Sentiment Analysis of Arabic Jordanian Dialect Tweets

Atoum, Jalal Omer; Nouman, Mais

doi:10.14569/ijacsa.2019.0100234

Cited by 26 publications

(10 citation statements)

References 13 publications

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“…The corpus contains 5400 tweets. Another dialect sentiment corpus was proposed in [31] for Jordanian dialect tweets. The corpus was manually annotated into three classes-positive, negative, and neutral-by Arab Jordanian students.…”

Section: Sentiment Annotationmentioning

confidence: 99%

AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

et al. 2021

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At a time when research in the field of sentiment analysis tends to study advanced topics in languages, such as English, other languages such as Arabic still suffer from basic problems and challenges, most notably the availability of large corpora. Furthermore, manual annotation is time-consuming and difficult when the corpus is too large. This paper presents a semi-supervised self-learning technique, to extend an Arabic sentiment annotated corpus with unlabeled data, named AraSenCorpus. We use a neural network to train a set of models on a manually labeled dataset containing 15,000 tweets. We used these models to extend the corpus to a large Arabic sentiment corpus called “AraSenCorpus”. AraSenCorpus contains 4.5 million tweets and covers both modern standard Arabic and some of the Arabic dialects. The long-short term memory (LSTM) deep learning classifier is used to train and test the final corpus. We evaluate our proposed framework on two external benchmark datasets to ensure the improvement of the Arabic sentiment classification. The experimental results show that our corpus outperforms the existing state-of-the-art systems.

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Section: Sentiment Annotationmentioning

confidence: 99%

AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

et al. 2021

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“…The most common approaches are Machine Learning algorithms (classifiers), Deep Learning models, a Lexicon-Based approach, and a hybrid approach that combined both machine learning algorithms and a Lexicon-based approach. In [12][13][14][15][16][17][18][19][20][21][22][23][24] authors used machine learning algorithms (classifiers) approach for Sentiment Analysis of Arabic Dialects, and the most common used algorithm is SVM followed by NB. In [10,[25][26][27], authors utilized lexicon-based approach.…”

Section: Sentiment Analysis Of Arabic Dialectsmentioning

confidence: 99%

“…Figure 4 presented the same finding. Twitter attracted researchers due to many reasons such as the tweets are formed in short sentences, Twitter API that makes it easy to export Web-based tool (lexicon-based approaches or machine Learning approaches) [31] S4 Deep learning and ensemble implementations [10] S5 Hybrid lexicon approach (unsupervised and supervised technique) [32] S6 Deep learning [44] S7 Hybrid approach (machine learning and semantic orientation) [13] S8 SVM classifier and NB classifier [33] S9 Deep learning (CNN and LSTM) [45] S10 Aspect-based sentiment analysis [14] S11 Machine learning algorithms [27] S12 Hybrid model (corpus-based and lexicon-based models) [15] S13 Machine learning algorithms [38] S14 Machine learning algorithms and deep learning (CNN) [39] S15 Machine learning algorithms and deep learning (SVM and RNN) [16] S16 Machine learning algorithms (SVM, MNB, SGD, KNN, LR, PA) [34] S17 Deep learning (CNN) [17] S18 Machine learning algorithms (SVM, NB, DT, KNN) [28] S19 Hybrid approach (lexicon-based and machine learning) [18] S20 Machine learning algorithms [29] S21 Hybrid approach (lexicon-based and machine learning (SVM)) [46] S22 Machine learning algorithms (BNB, MNB, NSVC, LSVC, SGD, RGD, LR) [47] S23 Machine learning algorithms (SVM, NB, KNN, LR, MLP) [35] S24 Deep learning (CNN and LSTM) [36] S25 Deep learning (narrow CNN) [19] S26 Machine learning algorithms (SVM, NB, BNB, MNB, SGD, LR) [30] S27 Hybrid approach (lexicon-based and machine learning (SVM and NB)) [20] S28 Machine learning algorithms (SVM and NB) [26] S29 Hybrid model (corpus-based and lexicon-based models) [37] S30 Deep learning (CNN and LSTM) [21] S31 Machine learning algorithms (SVM, NB, and KNN) [22] S32 Machine learning algorithms (SVM, BNB, MLP) [48] S33 Shallow neural network (syntax-ignorant n-grams embeddi...…”

