Paper Key : IRJ************495
Author: Sanika Hanmant Dange,Sakshi Sanjay Dhumake,Shruti Sanjay Khade,Kumud Yashwant Patil
Date Published: 21 Nov 2024
Abstract
Sentiment analysis of product reviews on e-commerce websites helps to identify customer preferences. Aspect-based sentiment analysis (ABSA) goes a step further by pinpointing specific aspects of a product and analyzing the sentiment associated with each, offering a more granular understanding of customer attitudes. This method improves the traditional rating-based recommendation system by focusing on product aspects. To automate ABSA, a labeled dataset is necessary for training supervised machine learning models. However, their availability is limited due to the manual effort required to create such datasets. This annotated dataset, which contains customer reviews of the Apple iPhone 11, has been manually labeled with predefined aspect categories and their corresponding sentiments like positive, Negative, and Neutral. The accuracy of this dataset has been validated using several state-of-the-art machine learning techniques, including Naive Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor, and a Multi-Layer Perceptron (MLP) model built with the Keras API. These models enable the transition from a conventional rating- based approach to a more precise, aspect-driven analysis, leading to enhanced product recommendations based on customer reviews.