A Proposed Lexicon-Based Sentiment Analysis Approach for the Vernacular Algerian Arabic

Mataoui, M'hamed; Zelmati, Omar; Boumechache, Madiha

doi:10.13053/rcs-110-1-5

Cited by 63 publications

(29 citation statements)

References 16 publications

(23 reference statements)

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“…Only few lexicons are constructed manually [30,31]. The work in [30] described the process of creating SIFAAT, a manually created lexicon of 3325 Arabic adjectives labelled with one of the following tags, positive (Pos), negative (Neg), neutral (Obj).…”

Section: Manual Constructionmentioning

confidence: 99%

“…The adjectives in SIFAAT pertained to the newswire domain and were extracted from the first four parts of the Penn Arabic Treebank [32]. In [31], the authors focused on Algerian dialect by constructing three lexicons: (1) However, Almost all the corpora were constructed manually [11, 20-22, 33, 34]. In the majority of cases, the annotation is done by natives annotators.…”

Section: Manual Constructionmentioning

confidence: 99%

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A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

et al. 2021

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In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach consists of constructing and applying a set of deep learning algorithms to classify the sentiment of Arabic messages as positive or negative. It was applied on Facebook messages written in Modern Standard Arabic (MSA) as well as in Algerian dialect (DALG, which is a low resourced-dialect, spoken by more than 40 million people) with both scripts Arabic and Arabizi. To handle Arabizi, we consider both options: transliteration (largely used in the research literature for handling Arabizi) and translation (never used in the research literature for handling Arabizi). For highlighting the effectiveness of a semi-supervised approach, we carried out different experiments using both corpora for the training (i.e. the corpus constructed automatically and the one that was reviewed manually). The experiments were done on many test corpora dedicated to MSA/DALG, which were proposed and evaluated in the research literature. Both classifiers are used, shallow and deep learning classifiers such as Random Forest (RF), Logistic Regression(LR) Convolutional Neural Network (CNN) and Long short-term memory (LSTM). These classifiers are combined with word embedding models such as Word2vec and fastText that were used for sentiment classification. Experimental results (F1 score up to 95% for intrinsic experiments and up to 89% for extrinsic experiments) showed that the proposed system outperforms the existing state-of-the-art methodologies (the best improvement is up to 25%).

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Section: Manual Constructionmentioning

confidence: 99%

Section: Manual Constructionmentioning

confidence: 99%

A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

et al. 2021

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“…They concluded that Naïve Bayes outperformed the other two classifiers in sentiment classification. Mataoui et al (2016) proposed a lexicon-based approach for Algerian Arabic sentiments. They classified the public sentiments shared in the Algerian Arabic language from Facebook.…”

Section: Informalmentioning

confidence: 99%

Socio monitoring framework (SMF): Efficient sentiment analysis through informal and native terms

al.¹

2020

Int. j. adv. appl. sci.

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Prediction and analysis of public expression is the trending topic of the current research arena. Opinion mining (a.k.a. Sentiment Analysis) is the automated orientation of public sentiments, views, suggestions, and opinions. It assists in estimating the popularity of products, events, services, and even political policies via user-generated content. Machine learning based supervised, semi-supervised, and unsupervised lexicon oriented techniques are applicable in the semantic orientation of public opinions about numerous real world entities. It is observed that socio channels contain real-time contents, which sometimes face the intricacy of informality, Slangs, Vernacular (Native terms), and sarcasm; however, these indicators provide high visibility of sentiments and opinions in terms of orientation. Unfortunately, the unclear perceptiveness of such contents lack in optimized orientation, and supervised machine learning systems are inappropriate where the Lexicon based opinion mining methods are preferred over learning based ones when training data is not adequate. In this paper, we seek to improve the performance of lexicon-based sentiment analysis by incorporating novel linguistic features such as vernaculars, slangs, and sarcasm for monitoring the social media contents up to a more realistic level. The core contributions are sarcasm detection and identification of vernacular terms. The performance of the proposed unsupervised lexicon-based framework over native, informal, and sarcastic opinion bearing terms is assessed via numerous experiments. For this, we utilized tweets relevant to two key domains, including Product and Politics. Experimental outcomes revealed that the proposed system outperformed the existing supervised and semi-supervised systems as 84.24%, and 82.35% of accuracies are achieved over informal and sarcastic contents for product and politics domains, respectively. The average accuracy for both domains is 83.29%.

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“…To the best of our knowledge, (Duwairi et al, 2016;Mataoui et al, 2016;GUELLIL et al, 2018) are the only works that looked into sentiment analysis for Arabizi. All three papers proposed to transliterate Arabizi to Arabic.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(Duwairi et al, 2016) transliterated Jordanian dialect Arabizi to Arabic using their own transliterator without evaluating the transliterations. (Mataoui et al, 2016) transliterated Algerian dialect Arabizi as part of their sentiment analysis for Algerian Arabic pipeline. They used Google Translate without evaluating the transliterations as well.…”

Section: Literature Reviewmentioning

confidence: 99%

SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)

Tobaili¹,

Fernández²,

Alani³

et al. 2019

Proceedings - Natural Language Processing in a Deep Learning World

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Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi.

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A Proposed Lexicon-Based Sentiment Analysis Approach for the Vernacular Algerian Arabic

Cited by 63 publications

References 16 publications

A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

Socio monitoring framework (SMF): Efficient sentiment analysis through informal and native terms

SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)

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