Word embeddings for Arabic sentiment analysis

Altowayan, A. Aziz; Tao, Lixin

doi:10.1109/bigdata.2016.7841054

Cited by 86 publications

(64 citation statements)

References 8 publications

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“…Preprocessing [17] Normalization, POS tagging [24][25][26][27] Stemming [28][29][30][31][32][33] Text cleaning [34][35][36][37][38][39] Normalization, stemming, stop words removal [40][41][42] Text cleaning, normalization, stemming, stop words removal [43][44][45] Normalization Text cleaning, normalization, tokenization, stemming, stop words removal [49][50][51][52] Normalization, tokenization [53,54] Text cleaning, normalization, tokenization [55,56] Normalization, tokenization, POS tagging [13,[57][58][59][60][61][62][63][64] Normalization, tokenization, stemming, stop words removal [65,66] Normalization, tokenization, stemming, lemmatization [67,68] Text cleaning, normalization, tokenization, stemming [69] Text cleaning, tokenization, stemming, negation detection [70]…”

Section: Referencementioning

confidence: 99%

“…Word embedding is an alternative approach for such hand-crafted features in ASA. Several recent studies have exploited this technique [14,33,91,134].…”

Section: Machine Learning Approachmentioning

confidence: 99%

“…It is noticed that using the word embedding with DL models helped improving the results over the linear models, such as SVM, as it is suitable for large datasets and can be computationally efficient [33,90].…”

Section: Machine Learning Approachmentioning

confidence: 99%

See 2 more Smart Citations

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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Section: Referencementioning

confidence: 99%

“…Word embedding is an alternative approach for such hand-crafted features in ASA. Several recent studies have exploited this technique [14,33,91,134].…”

Section: Machine Learning Approachmentioning

confidence: 99%

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Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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“…This model has been successfully applied to a number of NLP tasks such as sentiment analysis, translation and classification (Mikolov et al, 2013). It uses simple neural network (NN) for training and it is considered as prediction based model that can capture linguistic features such as semantic feature (Mikolov et al, 2013;Altowayan and Tao, 2016). There are different parameters that were used for learning NN including the window of the context, the size of the features and negative sample.…”

Section: Word2vec Translation Modelmentioning

confidence: 99%

“…It can be implemented using neural networks with the aim of representing words as vectors based on semantic features. Word embedding was used in numerous NLP tasks, such as classification (Rahmawati and Khodra, 2016), language model (Bengio et al, 2003) and sentiment analysis (Altowayan and Tao, 2016). There are many types of word representation methods including Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), Word2vec and Glove.…”

Section: Introductionmentioning

confidence: 99%

Translating Dialectal Arabic as Low Resource Language using Word Embedding

Almansor¹,

Al-Ani²

2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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A number of machine translation methods have been proposed in recent years to deal with the increasingly important problem of automatic translation between texts of different languages or languages and their dialects. These methods have produced promising results when applied to some of the widely studied languages. Existing translation methods are mainly implemented using rule-based and static machine translation approaches. Rule based approaches utilize language translation rules that can either be constructed by an expert, which is quite difficult when dealing with dialects, or rely on rule construction algorithms, which require very large parallel datasets. Statistical approaches also require large parallel datasets to build the translation models. However, large parallel datasets do not exist for languages with low resources, such as the Arabic language and its dialects. In this paper we propose an algorithm that attempts to overcome this limitation, and apply it to translate the Egyptian dialect (EGY) to Modern Standard Arabic (MSA). Monolingual corpus was collected for both MSA and EGY and a relatively small parallel language pair set was built to train the models. The proposed method utilizes Word embeddings as it requires monolingual data rather than parallel corpus. Both Continuous Bag of Words and Skip-gram were used to build word vectors. The proposed method was validated on four different datasets using a four-fold cross validation approach.

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A Comparison of fastText Implementations Using Arabic Text Classification

Alghamdi

Assiri

2019

Advances in Intelligent Systems and Computing

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Word embeddings for Arabic sentiment analysis

Cited by 86 publications

References 8 publications

Arabic Sentiment Analysis: A Systematic Literature Review

Arabic Sentiment Analysis: A Systematic Literature Review

Translating Dialectal Arabic as Low Resource Language using Word Embedding

A Comparison of fastText Implementations Using Arabic Text Classification

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