Tunisian Dialect Sentiment Analysis: A Natural Language Processing-based Approach

Mulki, Hala; Haddad, Hatem; Ali, Chedi Bechikh; Babaoğlu, İsmail

doi:10.13053/cys-22-4-3009

Cited by 20 publications

(4 citation statements)

References 11 publications

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“…In [16], authors have proposed to investigate the impact of pre-processing techniques on SA of Tunisian dialect. They have compared the performance of two SA models, a lexicon-based model and a supervised ML-based model (SVM and NB).…”

Section: Lexicon-based Approachmentioning

confidence: 99%

Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Tunisian Dialectical Facebook Content During the Spread of the Coronavirus Pandemic

Jaballi,

Hazar,

Zrigui

et al. 2023

Communications in Computer and Information Science

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Copyright

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Section: Lexicon-based Approachmentioning

confidence: 99%

Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Tunisian Dialectical Facebook Content During the Spread of the Coronavirus Pandemic

Jaballi,

Hazar,

Zrigui

et al. 2023

Communications in Computer and Information Science

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“…In [ 16 ], authors conducted a study on the impact on the Tunisian sentiment classification performance when it is combined with other Arabic based preprocessing tasks (Named Entities tagging, stopwords removal, common emoji recognition, etc.). A lexicon-based approach and the support vector machine model were used to evaluate the performances on the above-mentioned datasets (TEC and TSAC datasets).…”

Section: Tunisian Sentiment Analysismentioning

confidence: 99%

Learning Word Representations for Tunisian Sentiment Analysis

Messaoudi¹,

Haddad²,

HajHmida³

et al. 2021

Pattern Recognition and Artificial Intelligence

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Tunisians on social media tend to express themselves in their local dialect using Latin script (TUNIZI). This raises an additional challenge to the process of exploring and recognizing online opinions. To date, very little work has addressed TUNIZI sentiment analysis due to scarce resources for training an automated system. In this paper, we focus on the Tunisian dialect sentiment analysis used on social media. Most of the previous work used machine learning techniques combined with handcrafted features. More recently, Deep Neural Networks were widely used for this task, especially for the English language. In this paper, we explore the importance of various unsupervised word representations (word2vec, BERT) and we investigate the use of Convolutional Neural Networks and Bidirectional Long Short-Term Memory. Without using any kind of handcrafted features, our experimental results on two publicly available datasets [ 18 , 19 ] showed comparable performances to other languages.

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“…It is also able to treat all non-Arabic words used in Arabic dialects. (Mulki et al, 2018) tested the impact of pre-processing techniques on sentiment analysis using three Tunisian datasets of different sizes and multiple domains. The results emphasize the positive impact of pre-processing phase in stemming, emoji recognition and negation detection tasks.…”

Section: Related Workmentioning

confidence: 99%

Preprocessing Arabic Dialect for Sentiment Mining: State of Art

Nassr

Sael

Benabbou

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Sentiment Analysis concerns the analysis of ideas, emotions, evaluations, values, attitudes and feelings about products, services, companies, individuals, tasks, events, titles and their characteristics. With the increase in applications on the Internet and social networks, Sentiment Analysis has become more crucial in the field of text mining research and has since been used to explore users’ opinions on various products or topics discussed on the Internet. Developments in the fields of Natural Language Processing and Computational Linguistics have contributed positively to Sentiment Analysis studies, especially for sentiments written in non-structured or semi-structured languages. In this paper, we present a literature review on the pre-processing task on the field of sentiment analysis and an analytical and comparative study of different researches conducted in Arabic social networks. This study allowed as concluding that several works have dealt with the generation of stop words dictionary. In this context, two approaches are adopted: first, the manual one, which gives rise to a limited list, and second, the automatic, where the list of stop words is extracted from social networks based on defined rules. For stemming two, algorithms have been proposed to isolate prefixes and suffixes from words in dialects. However, few works have been interested in dialects directly without translation. The Moroccan dialect in particular is considered as the 5th dialect studied among Arabic dialects after Jordanian, Egyptian, Tunisian and Algerian dialects. Despite the significant lack in studies carried out on Arabic dialects, we were able to extract several conclusions about the difficulties and challenges encountered through this comparative study, as well as the possible ways and tracks to study in any dialects sentiment analysis pre-processing solution.

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Tunisian Dialect Sentiment Analysis: A Natural Language Processing-based Approach

Cited by 20 publications

References 11 publications

Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Tunisian Dialectical Facebook Content During the Spread of the Coronavirus Pandemic

Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Tunisian Dialectical Facebook Content During the Spread of the Coronavirus Pandemic

Learning Word Representations for Tunisian Sentiment Analysis

Preprocessing Arabic Dialect for Sentiment Mining: State of Art

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