Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 19, 08 December 2023
Open
Access | Article
Recommendation and sentiment classification on E-Commerce reviews
* Author to whom correspondence should be addressed.
Abstract
Due to the improvement of online shopping mode, an increasing number of customers rely on reviews displayed on online shopping websites to choose products, and there are also more and more sellers taking consumers' text reviews into consideration to modify their products. Therefore, understanding and analyzing these reviews are getting increasingly significant. This study utilized natural language processing on E-Commerce Reviews. First, I used the Naïve Bayes model and Support Vector Machine to classify whether a reviewer recommends the reviewed product; the accuracies are both 87%. Then I used the random forest to classify the reviewer's positive, neutral, and negative sentiment on each review, which gave 86% precision.
Keywords
natural language processing, sentiment classification, text reviews, unbalanced data.
References
1. Brooks, Nick. (2018). Women’s E-Commerce Clothing Reviews. Kaggle. https://www.kaggle.com/datasets/nicapotato/womens-ecommerce-clothing-reviews.
2. Jagdale, R. S., Shirsat, V. S. and Deshmukh, S. N. (2019). Sentiment analysis on product reviews using machine learning techniques, Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 768 pp. 639–647.
3. Vanderplas, J. (2017). Python Data Science Handbook. O’Reilly Media, Inc.
4. Xie, S. (2019). Sentiment Analysis Using Machine Learning Algorithms: Online Women Clothing Reviews.
5. Agarap, A. F. m. (2020). Statistical Analysis on E-Commerce Reviews, with Sentiment Classification Using Bidirectional Recurrent Neural Network.
6. Alrehili, A. and Albalawi, K. (2019). Sentiment analysis of customer reviews using ensemble method, 2019 International Conference on Computer and Information Sciences (ICCIS).
7. Kumar, G. R. (2020). NLP with Women Clothing Reviews. Kaggle. https://www.kaggle.com/code/granjithkumar/nlp-with-women-clothing-reviews
8. Lemaitre, G. (2014, August). 5. Ensemble of Samplers. Imbalanced Learn. https://imbalanced-learn.org/stable/ensemble.html#forest
9. Boisberranger, J. D. (2007). 1.4. Support Vector Machines. Scikit-Learn. https://scikit-learn.org/stable/modules/svm.html
10. Yang, P., Wang, D., Du, X.-L. and Wang, M. (2018). Evolutionary dbn for the customers’ sentiment classification with incremental rules, Industrial Conference on Data Mining ICDM 2018: Advances in Data Mining. Applications and Theoretical Aspects pp. 119–134.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-203-9
- ISBN (Online)
- 978-1-83558-204-6
- Published Date
- 08 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/19/20230528
- Copyright
- 08 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited