Incorporating sentimental analysis into development of a hybrid classification model: A comprehensive study

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

doi:10.53730/ijhs.v6nS1.4924

Authors

Chamandeep Kaur
kaur.chaman83@gmail.com
Lecturer, Computer Science Department, Jazan University, Saudi Arabia
Mawahib Sharafeldin Adam Boush Assistant Professor, Computer Science Department, Jazan University, Saudi Arabia
Samar Mansoor Hassen Lecturer, Computer Science Department, Jazan University, Saudi Arabia
Wafaa Abushmlah Hakami Teaching Assistant, Computer Science Department, Jazan University, Saudi Arabia
Mohammed Hassan Osman Abdalraheem Assistant Professor, Computer Science Department, Jazan University, Saudi Arabia
Najla Mohammed Galam Teaching Assistant, Computer Science Department, Jazan University, Saudi Arabia
Nedaa Abdulaziz Hadi Teaching Assistant, Computer Science Department, Jazan University, Saudi Arabia
Nedaa Abdulaziz Hadi Teaching Assistant, Computer Science Department, Jazan University, Saudi Arabia
Atheer Omar S Benjeed Lecturer, Computer Science Department, Jazan University, Saudi Arabia

Keywords:

SVM, data analytics, hybrid classification, sentiment analysis, bidirectional long short-term memory, multi-feature fusion, convolution neural network

Abstract

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.

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