Sentence type identification-based product review sentiment analysis using BeDi-DC and Log-Squish CNN

Abstract

Abstract People share their comments and reviews on public platforms in advanced social media systems. The customer’s perception of the product is reviewed by analysing the sentiment of product reviews, thus assisting in business decision-making. In most of the prevailing works, the sentence type of product review was not recognised to analyse the sentiment; thus, the complexity of the sentiment analysis process increased. Thus, this study performs sentence-type assessment-based product review sentiment analysis using beta divergence divide and conquer (BeDi-DC) and Log-Squish Convolutional Neural Network (Log-Squish CNN). Initially, the input product review data were preprocessed, followed by word count extraction. Next, the data were clustered with the Permutation Distribution Hierarchical Clustering (PerDHC) algorithm and classified into real and fake reviews by the proposed Log-Squish CNN approach. Subsequently, the BeDi-DC technique was used to identify the sentence types of real reviews. Word sense disambiguation is performed on the multi-target review to identify the exact target. Next, to analyse the sentiwords and their score values, the Mean-Senticircle Method (MeSM) was utilised. Finally, using the Log-Squish CNN model, the sentiment of the review was classified as positive, neutral, or negative. The accuracy, f-measure, and degree correlation attained by the proposed model are 98.99%, 98.78%, and 0.845, respectively, thus outperforming the prevailing models.

Keywords

• Machine learning (ML)
• Target identification
• Word sense disambiguation
• Product review analysis