Improved chaotic fruit fly optimization with elman neural network based data-driven approach on product reviews classification

In the digital era, business platforms have considerably evolved towards online stores on the internet. Through the internet-based platform, customers can easily buy products through their smartphones and receive delivery at the place without going to the shopping mall. On the other hand, the disadvantage of these platforms is that customers need to learn about the product quality. Thus, these platform services frequently offer customer feedback to give feedback about the shipped items. Feedback is a valuable source for analyzing customer satisfaction. Customer satisfaction is effectively enhanced by performing a Sentiment Analysis (SA) of many customer ratings on E-commerce platforms. Yet, predicting the correct sentiment difference in customer feedback is challenging due to complicated logic, sequence length, and text pattern adjustments. This article focuses on designing and developing Enhanced NLP with Data-driven Product Reviews Classification using the improved chaotic fruit fly optimization (ENLPPR-ICFFO) method. The major goal of the ENLPPR-ICFFO method is to detect and classify the sentiments existing in product ratings. To accomplish this, the presented ENLPPR-ICFFO method employs data pre-processing to be consistent. Then, the Continuous Bag-of-Words (CBoW) method is utilized for the feature extraction. The extreme learning machine (ELM) method is used for the product review classification. The ENLPPR-ICFFO methodology includes an ICFFO-based hyperparameter selection technique for improving the overall product review classification technique. The empirical findings of the projected ENLPPR-ICFFO methodology are tested using a set of datasets. The performance validation of the ENLPPR-ICFFO technique portrayed superior accuracy values of 98.04 % and 96.57 % over existing methods under Amazon-Fine-Food-Reviews and Amazon-Products datasets.