Recommendation and sentiment classification on E-Commerce reviews

Tianyi Lin

doi:10.54254/2753-8818/19/20230528

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.

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[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

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[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.

Cite this article

Lin,T. (2023). Recommendation and sentiment classification on E-Commerce reviews. Theoretical and Natural Science,19,161-168.

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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About volume

Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics

ISBN：978-1-83558-203-9(Print) / 978-1-83558-204-6(Online)

Editor：Marwan Omar, Roman Bauer

Conference website: https://www.confciap.org/

Conference date: 25 March 2023

Series: Theoretical and Natural Science

Volume number: Vol.19

ISSN：2753-8818(Print) / 2753-8826(Online)

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