Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Assiri, Adel; Emam, Ahmed; Al-Dossari, Hmood

doi:10.1177/0165551516688143

Cited by 48 publications

(33 citation statements)

References 31 publications

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“…Dictionaries and Lexicons. This feature usually employed in unsupervised machine learning scenarios [22]. Wiegand et al [23] addressed the detection of profane words by taking advantage of the corpora and lexical resources.…”

Section: Features Representation For Hate Speech Detectionmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

Self Cite

119

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In social media platforms, hate speech can be a reason of "cyber conflict" which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented.

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Section: Features Representation For Hate Speech Detectionmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

Self Cite

119

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“…AraSenTi [3] developed an Arabic sentiment lexicons based on tweets called AraSenTi-Trans and AraSenTi-PMI. Assiri [7] built a large lexicon that contains 14,000 sentiment terms based on a pre-created lexicon developed by Badaro et al [9] and encoded using the Buckwalter translation. It is to be noted that most of the aforementioned approaches focused on other dialects and ignored Saudi dialect.…”

Section: Sentiment Lexicon Constructionmentioning

confidence: 99%

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

Alqarafi

Adeel

Hawalah

et al. 2018

Lecture Notes in Computer Science

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In the literature, limited work has been conducted to develop sentiment resources for Saudi dialect. The lack of resources such as dialectical lexicons and corpora are some of the major bottlenecks to the successful development of Arabic sentiment analysis models. In this paper, a semi-supervised approach is presented to construct an annotated sentiment corpus for Saudi dialect using Twitter. The presented approach is primarily based on a list of lexicons built by using word embedding techniques such as word2vec. A huge corpus extracted from twitter is annotated and manually reviewed to exclude incorrect annotated tweets which is publicly available. For corpus validation, state-of-the-art classification algorithms (such as Logistic Regression, Support Vector Machine, and Naive Bayes) are applied and evaluated. Simulation results demonstrate that the Naive Bayes algorithm outperformed all other approaches and achieved accuracy up to 91%.

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“…Assiri et al [59] developed a Saudi dialect lexicon after integrating the various corpus-based and dictionary-based techniques. They expanded a list of Saudi seed words by applying the method described earlier [60], wherein they added the terms from the pre-developed lexicon [61] to a Saudi lexicon after they carried out a normalisation and cleaning process.…”

Section: A the Approaches For Dialect Arabic (Da)mentioning

confidence: 99%

A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

Abo¹,

Raj²,

Qazi³

2019

IEEE Access

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Due to the significant use of Arabic language in social media networks, the demand for Arabic sentiment analysis has increased rapidly. Although, numerous sentiment analysis techniques enable people to obtain valuable insights from the opinions shared on social media. However, these techniques are still in their infancy, and the Arabic sentiment analysis domain lacks a compressive survey. Therefore, this study focused on the various characteristics, State-of-the-Art, and the level of sentiment analysis along with the natural language processing applied in the Arabic sentiment analysis. Furthermore, this study also discussed the sentiment analysis of the modern standards and the dialects of Arabic languages along with various machine learning processes and a few popular algorithms. Moreover, this study adds values by critical analysis of two case studies, which displayed an extensive set of the various research communities in this field of sentiment analysis. Finally, open research challenges are investigated, with a focus on the shortage of lexicons; availability; use of Dialect Arabic (DA); lack of corpora and datasets; right to left reading and compound phrases and idioms.

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Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Cited by 48 publications

References 31 publications

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

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