Section: Rq2mentioning

confidence: 99%

A Systematic Review for Sentiment Analysis of Arabic Dialect Texts Researches

Shamsi

Abdallah

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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People in the Arab world prefer to use their dialects in writing while using social media sites which results in generating a significant amount of texts. Sentiment analysis in texts is one of the most important applications of the Natural Language Processing field of science. Sentiment Analysis involves classifying texts based upon emotions or polarity. Different approaches have been utilized so far for Sentiment Analysis of Arabic Texts. In this systematic review, the author aims to explore the researches and studies that were in the field of Sentiment Analysis of Arabic Dialects to report the utilized approaches, constructed datasets, most common type of dialects explored by researchers, and reported results of Sentiment Analysis in term of accuracy, recall, precision, and F-Measure. Author reported the following findings for this systematic review: (1) in most of the included research papers the dialect type is not specified and mostly it was mentioned (Arabic Dialects) in general, however, some other dialects were explored specifically by authors such as Saudi Dialect (2) 50% of the utilized datasets in the included research papers are constructed by the authors themselves, moreover, the size of most of the utilized datasets are between 10,000 and 50,000, and very limit number of datasets had size above 100,000, (3) machine learning algorithms (classifiers) are the most common approach that were used for Sentiment Analysis of Arabic Dialects (4) the best result for Sentiment Analysis of Arabic Dialects achieved while different machine learning algorithms (classifiers) were used, and (5) among social media platforms, Twitter is the most common utilized online platform for constructing datasets for Arabic Dialects texts.

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“…In the same context and sharing the same objective, that of enriching the resources available for sentiment analysis applications in dialectal Arabic, other works have presented datasets in different dialects, including, but not limited to, the following: In Saudi dialect, the Arasenti-tweet [27], a dataset retrieved on Twitter and manually annotated in four classes (positive, negative, neutral and mixed), other datasets have been reported in [28] [29]. With regard to the Jordanian dialect, different datasets gathered on Facebook as well as Twitter, were introduced in [30][31] [32]. Regarding the Levantine dialect, the works [33][34] yields valuable datasets.…”

Section: B Vernacular Arabic Datasetsmentioning

confidence: 99%

MSTD: Moroccan Sentiment Twitter Dataset

Mihi¹,

Ait²,

El³

et al. 2020

IJACSA

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With the proliferation of social media and Internet accessibility, a massive amount of data has been produced. In most cases, the textual data available through the web comes mainly from people expressing their views in informal words. The Arabic language is one of the hardest Semitic languages to deal with because of its complex morphology. In this paper, a new contribution to the Arabic resources is presented as a large Moroccan dataset retrieved from Twitter and carefully annotated by native speakers. For the best of our knowledge, this dataset is the largest Moroccan dataset for sentiment analysis. It is distinguished by its size, its quality given by the commitment of annotators, and its accessibility for the research community. Furthermore, the MSTD (Moroccan Sentiment Twitter Dataset) is benchmarked through experiments carried out for 4-way classification as well as polarity classification (positive, negative). Various machine-learning algorithms are combined to feature extraction techniques to reach optimal settings. This work also presents the effect of stemming and lemmatization on the improvement of the obtained accuracies.

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Sentiment Analysis of Arabic Jordanian Dialect Tweets

Cited by 26 publications

References 13 publications

AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

A Systematic Review for Sentiment Analysis of Arabic Dialect Texts Researches

MSTD: Moroccan Sentiment Twitter Dataset

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