Textual Analysis Applications: Subject Review

Al-Mashhsdany, Abeer K.; Ahmed, Safa A.

doi:10.37652/juaps.2022.171861

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2022

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“…Negative class gives advice that something is bad, so the decision will be leave it. While positive class gives advice that something is good, so the decision will be use it [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Healthcare assessment for beauty centers using hybrid sentiment analysis

Al-Mashhadany

Sadiq

Ali³

et al. 2022

IJEECS

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<span>Because of COVID-19, healthcare became the first interesting domain at the world. Here, comes the role of researchers to do what they can to guide people. Nowadays, the most wanted field is beauty industry. It achieved large market. And the estimation is toward the growing. Researchers can give advice to prevent unhealthy causes in this field. They can apply sentiment analysis methods to make decision whether a Beauty center is healthy or unhealthy. This work develops an improved method of sentiment analysis to classify the beauty centers in Iraq into healthy and unhealthy classes. Researchers used comments of beauty centers’ Facebooks to perform the assessment. The methodologies encompass the two approaches lexicon-based and machine-learning-based. Three machine learning mechanisms had been applied; rough set theory, naïve bayes, and k-nearest neighbors. It will be shown that rough set theory is the best compared with the others two. Rough set theory achieved 95.2%, while Naïve Bayes achieved 87.5% and k-nearest neighbors achieved 78%.</span>

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“…Negative class gives advice that something is bad, so the decision will be leave it. While positive class gives advice that something is good, so the decision will be use it [3]- [5].…”

Section: Introductionmentioning

confidence: 99%