A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining

Badaro, Gilbert; Baly, Ramy; Hajj, Hazem; Habash, Nizar; El-Hajj, Wassim

doi:10.3115/v1/w14-3623

Cited by 138 publications

(112 citation statements)

References 14 publications

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“…These features performed well after reduction via the Entropy-Weighted Genetic Algorithm (EWGA) (Abbasi et al, 2008). Sentiment lexicons also provided an additional source of features that proved useful for the task (Abdul-Mageed et al, 2011;Badaro et al, 2014;.…”

Section: Related Workmentioning

confidence: 99%

A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models

Baly¹,

Badaro²,

El-Khoury³

et al. 2017

Proceedings of the Third Arabic Natural Language Processing Workshop

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Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic's complex morphology.

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Section: Related Workmentioning

confidence: 99%

A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models

Baly¹,

Badaro²,

El-Khoury³

et al. 2017

Proceedings of the Third Arabic Natural Language Processing Workshop

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“…They [12] produced the first publicly available large scale Standard Arabic sentiment lexicon (Ar-SenL) using a combination of existing resources: English WordNet (EWN), Arabic Word-Net (AWN 2.0), English SentiWordNet (ESWN 3.0) and the Standard Arabic Morphological Analyzer (SAMA 3.1) [13]. They showed that using English-based linking produces, on average, superior performance in comparison to using the WordNet-based approach.…”

Section: Lexicon Building Of Other Languagesmentioning

confidence: 99%

Assigning Polarity Scores to Facebook Myanmar Movie Comments

Thant¹,

Aung²,

Htay³

et al. 2017

IJCA

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User-generated texts such as reviews, discussions or comments are valuable indicators of users' preferences. Apart from binary classification (positive or negative) of the reviews, some researchers calculated polarity scores that give a very concise summary and provide more information of the reviews. In this paper, a system for assigning polarity scores to Facebook Myanmar movie comments is proposed. Myanmar is a language with underdeveloped electric resources. As this is pioneering work for this combination of language and sentiment analysis, the polarity scores of each positive and negative word in the movie domain-specific polarity lexicon is calculated. And then the polarity scores to each comment of the plain text movie corpus are assigned. The proposed system achieves 89% and 85% accuracy on positive and negative opinion words respectively in the evaluation of polarity score lexicon. We also make the comment polarity for 3-class evaluation and 5-class evaluation based on the scores of comments. General TermsSentiment analysis

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“…We followed the setup proposed by (Al Sallab et al, in press 2017) by applying RAE to morphologically tokenized text which proved to improve the performance by reducing the lexical sparsity of the language. We also use a broader semantic representation of words by concatenating word embeddings trained using the skip-gram model (Mikolov et al, 2013) with sentiment embeddings trained using the ArSenL sentiment lexicon (Badaro et al, 2014).…”

Section: System 3: Recursive Auto Encodersmentioning

confidence: 99%

“…and achieved good performances after applying the EntropyWeighted Genetic Algorithm for feature reduction (Abbasi et al, 2008). Sentiment lexicons also provided an additional source of features that proved useful for the task (Abdul-Mageed et al, 2011;Badaro et al, 2014 A framework was developed for tweets written in Modern Standard Arabic (MSA) and containing Jordanian dialects, Arabizi (Arabic words written using Latin characters) and emoticons. This framework was realized by training different classifiers using features that capture the different linguistic phenomena (Duwairi et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model

Baly

Badaro

Hamdi

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textual objects to express sentiments. This paper describes the "OMAM" systems that we developed as part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on Arabic tweets for subtask A. As for the remaining subtasks, we introduce a topicbased approach that accounts for topic specificities by predicting topics or domains of upcoming tweets, and then using this information to predict their sentiment. Results indicate that applying the English state-of-the-art method to Arabic has achieved solid results without significant enhancements. Furthermore, the topic-based method ranked 1 st in subtasks C and E, and 2 nd in subtask D.

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A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining

Cited by 138 publications

References 14 publications

A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models

A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models

Assigning Polarity Scores to Facebook Myanmar Movie Comments

OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model

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