OCA: Opinion corpus for Arabic

Rushdi-Saleh, Mohammed; Martín–Valdivia, María Teresa; Ureña‐López, L. Alfonso; Perea‐Ortega, José M.

doi:10.1002/asi.21598

Cited by 218 publications

(162 citation statements)

References 26 publications

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“…SVM has outperformed other machine-learning techniques because of its primary advantages. For instance, generality of text categorisation dilemmas are linearly separable, it's robust in high-dimensional spaces and powerful when there is a sparse set of samples and any feature is pertinent (Rushdi-Saleh et al, 2011). The fundamental conception of SVM is to find a hyper-plane represented by a vector that does not only dissever the document vectors into a different class from those in other documents.…”

Section: Classification Methodsmentioning

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: Classification Methodsmentioning

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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“…It has outperformed other machine learning techniques due to the associated advantage. For instance, the powerful in highdimensional spaces [19].…”

Section: Classification Methodsmentioning

confidence: 99%

Visualising Arabic Sentiments and Association Rules in Financial Text

AL-Rubaiee¹,

Qiu²,

Li³

2017

ijacsa

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Abstract-Text mining methods involve various techniques, such as text categorization, summarisation, information retrieval, document clustering, topic detection, and concept extraction. In addition, because of the difficulties involved in text mining, visualisation techniques can play a paramount role in the analysis and pre-processing of textual data. This paper will present two novel frameworks for the classification and extraction of the association rules and the visualisation of financial Arabic text in order to realize both the general structure and the sentiment within an accumulated corpus. However, mining unstructured data with natural language processing (NLP) and machine learning techniques can be arduous, especially where the Arabic language is concerned, because of limited research in this area. The results show that our frameworks can readily classify Arabic tweets. Furthermore, they can handle many antecedent text association rules for the positive class and the negative class.

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“…It is collected from three different resources: Penn Arabic Treebank (PATB) which is a collection of news wire topics of different domains, Wikipedia user talk pages and a user conversation on web forum sites. Opinion corpus for Arabic (OCA) is built by Rushdi-Saleh [22]. The data is collected from several blogs of movie reviews, obtaining a total of 500 comments (250 positive and 250 negative).…”

Section: Related Workmentioning

confidence: 99%