Sentiment Analysis of Algerian Dialect Using a Deep Learning Approach

Klouche, Badia; Benslimane, Sidi Mohamed; Mahammed, Nadir

doi:10.1007/978-3-030-96311-8_12

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The development of a domain-specific sentiment lexicon or building context-specific corpora can solve this issue (Birjali et al , 2021). For instance, Klouche et al (2022) investigated how DL of the AlgDial may enhance SA. For this, they extracted and labeled tweets related to a specific mobile network operator (MNO) to investigate users' opinions.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Sentiment analysis of the Algerian social movement inception

Laifa

Mohdeb²

2023

DTA

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PurposeThis study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.Design/methodology/approachRelated tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.FindingsThe findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.Originality/valueAt the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Sentiment analysis of the Algerian social movement inception

Laifa

Mohdeb²

2023

DTA

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“…Klouche et al [9] have proposed a new approach to analyze the Algerian dialect sentiments for the benefit of the Algerian Telephone Operator Ooredoo based on CNN and SVM classifier from the machine learning approach that has used to classify the polarity of the data. Experimental results have revealed that deep learning approaches perform better than sentiment traditional methods.…”

Section: Related Workmentioning

confidence: 99%

DZ-OPINION: Algerian Dialect Opinion Analysis Model with Deep Learning Techniques

Bousmaha¹,

Hamadouche²,

Gourara³

et al. 2022

RIA

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Today, many sources of unstructured information such as social networks and blogs are more or less freely available on the web, and their volume is constantly growing, which constitutes a free gold mine for collecting public opinion. Opinion plays a crucial role because it can influence the decision-making process. Sentiment or opinion analysis is a discipline that can be used to meet decision-making needs, provide feedback to new product launches and marketing campaigns, and protect a company’s reputation, especially in social networking environments with massive data by exploiting textual data generated by users. In contrast to the techniques used, we have used and adapted in this study deep learning techniques: CNN and LSTM to identify their potential in this area and apply it to a corpus of Arabic data and in particular in Algerian dialect collected from social networks (50572 Facebook comments). We obtained promising results with an 85% f-measure. This represents a good start for an opinion analysis on the Algerian Dialect.

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“…Itreachedanaccuracyof73.67%forthe3-classclassificationproblemand83.73%forthe2-class classificationproblem.Thesemiautomaticlearningcomponenthasbeenshowntobeeffectivebecause theaccuracyimprovementis17.55%. (Klouche et al, 2019) presented a general architecture of a sentiment analyzer based on the feedbackofAlgerianTelephoneoperatorcustomers,writteninModernStandardArabic,Arabizi andAlgeriandialect.However,theapproachisinapreliminarystateandnoimplementationhas beencarriedout.…”

Section: Sentiment Analysis For Arabic Languagementioning

confidence: 99%

Ooredoo Rayek

Klouche

Benslimane

Bennabi

2020

International Journal of Technology Diffusion

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Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.

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Sentiment Analysis of Algerian Dialect Using a Deep Learning Approach

Cited by 5 publications

References 13 publications

Sentiment analysis of the Algerian social movement inception

Sentiment analysis of the Algerian social movement inception

DZ-OPINION: Algerian Dialect Opinion Analysis Model with Deep Learning Techniques

Ooredoo Rayek

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