Arabic sentiment analysis: Lexicon-based and corpus-based

Abdulla, Nawaf; Ahmed, Nizar A.; Shehab, Mohammed A.; Al‐Ayyoub, Mahmoud

doi:10.1109/aeect.2013.6716448

Cited by 232 publications

(126 citation statements)

References 13 publications

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“…(Abdul-Mageed et al, 2014) is also related to our work in that the we also investigate ways to best represent lexical information, yet on newswire data rather than social media. A number of studies have reported models using n-gram features after preprocessing input data (Abdulla et al, 2013;Aly and Atiya, 2013;ElSahar and ElBeltagy, 2015;Mourad and Darwish, 2013;Saleh et al, 2011). The focus of our work is different in that we seek to break the space of lexical input based on syntactic criteria and introduce a method to weigh the informativity of the resulting spaces via feature selection.…”

Section: Related Workmentioning

confidence: 99%

Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space

Abdul-Mageed¹

2017

Proceedings of the Third Arabic Natural Language Processing Workshop

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Although there is by now a considerable amount of research on subjectivity and sentiment analysis on morphologicallyrich languages, it is still unclear how lexical information can best be modeled in these languages. To bridge this gap, we build effective models exploiting exclusively gold and machine-segmented lexical input and successfully employ syntactically motivated feature selection to improve classification. Our best models achieve significantly above the baselines, with 67.93% and 69.37% accuracies for subjectivity and sentiment classification respectively.

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Section: Related Workmentioning

confidence: 99%

Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space

Abdul-Mageed¹

2017

Proceedings of the Third Arabic Natural Language Processing Workshop

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“…Other researches have focused upon the lexicon-based approach, which, typically, is used less often in Arabic sentiment analysis because of the low number of existing Arabic sentiment lexicons. The main challenge here is in building lexicons for informal words, as [1] [16] [17] and [18]. These studies encourage researchers to contribute more extensively to the field.…”

Section: A Sentiment Analysis In Informal Arabic Languagementioning

confidence: 99%

Discovering Semantic and Sentiment Correlations using Short Informal Arabic Language Text

AlOtaibi¹,

Khan²

2017

ijacsa

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Abstract-Semantic and Sentiment analysis have received a great deal of attention over the last few years due to the important role they play in many different fields, including marketing, education, and politics. Social media has given tremendous opportunities for researchers to collect huge amount of data as input for their semantic and sentiment analysis. Using twitter API, we collected around 4.5 million Arabic tweets and used them to propose a novel automatic unsupervised approach to capture patterns of words and sentences of similar contextual semantics and sentiment in informal Arabic language at word and sentence levels.

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“…In this research, twitter sentiments are analyzed with Lexical based classification using Naive Bayes (NB) and Support Vector Machines (SVM). An analysis based on Lexicon and Corpus for Arabic sentiments is presented in [16]. This research starts by building a manually annotated dataset and then takes the reader through the detailed steps of building the lexicon.…”

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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Arabic sentiment analysis: Lexicon-based and corpus-based

Cited by 232 publications

References 13 publications

Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space

Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space

Discovering Semantic and Sentiment Correlations using Short Informal Arabic Language Text

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

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