Deep Learning Models for Sentiment Analysis in Arabic

Sallab, Ahmad A. Al; Hajj, Hazem; Badaro, Gilbert; Baly, Ramy; El-Hajj, Wassim; Shaban, Khaled

doi:10.18653/v1/w15-3202

Cited by 100 publications

(80 citation statements)

References 9 publications

(8 reference statements)

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“…The same method is used to obtain the whole tweet's negative and objective scores. Other more complex methods can be used to find the tweet sentiment [(Hobeica et al, 2011), (Al Sallab et al, 2015, , (Baly et al, 2016), (Al Sallab et al, in press 2017)], but we resorted to this method for simplicity.…”

Section: Sentiment Extractionmentioning

confidence: 99%

CAT: Credibility Analysis of Arabic Content on Twitter

Ballouli¹,

El-Hajj²,

Ghandour³

et al. 2017

Proceedings of the Third Arabic Natural Language Processing Workshop

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Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and rumor detection among others, suffer considerably if the tweet is not credible, not real, or spam. In this paper, we perform an extensive analysis on credibility of Arabic content on Twitter. We also build a classification model (CAT) to automatically predict the credibility of a given Arabic tweet. Of particular originality is the inclusion of features extracted directly or indirectly from the author's profile and timeline. To train and test CAT, we annotated for credibility a data set of 9, 000 Arabic tweets that are topic independent. CAT achieved consistent improvements in predicting the credibility of the tweets when compared to several baselines and when compared to the state-of-the-art approach with an improvement of 21% in weighted average Fmeasure. We also conducted experiments to highlight the importance of the userbased features as opposed to the contentbased features. We conclude our work with a feature reduction experiment that highlights the best indicative features of credibility.

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Section: Sentiment Extractionmentioning

confidence: 99%

CAT: Credibility Analysis of Arabic Content on Twitter

Ballouli¹,

El-Hajj²,

Ghandour³

et al. 2017

Proceedings of the Third Arabic Natural Language Processing Workshop

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“…Hal tersebut dilakukan karena pada data pelatihan sudah terdapat label yang nantinya juga akan digunakan pada lapisan output jaringan. Meskipun demikian pre-training tetap dilakukan dengan mengatur tumpukan RBM yang digunakan dalam DBN menyesuaikan data yaitu dengan Bernoulli (Biner) RBM [5].…”

Section: Algoritma Pelatihan Pada Dbnunclassified

Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network

zulfa¹,

Winarko

2017

Indonesian J. Comput. Cybern. Syst.

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Abstrak Kata kunci-Sentimen Analisis, Twitter, deep belief Abstract Sentiment analysis is a computational research of opinion sentiment and emotion which is expressed in textual mode. Twitter becomes the most popular communication device among internet users. Deep Learning is a new area of machine learning research. It aims to move machine learning closer to its main goal, artificial intelligence. The purpose of deep learning is to change the manual of engineering with learning. At its growth, deep learning has algorithms arrangement that focus on non-linear data representation. One of the machine learning methods is Deep Belief Network (DBN). Deep Belief Network (DBN), which is included in Deep

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“…We trained the RAE deep learning model that achieved high performances in both English (Socher et al, 2011) and Arabic (Al Sallab et al, 2015). Briefly, the RAE model derive a sentence representation by combining word embeddings, two at a time, following the structure of a syntactic parse tree.…”

Section: System 3: Recursive Auto Encodersmentioning

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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Deep Learning Models for Sentiment Analysis in Arabic

Cited by 100 publications

References 9 publications

CAT: Credibility Analysis of Arabic Content on Twitter

CAT: Credibility Analysis of Arabic Content on Twitter

Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network

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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