A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

Abstract

With more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching–learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110 K learner comments (referred to as Educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too.