A Implementation of Text Mining In Sentiment Analysis of Shopee Indonesia Using SVM

  • Kusdarnowo Hantoro * Mail Universitas Bhayangkara Jaya, Indonesia
  • Dwipa Handayani Universitas Bahayangkara Jaya, Indonesia
  • Siti Setiawati Universitas Bahayangkara Jaya, Indonesia
DOI: https://doi.org/10.47065/bit.v3i2.282
Keywords: online business, opinion, assessment,classification,recommendation

Abstract

Many online shopping users convey an assessment of a product through status comments. Comments indicate a non-standard form of expression. Nowadays, user comments are increasing rapidly, which makes data management difficult. Today's society has so many choices; most of them are used for preference as a final recommendation. The top item shows the preferences of the available items, recommended based on future predictability. The predictive rating of some items is used by the user to recommend the item to other users. Sentiment analysis can recommend items of choice for online shopping in various fields of application, especially in e-commerce. Sentiment analysis is used to identify opinions, ideas, or thoughts from online media. Shopee is one of the largest online business platforms. Classification Sentiment analysis towards Shopee based on the Support Vector Machine (SVM) was used in this study on the Shopee application using 990 training data and 110 test data. From the test data, 28 data entered the negative class and the remaining 82 data entered the positive class and resulted in an accuracy rate of 80.90%, meaning that from 110 assessments there were 89 assessments classified exactly in the sentiment class.

References

C. S. M A Rahman, Aminul Islam, Esha B H, Sultana N, “Q2 - Consumer buying behavior towards online shopping_.pdf.” pp. 1–22, 2018.

M. Anisur Rahman, M. Aminul Islam, B. Humyra Esha, N. Sultana, and S. Chakravorty, “Cogent Business & Management Consumer buying behavior towards online shopping: An empirical study on Dhaka city, Bangladesh,” Cogent Bus. Manag., vol. 5, p. 3, 2018.

M. Khader, A. Awajan, and G. Al-Naymat, “The Effects of Natural Language Processing on Big Data Analysis: Sentiment Analysis Case Study,” ACIT 2018 - 19th Int. Arab Conf. Inf. Technol., pp. 1–7, 2019.

M. F. Daohai Zhang, Xue Liu, Juan Li, “Q0 - Sentiment Analysisv Enhanced Reader.pdf,” in IOP Conf. Series: Journal of Physics: Conf. Series, 2018.

A. F. Salam, H. Dai, and L. Wang, “Online Users’ Identity Theft and Coping Strategies, Attribution and Sense of Urgency: A Non-Linear Quadratic Effect Assessment,” Inf. Syst. Frointiers, 2021.

M. Lu and F. Li, “Survey on lie group machine learning,” Big Data Min. Anal., vol. 3, no. 4, pp. 235–258, 2020.

D. Zhang et al., “Knowledge-oriented Hierarchical Neural Network for Sentiment Classification,” in IOP Conference Series : Material Science and Engineering, 2019.

A. V. Sakhare and P. S. Kasbe, “A review on road accident data analysis using data mining techniques,” Proc. 2017 Int. Conf. Innov. Information, Embed. Commun. Syst. ICIIECS 2017, vol. 2018-Janua, pp. 1–5, 2018.

I. M. Karo Karo, M. F. Md Fudzee, S. Kasim, and A. A. Ramli, “Sentiment Analysis in Karonese Tweet using Machine Learning,” Indones. J. Electr. Eng. Informatics, vol. 10, no. 1, pp. 219–231, 2022.

C. M. Liyew and H. A. Melese, “Machine learning techniques to predict daily rainfall amount.”

M. M. Rahman et al., “Sentiment topic mining based on comment tags.”

S. Jarp et al., “Supervised Ensemble Machine Learning Aided Performance Evaluation of Sentiment Classification,” IOP Conf. Ser. J. Phys. Conf. Ser., vol. 1060, p. 12036, 2018.

Y. Qi, H. Li, N. Liu, A. A. Arman, A. B. Kawi, and R. Hurriyati, “Sentiment analysis enhancement with target variable in Kumar’s Algorithm,” in IOP Conference Series : Material Science and Engineering, 2019, p. 128.

D. S. Maylawati, Y. J. Kumar, and F. B. Kasmin, “Deep Learning-Now and Next in Text Mining and Natural Language Processing.”

S. Gao, “The Application of Information Classification in Agricultural Production Based on Internet of Things and Deep Learning,” IEEE Access, vol. 10, pp. 22622–22630, 2022.

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Published
2022-06-30
How to Cite
Hantoro, K., Handayani, D., & Setiawati, S. (2022). A Implementation of Text Mining In Sentiment Analysis of Shopee Indonesia Using SVM. Bulletin of Information Technology (BIT), 3(2), 115 - 120. https://doi.org/10.47065/bit.v3i2.282
Issue
Vol 3 No 2: Juni 2022
Section
Articles

Copyright (c) 2022 Kusdarnowo Hantoro, Dwipa Handayani, Siti Setiawati

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