Consumer Opinion Extraction Using Text Mining for Product Recommendations On E-Commerce

Erlina Halim, Ronsen Purba, Andri Andri

Abstract


This study aims to evaluate consumer opinions in text form on e-commerce to determine the accuracy of ratings given by consumers with opinions using text mining with the lexicon approach. The research data was obtained online using a crawling technique using the API provided by Shopee. The conditions of diverse opinions and use of non-standard words are challenges in processing opinions. Opinion must be processed normalization and repairs using dictionary of words before going to extract using lexicon approach. Dictionary of words contain opinions with weights that are worth 1 to 5 for positive opinions and are worth -1 to -5 for negative opinions. For each opinion will be classified using the maximum ratio of the weight of positive opinion compared to the weight of negative opinion. The classification of opinion produced is positive, negative or neutral. Opinion classification is then compared with the rating classification to work out the extent of accuracy. The comparison produces an accuracy of 80.34% by completing an opinion dictionary.

Keywords


text mining; lexicon; classification; consumer opinion; e-commerce

Full Text:

PDF

References


Ramanathan, V. & Meyyappan, T. “Twitter Text Mining for Sentiment Analysis on People’s Feedback About Oman Tourism”, 2019 4th MEC International Conference on Big Data and Smart City, ICBDSC 2019. IEEE, 2019, pp. 1–5.

Xia, P. & Jiang, W. “Understanding the Evolution of Fine-Grained User Opinions in Product Reviews”, Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ ScalCom/CBDCo. IEEE, 2018, pp. 1335–1340.

Fan, Z., Chang, D. & Cui, J. “Algorithm in E-commerce Recommendation”, 2018 5th International Conference on Industrial Economics System and Industrial Security Engineering (IEIS). IEEE, 2018, pp. 1–6.

Tama, V. O., Sibaroni, Y. & Adiwijaya. “Labeling Analysis in the Classification of Product Review Sentiments by using Multinomial Naive Bayes Algorithm”, Journal of Physics: Conference Series, 1192, 2019, p. 012036.

Vikas, B. O. & Mungara, J. “An Enhanced Extraction and Summarization Technique with User Review Data for Product Recommendation to Customers”, International Journal of Scientific Research in Science, Engineering and Technology, 2(6), 2016, pp. 25–30.

Addepalli, S. L. et al. “A Proposed Framework for Measuring Customer Satisfaction and Product Recommendation for Ecommerce”, International Journal of Computer Applications, 138(3), 2016, pp. 30–35.

Sohail, S. S., Siddiqui, J. & Ali, R. “User Feedback Scoring and Evaluation of a Product Recommendation System”, IEEE, 2014.

Rajganesh, N., Nandhini, R. & Sumitha, M. “A Recommendation System for Online Products by Analyzing the Customer Feedback”, International Journal of Computer Science Trends and Technology, 4(2), 2016, pp. 14–18.

Al-Rubaiee, H. et al. “Techniques for Improving the Labelling Process of Sentiment Analysis in the Saudi Stock Market”, International Journal of Advanced Computer Science and Applications, 9(3), 2018.

Yassine, M. & Hajj, H. “A Framework for Emotion Mining from Text in Online Social Networks”, Proceedings - IEEE International Conference on Data Mining, ICDM, December 2010, pp. 1136–1142.

Davydova, O. Medium. https://medium.com/@datamonsters/text-preprocessing-in-python-steps-tools-and-examples-bf025f872908, 2018, retrieved April 9, 2019.

Islam, M., “Numeric Rating of Apps on Google Play Store by Sentiment Analysis on User Reviews”, 1st International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), At Military Institute of Science and Technology, Dhaka, Bangladesh, 2014.

Pamungkas, E. W. & Putri, D. G. P. “An Experimental Study of Lexicon-Based Sentiment Analysis on Bahasa Indonesia”, Proceedings - 2016 6th International Annual Engineering Seminar, InAES 2016, pp. 28–31.

Dhaoui, C., Webster, C.M., Tan, L.P., "Social Media Sentiment Analysis: Lexicon Versus Machine Learning", Journal of Consumer Marketing, Vol. 34 Issue: 6, 2017, pp.480-488

Nurfalah, A. & Suryani, A. A. “Analisis Sentimen Berbahasa Indonesia dengan Pendekatan Lexicon-Based Pada Media Sosial”, Jurnal Masyarakat Informatika Indonesia, 2(1), 2017, pp. 1–8.

Suyanto. Data Mining Untuk Klasifikasi dan Klasterisasi Data. Bandung: Informatika, 2019.

Thelwall, M. SentiStrength. http://sentistrength.wlv.ac.uk/, 2012, retrieved April 2019.

Katadata. “Perilaku Konsumen E-Commerce”. Katadata Insight Center, November 2018, Indonesia.

Han, SuHun. Googletrans: Free and Unlimited Google translate API for Python. 2018. https://py-googletrans.readthedocs.io/ en/latest/, retrieved Juli, 2019.




DOI: http://dx.doi.org/10.24014/ijaidm.v4i1.10834

Refbacks

  • There are currently no refbacks.


Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone./ Hp.: +62 852-7535-9942


Journal Indexing:

Google Scholar | ROAD | PKP Index | BASE | ESJI | Journal Factor | General Impact Factor | Garuda | Moraref | One Search | Cite Factor | Crossref | WorldCat | Neliti  | SINTA

IJAIDM Stats