Opinion and Sentiment Polarity Detection Using Supervised Machine Learning

Touahri, Ibtissam; Mazroui, Azzeddine

doi:10.1109/cist.2018.8596563

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…The baptized ArSAS [17], SemEval 2017 [18] and ASTD [19] corpora contain tweets annotated as positive, negative or neutral. The sentimental lexicons contain sentimental terms that are verified manually, or obtained automatically by machine translation [20]. Sentiwordnet is a lexical resource that assigns numerical scores to each wordnet synset based on objectivity, positivity, and negativity [21].…”

Section: Previous Workmentioning

confidence: 99%

The construction of an accurate Arabic sentiment analysis system based on resources alteration and approaches comparison

Touahri

2022

ACI

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PurposeThis paper purposed a multi-facet sentiment analysis system.Design/methodology/approachHence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.FindingsThe proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.Originality/valueThe construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

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Section: Previous Workmentioning

confidence: 99%

The construction of an accurate Arabic sentiment analysis system based on resources alteration and approaches comparison

Touahri

2022

ACI

Self Cite

View full text Add to dashboard Cite

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Survey of machine learning techniques for Arabic fake news detection

Touahri,

Mazroui

2024

Artif Intell Rev

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Social media platforms have emerged as primary information sources, offering easy access to a wide audience. Consequently, a significant portion of the global population relies on these platforms for updates on current events. However, fraudulent actors exploit social networks to disseminate false information, either for financial gain or to manipulate public opinion. Recognizing the detrimental impact of fake news, researchers have turned their attention to automating its detection. In this paper, we provide a thorough review of fake news detection in Arabic, a low-resource language, to contextualize the current state of research in this domain. In our research methodology, we recall fake news terminology, provide examples for clarity, particularly in Arabic contexts, and explore its impact on public opinion. We discuss the challenges in fake news detection, outline the used datasets, and provide Arabic annotation samples for label assignment. Likewise, preprocessing steps for Arabic language nuances are highlighted. We also explore features from shared tasks and their implications. Lastly, we address open issues, proposing some future research directions like dataset improvement, feature refinement, and increased awareness to combat fake news proliferation. We contend that incorporating our perspective into the examination of fake news aspects, along with suggesting enhancements, sets this survey apart from others currently available.

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