Aspect Based Suggestion Classification Using Deep Neural Network and Principal Component Analysis with Honey Badger Optimization

Aspect based suggestion is the process of analyzing the aspect of the review and classifying them as suggestion or non-suggestion comment. Today, online reviews are becoming a more popular way to express suggestions. To manually analyze and extract recommendations from such a large volume of reviews is practically impossible. However, the existing algorithm yields low accuracy with more errors. A deep learning-based DNN (Deep Neural Network) is created to address these problems. Raw data's are collected and pre-processed to remove the unnecessary contents. After that, a count vectorizer is utilized to convert the words into vectors as well as to extract features from the data. Then, reducing the dimension of the feature vector by applying a hybrid PCA-HBA (Principal Component Analysis-Honey Badger Algorithm). HBA optimization is utilized to select the optimal number of components to enhance the accuracy of the proposed model. Then, the features are classified using two trained deep neural network. One trained model is utilized to identify the aspect of the review, and another trained model is utilized to identify whether the aspect is a suggestion or non-suggestion. The experimental analysis shows that the proposed approach achieves 93% accuracy and 93% specificity for aspect identification as well as 87% accuracy and 66% specificity for the classification of suggestions. Thus, the designed model is the best choice for aspect-based suggestion classification.