Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments

Medhaffar, Salima; Bougares, Fethi; Estève, Yannick; Hadrich-Belguith, Lamia

doi:10.18653/v1/w17-1307

Cited by 119 publications

(75 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3952 entries for SentiAlg and 2375 for SOCALAlg. Medhaffar et al (2017) presented the TSAC (Tunisian Sentiment Analysis Corpus) corpus containing 17,060 Tunisian Facebook comments, manually annotated (8215 positive and 8845 negative). Guellil et al (2018b) presented the automatic construction of an Algerian sentiment corpus using the constructed lexicon (Guellil et al, 2017d) Afterwards, they randomly selected 8000 messages (where 4000 are positives and 4000 are negatives) Al-Twairesh et al (2017), introduced the corpus AraSenTi-Tweet containing 17,573 Saudi tweets and semi-automatically annotated into four classes: positive, negative, neutral and mixed.…”

Section: Building Resourcesmentioning

confidence: 99%

“…However, Guellil et al (2018b) clearly stated that the low F1-score that they obtained for the Arabizi dataset (up to 0.66) is principally related to the fact of handling Arabizi without transliteration. In addition to Arabizi, the two works of Medhaffar et al (2017) and Guellil et al (2018b) also concentrated on Arabic, they are presented in detail in Section 4.3.…”

Section: Semantic-level Analysismentioning

confidence: 99%

See 1 more Smart Citation

Arabic natural language processing: An overview

Guellil

Saadane²,

Azouaou

et al. 2021

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

a b s t r a c tArabic is recognised as the 4th most used language of the Internet. Arabic has three main varieties: (1) classical Arabic (CA), (2) Modern Standard Arabic (MSA), (3) Arabic Dialect (AD). MSA and AD could be written either in Arabic or in Roman script (Arabizi), which corresponds to Arabic written with Latin letters, numerals and punctuation. Due to the complexity of this language and the number of corresponding challenges for NLP, many surveys have been conducted, in order to synthesise the work done on Arabic. However these surveys principally focus on two varieties of Arabic (MSA and AD, written in Arabic letters only), they are slightly old (no such survey since 2015) and therefore do not cover recent resources and tools. To bridge the gap, we propose a survey focusing on 90 recent research papers (74% of which were published after 2015). Our study presents and classifies the work done on the three varieties of Arabic, by concentrating on both Arabic and Arabizi, and associates each work to its publicly available resources whenever available.

show abstract

Section: Building Resourcesmentioning

confidence: 99%

Section: Semantic-level Analysismentioning

confidence: 99%

Arabic natural language processing: An overview

Guellil

Saadane²,

Azouaou

et al. 2021

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

show abstract

“…Previous work on Tunisian Sentiment Analysis (SA) has mostly processed the textual data using initial cleaning and normalization procedures [18,11,8].…”

Section: Introductionmentioning

confidence: 99%

“…To do that, three different-sized Tunisian datasets containing positive/negative tweets and comments from multiple domains provided by [18,11,8] were used. The data has been subjected to several combinations of preprocessing techniques with NEs tagging integrated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Mulki¹,

Haddad²,

Ali³

et al. 2018

CyS

View full text Add to dashboard Cite

Social media platforms have been witnessing a significant increase in posts written in the Tunisian dialect since the uprising in Tunisia at the end of 2010. Most of the posted tweets or comments reflect the impressions of the Tunisian public towards social, economical and political major events. These opinions have been tracked, analyzed and evaluated through sentiment analysis systems. In the current study, we investigate the impact of several preprocessing techniques on sentiment analysis using two sentiment classification models: Supervised and lexicon-based. These models were trained on three Tunisian datasets of different sizes and multiple domains. Our results emphasize the positive impact of preprocessing phase on the evaluation measures of both sentiment classifiers as the baseline was significantly outperformed when stemming, emoji recognition and negation detection tasks were applied. Moreover, integrating named entities with these tasks enhanced the lexicon-based classification performance in all datasets and that of the supervised model in medium and small sized datasets.

show abstract

SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis

Guellil

Adeel

Azouaou

et al. 2018

Advances in Brain Inspired Cognitive Systems

View full text Add to dashboard Cite

Data annotation is an important but time-consuming and costly procedure. To sort a text into two classes, the very first thing we need is a good annotation guideline, establishing what is required to qualify for each class. In the literature, the difficulties associated with an appropriate data annotation has been underestimated. In this paper, we present a novel approach to automatically construct an annotated sentiment corpus for Algerian dialect (A Maghrebi Arabic dialect). The construction of this corpus is based on an Algerian sentiment lexicon that is also constructed automatically. The presented work deals with the two widely used scripts on Arabic social media: Arabic and Arabizi. The proposed approach automatically constructs a sentiment corpus containing 8000 messages (where 4000 are dedicated to Arabic and 4000 to Arabizi). The achieved F1-score is up to 72% and 78% for an Arabic and Arabizi test sets, respectively. Ongoing work is aimed at integrating transliteration process for Arabizi messages to further improve the obtained results.

show abstract

Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments

Cited by 119 publications

References 13 publications

Arabic natural language processing: An overview

Arabic natural language processing: An overview

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

SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis

Contact Info

Product

Resources

About