Open AccessIncorporating sentimental analysis into development of a hybrid classification model
Author(s) -
Chamandeep Kaur,
Mawahib Sharafeldin Adam Boush,
Samar Mansoor Hassen,
Wafaa Abushmlah Hakami,
Mohammed Hassan Osman Abdalraheem,
Najla Mohammed Galam,
Nedaa Abdulaziz Hadi,
Nedaa Abdulaziz Hadi,
Atheer Omar S Benjeed
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns1.4924
Subject(s) - sentiment analysis , computer science , recall , support vector machine , random forest , precision and recall , artificial intelligence , machine learning , classifier (uml) , subjectivity , logistic regression , data mining , natural language processing , data science , psychology , cognitive psychology , philosophy , epistemology
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.