Keywords
E-Commerce
Sentiment Analysis
Lazada
How to Cite
Abstract
The rapid growth of e-commerce in Indonesia has increased competition in this industry. To retain customers and win the competition, e-commerce platforms need to deeply understand customer needs and wants. One way to understand customer sentiment is to use NLP and sentiment analysis. This research aims to analyze the influence of NLP: Sentiment Analysis in increasing customer satisfaction among e-commerce users: Lazada. This research uses quantitative methods with a survey approach. Data is collected by: Collecting reviews and comments from Lazada customers on platforms and social media. Analyze data using NLP techniques: Sentiment Analysis to identify customer sentiment. Conduct statistical analysis to determine the relationship between customer sentiment and customer satisfaction. The research results show that there is a significant relationship between customer sentiment and Lazada customer satisfaction. Customers who are satisfied with Lazada's services tend to give positive reviews. These positive reviews can help Lazada to understand customer needs and wants. This can be used to improve the quality of Lazada products and services, as well as provide a better customer experience. Here are some of the benefits of NLP: Sentiment Analysis in increasing customer satisfaction: Helping e-commerce players to understand customer needs and desires, helping e-commerce players to identify problems faced by customers, helping e-commerce players to improve the quality of products and services, helping e-commerce players to provide a better customer experience. Therefore, e-commerce players can utilize NLP: Sentiment Analysis to increase customer satisfaction and increase their business competitiveness.
References
Budiyono, R., & Nurdin, A. N. (2022). Analisis sentimen pelanggan terhadap produk e-commerce menggunakan metode naive bayes. Jurnal Teknologi Informasi dan Komunikasi, 17(2), 126-135.
Ghozali, I. (2017). Aplikasi analisis multivariate dengan program IBM SPSS 23 (8th ed.). Semarang: Badan Penerbit Universitas Diponegoro.
Hirschberg, J., & Manning, C. (2015). Advances in natural language processing. Science, 349, 261 - 266. https://doi.org/10.1126/science.aaa8685.
Ling, Y., & He, Q. (2010). A survey on opinion mining and sentiment analysis. Mining Text Data, 133-163.
Lutfi, M., & Prasetyo, Y. E. (2021). Analisis sentimen pelanggan terhadap layanan e-commerce menggunakan metode machine learning. Jurnal Teknik Informatika, 14(2), 109-121.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5, 1093-1113. https://doi.org/10.1016/J.ASEJ.2014.04.011.
Nur, M., & Nurdin, A. N. (2020). Analisis sentimen pelanggan terhadap produk e-commerce menggunakan metode support vector machine. Jurnal Teknologi Informasi dan Komunikasi, 16(2), 145-154.
Xu, Q., Zhu, L., Dai, T., Guo, L., & Cao, S. (2020). Non-negative matrix factorization for implicit aspect identification. Journal of Ambient Intelligence and Humanized Computing, 11, 2683-2699. https://doi.org/10.1007/S12652-019-01328-9.