arabic tweets and the students' learning experiences at universities. Two experiments were carried out, one using negative and positive classes only and the other one with a neutral class. The results show that in Arabic, a sentiments SVM with an n-gram feature achieved higher accuracy than NB both with using negative and positive classes only and with the neutral class.

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Section: The Uniqueness Of the Arabic Languagementioning

confidence: 99%

Sentiment Analysis of Arabic Tweets in e-Learning

AL-Rubaiee

Qiu

Alomar

et al. 2016

Journal of Computer Science

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“…This can require a highly development of tools to develop the morphology and structure of Arabic language. So, the authors of [33] present a sentence classification to blogs, reviews, and tweets with regards to the given target. Different methods are proposed to address the problem of unbalanced sentiment classification in Arabic context [34].…”

Section: Arabic Setiment Classificationmentioning

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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“…For instance, authors in [34] used movie reviews written in English after translating them and modifying the sentence structure to fit the classifier used.…”

Section: The Arabic Languagementioning

confidence: 99%