Comparative Analysis of Machine Learning Algorithms for Sentiment Classification in Amazon Reviews

Authors

  • Bole Yu

DOI:

https://doi.org/10.54097/eqmavw44

Keywords:

Sentiment Analysis; Amazon Reviews; Machine Learning; Word Cloud; Text Classification

Abstract

Sentiment analysis serves as a crucial approach for gauging public opinion across various sectors, including the realm of product reviews. This study focuses on the evaluation of customer sentiments in Amazon reviews using an array of machine learning algorithms—Logistic Regression, Random Forest, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The methodology employed is robust, characterized by meticulous parameter tuning based on both theoretical and empirical considerations. Comparative analysis of these algorithms, grounded in accuracy and other performance metrics, offers valuable insights into their respective efficacies and limitations for sentiment analysis tasks. The findings of this study contribute to an enhanced understanding of the performance of different machine learning algorithms in sentiment classification and provide a foundation for future research in this domain.

Downloads

Download data is not yet available.

References

Das, B., & Chakraborty, S. An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation. arXiv preprint arXiv:1806.06407, cs.CL. 2018.

Li, X., Sun, X., Xu, Z., & Zhou, Y. Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews. arXiv preprint arXiv:2111.06070, cs.CL. 2021.

Das, M., Selvakumar, K., & Alphonse, P. J. A. A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset. arXiv preprint arXiv:2308.04037, cs.CL.2023.

Guda, B. P. R., Srivastava, M., & Karkhanis, D. Sentiment Analysis: Predicting Yelp Scores. arXiv preprint arXiv:2201.07999, cs.LG. 2020.

Khan, M., & Malik, K. Sentiment Classification of Customer's Reviews About Automobiles in Roman Urdu. In Advances in Intelligent Systems and Computing,2018, 630-640.

Kazhuparambil, S., & Kaushik, A. Cooking Is All About People: Comment Classification On Cookery Channels Using BERT and Classification Models (Malayalam-English Mix-Code). arXiv preprint arXiv:2007.04249, cs.CL.,2020.

Krishnan, A. B. H. Unmasking Falsehoods in Reviews: An Exploration of NLP Techniques". arXiv preprint arXiv:2307.10617, cs.IR. 2023.

Radford, A., Jozefowicz, R., & Sutskever, I. Learning to Generate Reviews and Discovering Sentiment. arXiv preprint arXiv:1704.01444, cs.LG.2017.

Mir, A. Q., Khan, F. Y., & Chishti, M. A. Online Fake Review Detection Using Supervised Machine Learning And BERT Model. arXiv preprint arXiv:2301.03225, cs.CL.2023.

Shrestha, N., & Nasoz, F. Deep Learning Sentiment Analysis of Amazon.Com Reviews and Ratings". International Journal on Soft Computing, Artificial Intelligence and Applications, 2019, 8(1), 01-15.

Minaee, S., Azimi, E., & Abdolrashidi, A. A. Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models. arXiv preprint arXiv:1904.04206, cs.CL. 2019.

Krishnan, J., Purohit, H., & Rangwala, H. Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift". arXiv preprint arXiv:2002.10937, cs.LG. 2020.

Saifullah, S., Fauziyah, Y., & Aribowo, A. S. Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data. Jurnal Informatika, 2021, 15(1), 45.

Feng, H., & Lin, R. Sentiment Classification of Food Reviews. arXiv preprint arXiv:1609.01933, cs.CL.2016.

Dasgupta, S., & Sen, J. A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach. arXiv preprint arXiv:2212.10051, cs.CL.2022.

So, C. What Emotions Make One or Five Stars? Understanding Ratings of Online Product Reviews by Sentiment Analysis and XAI. arXiv preprint arXiv:2003.00201, cs.AI. 2020.

Downloads

Published

22-01-2024

How to Cite

Yu, B. (2024). Comparative Analysis of Machine Learning Algorithms for Sentiment Classification in Amazon Reviews. Highlights in Business, Economics and Management, 24, 1389-1400. https://doi.org/10.54097/eqmavw44