Review of the Lazada application on Google Play Store: Sentiment Analysis
DOI:
https://doi.org/10.24191/jcrinn.v9i1.412Keywords:
Sentiment Analysis, Online Shopping Platform, E-Commerce, Natural Language Processing, Google PlaystoreAbstract
Sentiment analysis is a technique for gaining meaningful insight from unstructured and unorganized textual content from multiple platforms. It is a Natural Language Processing (NLP) method that may categorize data or reviews as positive, negative, or neutral. Analyzing reviews on the internet could yield helpful, actionable insights that could be economically beneficial to vendors or other interested parties. There are various online shopping platforms due to customer demand and rely on reviews and ratings while selecting choices. However, it could be difficult to tell whether the reviews are positive, negative, or both. The objective of this research is to classify the reviews on Lazada which is one of the online shopping platforms as positive, neutral or negative sentiments and to examine the words used most frequently in Lazada users’ reviews on the Google Play Store. This research used data from 7267 reviews that were extracted from the Google Play Store between 2019 and 2022 using the Google-play-scraper Python script. The reviews have been analyzed using the Valence Aware Dictionary and Sentiment Reasoner (VADER)’s to determine whether they are positive, neutral, or negative. The results indicate that 4229 reviews are positive. There are about 2857 negative sentiments and 181 neutral sentiments. It demonstrates that more people are happy using the Lazada app between 2019 and 2022. The results also demonstrate that sentiment analysis is an effective tool for categorising and evaluating other people's reviews and feedback.
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Afifah, K., Yulita, I. N., & Sarathan, I. (2021). Sentiment analysis on telemedicine app reviews using xgboost classifier. 2021 International Conference on Artificial Intelligence and Big Data Analytics (pp. 22-27). doi: 10.1109/ICAIBDA53487.2021.9689762
Annie, A. (2021). The state of mobile 2021. Retrieved from https://www.appannie.com/en/go/state-of-mobile-2021/
Apptentive. (2019). Mobile app benchmarks: The average ratings, reviews, and retention rates. Apptentive Blog. Retrieved 29 March, 2023, from https://www.apptentive.com/blog/2019/03/11/mobile-app-benchmarks-the-average-ratings-reviews-and-retention-rates/
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer New York.
Bush, T. (2020, Jun). Descriptive analysis: How-to, types, examples. Pestle Analysis. Retrieved from https://pestleanalysis.com/descriptive-analysis/
Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter? An empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016.
Dudhankar, V., Sen, N., Langde, A., & Kupade, V. (2022, June). Google playstore review sentiment analysis. International Research Journal of Modernization in Engineering Technology and Science, 4(06), 3901-3911. https://www.irjmets.com/uploadedfiles/paper/issue_6_june_2022/26972/final/fin_irjmets1656231688.pdf
Hossain, M. S., & Rahman, M. F. (2022, Jan). Sentiment analysis and review rating prediction of the users of Bangladeshi shopping apps. IGI Global. Retrieved from https://www.igi-global.com/chapter/sentiment-analysis-and-review-rating-prediction-of-the-users-of-bangladeshi-shopping-apps/306094.
Hu, P., Li, Q., & Ye, Y. (2019). Customer review analysis using natural language processing techniques: a case study of e-commerce platforms. Sustainability, 11(8), 2234.
iPrice Group. (2021). iPrice insights: State of ecommerce in Southeast Asia 2021. Retrieved from https://iprice.my/insights/mapofecommerce/en/
javaTpoint. (2021). Machine learning decision tree classification algorithm - javatpoint. Retrieved from https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm
Kanaris, I., Stamatatos, E., & Fakotakis, N. (2020). Tf-idf vs word2vec vs glove: An overview. arXiv preprint. arXiv:2010.02545
Kim, M.-G., & Yoon, Y.-J. (2020). Negative consequences of text classification: A critical review and practical remedies. Journal of the Association for Information Science and Technology, 71(8), 936–947.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2015). Efficient estimation of word representations in vector space. arXiv preprint. arXiv:1301.3781.
Naseem, S., Mahmood, T., Asif, M., Rashid, J., Umair, M., & Shah, M. (2021). Survey on sentiment analysis of user reviews. 2021 International Conference on Innovative Computing (ICIC) (pp.1-6). https://doi: 10.1109/ICIC53490.2021.9693029.
Ninyikiriza, D. L., Sourav, A. I., & Setyohadi, D. B. (2020). Increasing user satisfaction of mobile commerce using usability. International Journal of Advanced Computer Science and Applications, 11(8).
Nugroho, K. S., Sukmadewa, A. Y., Wuswilahaken DW, H., Bachtiar, F. A., & Yudistira, N. (2021). Bert fine-tuning for sentiment analysis on Indonesian mobile apps reviews. In 6th International Conference on Sustainable Information Engineering and Technology 2021 (pp.258–264). https://doi.org/10.1145/3479645.3479679
Raj, A. (2020, Nov). Perfect recipe for classification using logistic regression. Towards Data Science. Retrieved from https://towardsdatascience.com/the-perfect-recipe-for-classification-using-logistic-regression-f8648e267592#:~:text=Logistic%20regression%20is%20a%20classification
Rajendran, S. (2021). Improving the performance of global courier & delivery services industry by analyzing the voice of customers and employees using text analytics. International Journal of Logistics Research and Applications, 24(5), 473-493. https://doi.org/10.1080/13675567.2020.1769042 doi:10.1080/13675567.2020.1769042
Ranjan, S., & Mishra, S. (2020). Comparative sentiment analysis of app reviews. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT (pp. 1-7). https://doi: 10.1109/ICCCNT49239.2020.9225348
Similarweb. (2023, Jul). Top websites ranking most visited ecommerce shopping websites in Malaysia. Similarweb LTD. Retrieved from https://www.similarweb.com/top-websites/malaysia/e-commerce-
andshopping/#:~:text=shopee.com.my%20ranked%20number,eCommerce%20%26%20Shopping%20websites%20in%20Malaysia.
Sparck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11–21.
Sunil, R. (2019, Mar). Understanding support vector machine algorithm from examples (along with code). Retrieved from https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Association for Computational Linguistics.
arXiv:cs/0212032
Wei, H. (2019, Mar). Nlp pipeline 101 with basic code example—feature extraction. Voice Tech Podcast. Retrieved from https://medium.com/voice-tech-podcast/nlp-pipeline-101-with-basic-code-example-feature-extraction-ea9894ed8daf#:~:text=Feature%20extraction%20step%20means%20to
Yee, A., Kee, D., Xing, C., Qian, P., Siew, M., & Dehrab, A. (2019, 10). Lazada group. Journal of International Conference Proceedings, 2(No 2 (2019)), 19-29. https://doi: 10.32535/jicp.v2i2.599.
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Copyright (c) 2024 Nur Amalina Shafie, Amyra Nur Ain Zulkifli (Author)
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