Incorporating sentimental analysis into development of a hybrid classification model

A comprehensive study

Bibliographic Data

ID15799381
AuthorsChamandeep Kaur (0000-0002-9520-3411, Jazan University, corresponding author), Mawahib Sharafeldin Adam Boush (Jazan University, corresponding author), Samar Mansoor Hassen (Jazan University, corresponding author), Wafaa Abushmlah Hakami (Jazan University, corresponding author), Mohammed Abdalraheem (0000-0002-0013-0505, Jazan University, corresponding author), Mohammed Hassan Osman Abdalraheem, Najla Mohammed Galam (Jazan University, corresponding author), Nedaa Abdulaziz Hadi (Jazan University, corresponding author), Atheer Omar S Benjeed (Jazan University, corresponding author)
Year2022
Volume6
IssueS1
Pages1709-1720
Publication date2022-03-21
Open AccessYes
TypeARTICLE
VenueInternational Journal of Health Sciences (JOURNAL)
Journal identifiersISSN: 2550-696X • E-ISSN: 2550-6978
PublisherUniversidad Tecnica de Manabi (PUBLISHER • EC)
DOI10.53730/ijhs.v6ns1.4924
OpenAlexW4220694803
LanguageEN
Citations received5

The Sentimental Analysis approach is typically used for analyzing a user's ideas, sentiments, and text subjectivity, all of which are expressed through text. Sentimental analysis, also known as "opinion mining," is a type of data mining that follows the concept of emotional analysis presented by people in a thoughtful manner. Based on historical evidence, websites are the most effective venue for soliciting customer feedback. Existing methodologies based on sentimental analysis are ineffective. As a result, a novel hybrid framework based on three classifiers, including SVM, logistic regression, and random forest, is proposed in this paper. Based on user feedback or historical data, the hybrid model serves as an effective classifier, assisting in the development of more accurate classification results. Furthermore, the proposed model has worked well and has been compared to other methods based on several performance metrics, such as accuracy, precision, recall, and recall

Classifier (UML · Cognitive psychology · Data mining · Data science · Logistic regression · Machine learning · Natural language processing · Precision and recall · Random forest · Recall · Sentiment analysis · Subjectivity · Support vector machine · Computer Science · Psychology · Sentiment Analysis and Opinion Mining · Spam and Phishing Detection · Text and Document Classification Technologies · Artificial Intelligence

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