Sentiment analysis technique on product reviews using Inception Recurrent Convolutional Neural Network with ResNet Transfer Learning

ABSTRACT

Nowadays, online shopping has become a typical option for customers to make purchases due to the rapid technological development of the Internet. Sentiment analysis (SA) of a huge count of user reviews on e-commerce sites can successfully increase the fulfillment of user, but it is difficult so far to predict the precise sentiment polarizations of the user evaluations owing to the variations on textual arrangement, sequence length, and complex logic. In this manuscript, Inception Recurrent Convolutional Neural Network with ResNet Transfer Learning by Red Fox Optimization Algorithm using sentiment analysis on product review (IRCNN-ResNet-RFOA-SA-PR) is proposed. Here, the input data are collected through Amazon Product Reviews database and then preprocessing the input data by using whitespace tokenization, Snowball stemming, and Gensim lemmatization. The preprocessing data are supplied to the ternary pattern and discrete wavelet transform for feature extraction. Then the optimal features are selected by Fire Hawk Optimization Algorithm. After that, IRCNN-ResNet recommends the product based on SA. Finally, IRCNN-ResNet parameter is optimized by RFOA. The proposed method attains 22.37%, 31.08%, and 21.90% greater accuracy and 19.37%, 21.08%, and 25.40% greater precision when compared with existing models, such as machine learning-based SA-PR including new term weighting with feature selection mode, weighted word embedding with deep neural network-based SA-PR, and SA for e-commerce PRs in Chinese utilizing sentiment lexicon with deep learning, respectively.

GRAPHICAL ABSTRACT

In this manuscript, Inception Recurrent Convolutional Neural Network with ResNet Transfer Learning by Red Fox Optimization Algorithm using sentiment analysis on product review (IRCNN-ResNet-RFOA-SA-PR) is proposed. Here, the input data are collected through Amazon Product Reviews database, then preprocessing the input data by using whitespace tokenization, Snowball stemming, and Gensim lemmatization. The preprocessing data are supplied to the ternary pattern and discrete wavelet transform for feature extraction.

KEYWORDS:

• Fire Hawk Optimization Algorithm
• Inception Recurrent Convolutional Neural Network
• online shopping
• polarity classification
• ResNet
• transfer learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/23080477.2024.2370210.