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A Supervised Learning Technique for Classifying Amazon Product Reviews based on Buyers Sentiments
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 38 |
| Year of Publication: 2020 |
| Authors: Richa Chunekar Kokje, Gajendra Singh Chouhan |
10.5120/ijca2020920956
|
Richa Chunekar Kokje, Gajendra Singh Chouhan . A Supervised Learning Technique for Classifying Amazon Product Reviews based on Buyers Sentiments. International Journal of Computer Applications. 175, 38 ( Dec 2020), 36-41. DOI=10.5120/ijca2020920956
Abstract
A number of applications using internet provide vary essential services. Among them social media and ecommerce platforms are very common. These platforms include a large amount of data which is generated by the users. That data is available in the form of opinion about some post or review. Means the text with emotions which is contain the buyers or user sentiment about some kind of product or service. In this presented work a data mining model is introduced that offers sentiment based text classification for the Amazon product reviews. The proposed data model first preprocesses data and extract the actual review text, in next phase of preprocess the stop words and special characters are removed. The refined text is further utilized with two feature selection techniques first is based on TF-IDF which is used for selecting intense keywords from the review text. Additionally the second feature is selected using NLP text parser. That parser basically performs the POS (Part Of Speech) tagging of review text. Using the obtained POS tags the NLP feature is contracted. Both the features are combined in next and two supervised learners are used namely SVM (support vector machine) and SVR (support vector regression). The experimental results of both the model is measured and compared. The performance study demonstrates the proposed SVM based classifier performs classification accurately and efficiently as compared to SVR based classifier.
References
- A. Usai, M. Pironti, M. Mital, C. A. Mejri. “Knowledge discovery out of text data: a systematic review via text mining”. Journal of Knowledge Management 2018.
- P. Mikalef, J. Krogstie, I. O. Pappas, P. Pavlou, “Exploring the relationship between big data analytics capability andcompetitive performance: The mediating roles of dynamic and operationalcapabilities”. Elsevire 2020.
- F. J. B. Stoffi, J. Niederreiter, M. Riccaboni, “Supervised Learning for the Prediction of Firm Dynamics”. arXiv 2020
- V. Palanisamy, R. Thirunavukarasu, “Implications of big data analytics in developing healthcare frameworks –A review”, Science Direct 2019.
- D. W. Otter, J. R. Medina, J. K. Kalita, “A Survey of the Usages of Deep Learning for Natural Language Processing”, IEEE Transactions on Neural Networks and Learning Systems, Vol. XX, NO. X, July 2019.
- E. J. Topol. “High-performance medicine: the convergence of human and artificial intelligence”. Nature Medicine, Vol 25, January 2019.
- B. Liu, “Sentiment Analysis and Opinion Mining”, Sentiment Analysis Symposium 2012.
- B. Guo, Y. Liu , Y. Ouyang, V. W. Zheng, D. Zhang, Z. Yu, “Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey”. IEEE Access 2019.
- S. Gupta, A. Leszkiewicz, V. Kumar, T. Bijmolt, D. Potapov, “Digital Analytics: Modeling for Insights and New Methods”, Journal of Interactive Marketing 2020.
- W. N. Tun, “Sentiment Classification of Movie Review Comments Using Naive Bayesian Model”, University of Computer Studies, Yangon 2016.
- A. S. Rathor, A. Agarwal, P. Dimri, “Comparative Study of Machine Learning Approaches for Amazon Reviews”, Procedia Computer Science 132 (2018) 1552–1561.
- T. U. Haque, N. N. Saber, F. M. Shah, “Sentiment Analysis on Large Scale Amazon Product Reviews”, 2018 IEEE International Conference on Innovative Research and Development (ICIRD), 978-1-5386-5283-1/18/$31.00 ©2018 IEEE.
- R. S. Jagdale, V. S. Shirsat, S. N. Deshmukh, “Sentiment Analysis on Product Reviews Using Machine Learning Techniques”, Cognitive Informatics and Soft Computing, Advances in Intelligent Systems and Computing 768, https://doi.org/10.1007/978-981-13-0617-4_61.
- K. S. Srujan, S. S. Nikhil, H. RaghavRao, K. Karthik, B. S. Harish, and H. M. Keerthi Kumar, “Classification of Amazon Book Reviews Based on Sentiment Analysis”, Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing 672, https://doi.org/10.1007/978-981-10-7512-4_40.
- J. Singh, G. Singh, R. Singh, “Optimization of sentiment analysis using machine learning classifers”, Hum. Cent. Comput. Inf. Sci. (2017) 7:32 DOI 10.1186/s13673-017-0116-3.
- K. Dashtipour, S. Poria, A. Hussain, E. Cambria, A. Y. A. Hawalah, A. Gelbukh, Q. Zhou, “Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques”, CognComput (2016) 8:757–771 DOI 10.1007/s12559-016-9415-7.
- B. Zhao, Y. He, C. Yuan, Y. Huang, “Stock Market Prediction Exploiting Microblog Sentiment Analysis”, 978-1-5090-0620-5/16/$31.00 c 2016 IEEE.
- A. Potdar, P. Patil, R. Bagla, R. Pandey, Prof. N. Jadhav, “SAMIKSHA - Sentiment Based Product Review Analysis System”, Procedia Computer Science 78 ( 2016 ) 513 – 520, 2016 The Authors. Published by Elsevier B.V.
- S. Poria, E. Cambria, N. Howard, G. B. Huang, A. Hussain, “Fusing audio, visual and textual clues for sentiment analysis from multimodal content”, Neurocomputing 174 (2016) 50–59, & 2015 Elsevier B.V. All rights reserved.
- K. Santos, E. Loures, F. Piechnicki, O. Canciglieri, “Opportunities Assessment of Product Development Process in Industry 4.0”, Procedia Manufacturing 11 (2017) 1358 – 1365, © 2017 Published by Elsevier B.V.
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