Sentiment Analysis Using Maximum Entropy on Application Reviews (Study Case: Shopee on Google Play)

Authors

  • Ulinnuha Rhohmawati Universitas Sebelas Maret Surakarta
  • Isnandar Slamet Universitas Sebelas Maret Surakarta
  • Hasih Pratiwi Universitas Sebelas Maret Surakarta

DOI:

https://doi.org/10.26555/jiteki.v5i1.13087

Keywords:

Shopee, Google Play, Sentiment Analysis, Maximum Entropy, Word Association

Abstract

Shopee was one of the e-commerce application that could found on Google Play. The amount of Shopee application reviews on Google Play continues to grow over time. These make the company trying to get the overall information from all reviews because it would take a long time to read each of the reviews on Google Play. Therefore analysis was used using text mining. One part of text mining was sentiment analysis that applied the maximum entropy method to classification. Based on the results of the analysis found an accuracy of 97.32%. By using the maximum entropy method it could be concluded that word association obtained related to “applicationâ€, “promoâ€, “satisfyâ€, and “discount†for positive sentiment. Meanwhile for negative sentiment, the reviewers of Shopee application on Google Play were related to “problematicâ€, “loginâ€, “oldâ€, “verificationâ€, and “expensiveâ€. The results of this research in Indonesian.

Author Biography

Ulinnuha Rhohmawati, Universitas Sebelas Maret Surakarta

Departement of Statistics , Faculty of Mathematics and Natural Sciece

References

S. Anwar Hridoy, M. Ekram, M. Islam et al., “Localized Twitter Opinion Mining using Sentiment Analysis,†Decision Analytic, 2015, available at: Google Scholar.

N. Mehra, S. Khandelwal, P. Patel, “Sentiment Identification using Maximum Entropy Analysis of Movie Reviews,†International Journal of Computer Applications, 2016, available at: Google Scholar.

B. Wagh, J. Shinde, P. Kale, “A Twitter Sentiment Analysis using NLTK and Machine Learning Techniques,†International Journal of Emerging Research in Management and Technology., vol. 6, pp. 37, 2018, available at: Google Scholar.

W. Medhat, A. Hassan, and H.Korashy, “Sentiment Analysis and Applications: A Survey,†AIN Shams Engineering Journal., vol. 5, pp. 1093-1113, 2014, doi: 10.1016/j.asej.2014.04.011.

L. Zheng, H.Wang, and S.Gao, “Sentimental Feature Selection for Sentiment Analysis pf Chinese Online Reviews,†International Journal of Machine Learning and Cybernetics., vol.9, pp. 75-84, 2018, available at: Google Scholar.

H. Htet, and Y. Myint, “Social Media (Twitter) Data Analysis using Maximum Entropy Classifier on Big Data Processing Framework (Case Study : Analysis of Health Condition, Education Status, State of Business),†Journal of Pharmacognosy and Phytochemistry., vol. 7, pp. 695-700, 2018, available at: Google.

A. Gupte, S. Joshi, P. Gadhul, and A. Kadam, “Comparative Study of Classification Algorithm used in Sentiment Analysis,†International Journal of Computer Science and Information Technologies., vol. 5, pp. 6261-6264, 2014, available at: Google Scholar

D. Patel, S. Saxena, and T.Verma, “Sentiment Analysis using Maximum Entropy Algorithm in Big Data,†International Journal of Innovative Research in Science, Engineering and Technology., vol. 5, pp. 8356-8361, 2016, available at: Google Scholar.

P. Milev, “Conceptual Approach for Development of Web Scraping Application for Tracking Information,†Economic Articles., issue 3, pp. 475-485, 2017, available at: Google Scholar.

T. Agrawal, and A.Singhal, “An Efficient Knowledge-Based Text Pre-processing Approach for Twitter and Google+â€, Springer., pp. 379-389, ISBN 978-981-13-9942, 2019, available at: Google Scholar.

A. B. Panwar, M. A. Jawali, and D. N. Kyatanavar, “Fundamentals of Sentiment Analysis: Concepts and Methodology,†Sentiment Analysis and Ontology Engineering., pp. 25-48, ISBN 978-3-319-30317-8, 2016, available at: Google Scholar.

X. Xie, S.Ge, F.Hu, M.Xie, A and N. Jiang, “An Improved Algorithm for Sentiment Analysis Based on Maximum Entropy,†Soft Computing, vol.23, pp. 599-611, 2017, available at: Google Scholar.

Z. Zhao, T. Liu, B. Li, and X. Bu, “Correction to: Guiding the Training of Distributed text Representation With Supervised Weighting Scheme for Sentiment Analysis.†Data Science and Engineering, vol. 2, Issue. 4, 2017, doi: 10.1007/s41019-017-0040-6.

C. C. Aggrawal, Data Mining: The Textbook, Swiss: Springer International Publishing Switzerland, 2015, available at: Google.

S. Suthaharan, “Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning,†Springer., pp.10, 2015, available at: Google Scholar.

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Published

2019-07-01

How to Cite

[1]
U. Rhohmawati, I. Slamet, and H. Pratiwi, “Sentiment Analysis Using Maximum Entropy on Application Reviews (Study Case: Shopee on Google Play)”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 5, no. 1, pp. 44–49, Jul. 2019.

Issue

Vol. 5 No. 1 (2019): June

Section

Articles

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