Arabic Sentiment Analysis: A Survey

Assiri, Adel; Emam, Ahmed; Al-Dossari, Hmood

doi:10.14569/ijacsa.2015.061211

Cited by 32 publications

(41 citation statements)

References 58 publications

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“…Labelled data are fed into the machine learning algorithm to build a classification model, which in turn can predict the label for unforeseen instances. Naive Bayes and SVM are commonly used for sentiment classification, with satisfactory results [7], they have showed good accuracy in sentiment polarity classification in various languages [11], such as English, Chinese and Arabic.…”

Section: Proposed Modelmentioning

confidence: 99%

Subjective Sentiment Analysis for Arabic Newswire Comments

Bessou¹,

Aberkane²

2019

Journal of Digital Information Management

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This paper presents an approach based on supervised machine learning methods to discriminate between positive, negative and neutral Arabic reviews in online newswire. The corpus is labeled for subjectivity and sentiment analysis (SSA) at the sentence-level. The model uses both count and TF-IDF representations and apply six machine learning algorithms; Multinomial Naïve Bayes, Support Vector Machines (SVM), Random Forest, Logistic Regression, Multi-layer perceptron and k-nearest neighbors using uni-grams, bi-grams features. With the goal of extracting users' sentiment from written text.Experimental results showed that n-gram features could substantially improve performance; and showed that the Multinomial Naïve Bayes approach is the most accurate in predicting topic polarity. Best results were achieved using count vectors trained by combination of word-based uni-gramsand bi-grams with an overall accuracy of 85.57% over two classes and 65.64% over three classes.

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Section: Proposed Modelmentioning

confidence: 99%

Subjective Sentiment Analysis for Arabic Newswire Comments

Bessou¹,

Aberkane²

2019

Journal of Digital Information Management

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“…It is worth mentioning that the highest proportion of available resources and research publications in Arabic SA are devoted to MSA (Assiri et al, 2015). Regarding Arabic dialects, the Middle Eastern and Egyptian dialects received the lion's share of all research effort and funding.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments

Medhaffar¹,

Bougares²,

Estève³

et al. 2017

Proceedings of the Third Arabic Natural Language Processing Workshop

123

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Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political interest. In this paper we focus on SA of the Tunisian dialect. We use Machine Learning techniques to determine the polarity of comments written in Tunisian dialect. First, we evaluate the SA systems performances with models trained using freely available MSA and Multi-dialectal data sets. We then collect and annotate a Tunisian dialect corpus of 17.000 comments from Facebook. This corpus shows a significant improvement compared to the best model trained on other Arabic dialects or MSA data. We believe that this first freely available 12 corpus will be valuable to researchers working in the field of Tunisian Sentiment Analysis and similar areas.

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“…Hence, sentiment analysis should consider the difference in morphology and rules to get the desired results. Sentiment analysis has been investigated sufficiently in English language resulting in two derived methodologies: Corpus-based and lexicon-based [7]. Arabic language includes a huge number of words synonyms resulting of data sparsity [8].…”

Section: Related Workmentioning

confidence: 99%

Mining Trending Hash Tags for Arabic Sentiment Analysis

Almurtadha¹

2018

ijacsa

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Abstract-People text millions of posts everyday on microblogging social networking especially Twitter which make microblogs a rich source for public opinions, customer's comments and reviews. Companies and public sectors are looking for a way to measure the public response and feedback on particular service or product. Sentiment analysis is an encouraging technique capable to sense the public opinion in a fast and less cost tactic than traditional survey methods like questionnaires and interviews. Various sentiment methods were developed in many languages, such as English and Arabic with much more studies in the first one. Sometime, hash tags are misleading or may have a title that does not really reflects the subject. Tweets in trend hash tags may contain keyword or topics titles better represent the subject of the hash tag. This research aims at proposing an approach to explore Twitter Hash tag trends to retrieve tweets, group retrieved tweets to learn topics' profiles, do sentiment analysis to test the subjectivity of tweets then develop a prediction model using deep learning to classify a new tweet to the appropriate topic profile. Arabic hash tags trends have been used to evaluate the proposed approach. The performance of the proposed approach (clustering topics within hashtag trend to learn topics profiles then do sentiment analysis) shows better accuracy than sentiment analysis without clustering the topics.

show abstract

Arabic Sentiment Analysis: A Survey

Cited by 32 publications

References 58 publications

Subjective Sentiment Analysis for Arabic Newswire Comments

Subjective Sentiment Analysis for Arabic Newswire Comments

Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments

Mining Trending Hash Tags for Arabic Sentiment Analysis

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