Journal ArticleDOI
OCA: Opinion corpus for Arabic
TLDR
A new Arabic corpus for the OM task that has been made available to the scientific community for research purposes is presented, which contains 500 movie reviews collected from different web pages and blogs in Arabic.Abstract:
Sentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Nave Bayes. The results obtained are very promising and we are encouraged to continue this line of research. © 2011 Wiley Periodicals, Inc.read more
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Proceedings ArticleDOI
ASTD: Arabic Sentiment Tweets Dataset
TL;DR: ASTD, an Arabic social sentiment analysis dataset gathered from Twitter, consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective mixed.
Proceedings ArticleDOI
Arabic sentiment analysis: Lexicon-based and corpus-based
TL;DR: This paper starts by building a manually annotated dataset and then takes the reader through the detailed steps of building the lexicon, which addresses both approaches to SA for the Arabic language.
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Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews
TL;DR: The state-of-the-art approaches based on supervised machine learning are presented to address the challenges of aspect-based sentiment analysis (ABSA) of Arabic Hotels’ reviews and the SVM approach outperforms the other deep RNN approach in the research investigated tasks.
Proceedings ArticleDOI
Sentence-level Arabic sentiment analysis
Amira Shoukry,Ahmed Rafea +1 more
TL;DR: This paper shows an application on Arabic sentiment analysis by implementing a sentiment classification for Arabic tweets, which is collected from the social network Twitter.
Journal ArticleDOI
Sentiment analysis in Arabic: A review of the literature
TL;DR: A review of the major works that have dealt with Sentiment Analysis in Arabic, namely supervised, unsupervised and hybrid, finds that the results that these studies achieved are interesting but divergent.
References
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TL;DR: In this article, the authors deal with the nature and theory of meaning and present a new, objective method for its measurement which they call the semantic differential, which can be adapted to a wide variety of problems in such areas as clinical psychology, social psychology, linguistics, mass communications, esthetics, and political science.
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Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.
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Machine learning in automated text categorization
TL;DR: This survey discusses the main approaches to text categorization that fall within the machine learning paradigm and discusses in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
Book
Opinion Mining and Sentiment Analysis
Bo Pang,Lillian Lee +1 more
TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.