Abstract: Since most businesses today are conducted online, it is crucial that each customer provide feedback on the various items offered. Evaluating online product sentiment and making suggestions using state-of-the-art machine learning and deep learning algorithms requires a comprehensive pipeline. Thus, this paper addresses the need for a comprehensive pipeline to analyze online product sentiment and recommend products using advanced machine learning and deep learning algorithms. The methodology of the research is divided into two parts: the Sentiment Analysis Approach and the Product Recommendation Approach. The study applies several state-of-the-art algorithms, including Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Bidirectional Long-Short-Term-Memory (BI-LSTM), Convolutional Neural Network (CNN), Long-Short-Term-Memory (LSTM), and Stacked LSTM, with proper hyperparameter optimization techniques. The study also uses the collaborative filtering approach with the k-Nearest Neighbours (KNN) model to recommend products. Among these models, Random Forest achieved the highest accuracy of 95%, while the LSTM model scored 79%. The proposed model is evaluated using Receiver Operating Characteristic (ROC) - Area under the ROC Curve (AUC). Additionally, the study conducted exploratory data analysis, including Bundle or Bought-Together analysis, point of interest-based analysis, and sentiment analysis on reviews (1996-2018). Overall, the study achieves its objectives and proposes an adaptable solution for real-life scenarios.

Mohammed Alonazi, “Analyzing Sentiment in Terms of Online Feedback on Top of Users' Experiences” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141114

@article{Alonazi2023,
title = {Analyzing Sentiment in Terms of Online Feedback on Top of Users' Experiences},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0141114},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0141114},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Mohammed Alonazi}
}