Class Wise Linear Discriminant and Regression Based Binarized Nearest Learning in Digital Marketing

The employment of internet and social media has remodeled behavioral aspects of consumer or student communities and methods in which organizations or educational institutions perform their business pattern. Both social and digital marketing put forwards efficient scopes to educational institutions by way of reduced costs, enhanced brand perception and elevated sales. Nevertheless, notable disputes prevail from obstructive electronic word-of-mouth and invasive and annoying online brand existence. Nowadays, students use online promotions to know about best universities for education globally. This university choice and students’ feedback observed by student-experience shared across social media platforms. Several methods have been employed for selecting the university but not providing accurate information. This paper is motivated towards applying Machine Learning for learning, analyzing and classifying the student information based on the student experience by means of tweets in twitter. The twitter data with student tweets is collected from benchmark twitter dataset and applied to the proposed method, Class-wise Linear Discriminant and Regression-based Binarized Nearest Learning (CLD-RBNL). The CLD-RBNL method is split into two sections. First, preprocessing and relevant feature selection (i.e. tweets) are acquired by employing Class-wise Linear Discriminant-based Feature Selection (CLDFS) model to obtain dimensionality reduced tweets. To this result, Regression-based Binarized Nearest Neighbor model is applied for maximum lead generation. The CLD-RBNL method is compared with other state-of-the-art methods and found to outperform in terms of sensitivity, specificity, processing time, lead generation accuracy and error rate.