Sentiment Analysis on TikTok Shop Reviews Using Long Short-Term Memory Method to Find Business Opportunity

  • Cahyarini Maulida Tri Yunanda Department of Computer Science, Universitas AMIKOM Yogyakarta
  • Muhammad Hanafi Department of Computer Science, Universitas AMIKOM Yogyakarta
  • Windha Mega Pradnya Dhuhita Department of Computer Science, Universitas AMIKOM Yogyakarta
Abstract views: 322 , PDF downloads: 329
Keywords: TikTok shop, Business, Sentiment analysis, Social media commerce, LSTM algorithm


During the world-changing year of covid 19, social media commerce grew fast. The prolonged use of social media encourages users to make online purchases via social media. TikTok, the most downloaded social media app, offers its users a social media commerce experience, TikTok Shop. The TikTok shop provided a new option for business expansion. Business owners may optimize the potential use of TikTok shops by learning more about TikTok Shop. The purpose of this study is to use sentiment analysis to evaluate the business potential of TikTok Shop. The data from Google Play reviews is analysed using the LSTM algorithm. Based on the results of research conducted using a confusion matrix, the LSTM algorithm method using word2vec has an accuracy of 74%. This study found that the business prospects of TikTok shops may be challenging.


S. Sharma and A. Jain, "Role of sentiment analysis in social media security and analytics," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, no. 5. Wiley-Blackwell, Sep. 01, 2020. doi: 10.1002/widm.1366.

J. Koch, B. Frommeyer, and G. Schewe, "Online shopping motives during the COVID-19 pandemic—lessons from the crisis," Sustainability (Switzerland), vol. 12, no. 24, pp. 1–20, Dec. 2020, doi: 10.3390/su122410247.

H. Xu, "Association for Information Systems AIS Electronic Library (AISeL) Benefits and Concerns of Using Social Media-Users' Perspective." [Online]. Available:

X. Wang, H. Wang, and C. Zhang, "A Literature Review of Social Commerce Research from a Systems Thinking Perspective," Systems, vol. 10, no. 3. MDPI, Jun. 01, 2022. doi: 10.3390/systems10030056.

C. Valerio, L. William, and Q. Noémier, "The Impact of Social Media on E-Commerce Decision Making Process," International Journal of Technology for Business, vol. 1, no. 1, pp. 1–9, 2019, doi: 10.5281/zenodo.2591569.

A. H. Busalim and A. R. C. Hussin, "Understanding social commerce: A systematic literature review and directions for further research," International Journal of Information Management, vol. 36, no. 6. Elsevier Ltd, pp. 1075–1088, Dec. 01, 2016. doi: 10.1016/j.ijinfomgt.2016.06.005.

C. Montag, H. Yang, and J. D. Elhai, "On the Psychology of TikTok Use: A First Glimpse From Empirical Findings," Frontiers in Public Health, vol. 9. Frontiers Media S.A., Mar. 16, 2021. doi: 10.3389/fpubh.2021.641673.

L. Sun, H. Zhang, S. Zhang, and J. Luo, "Content-based Analysis of the Cultural Differences between TikTok and Douyin," in Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 4779–4786. doi: 10.1109/BigData50022.2020.9378032.

L. Xu, X. Yan, and Z. Zhang, "Research on the Causes of the 'Tik Tok' App Becoming Popular and the Existing Problems," Journal of Advanced Management Science, pp. 59–63, 2019, doi: 10.18178/joams.7.2.59-63.

S. Liao, R. Widowati, and C.-Y. Lee, "Data mining analytics investigation on TikTok users' behaviors: social media app development," Library Hi Tech, Oct. 2022, doi: 10.1108/LHT-08-2022-0368.

Y. Qin, B. Omar, and A. Musetti, "The addiction behavior of short-form video app TikTok: The information quality and system quality perspective," Front Psychol, vol. 13, Sep. 2022, doi: 10.3389/fpsyg.2022.932805.

P. Starkey, "'TikTok Made Me Try It': Social Media's New Role in Marketing' TikTok Made Me Try It': Social Media's New Role in Marketing Strategies and Its Effect on Consumer Behavior Strategies and Its Effect on Consumer Behavior." [Online]. Available:

R. W. Pratiwi, S. F. H, D. Dairoh, D. I. Af'idah, Q. R. A, and A. G. F, "Analisis Sentimen Pada Review Skincare Female Daily Menggunakan Metode Support Vector Machine (SVM)," Journal of Informatics, Information System, Software Engineering and Applications (INISTA), vol. 4, no. 1, pp. 40–46, Dec. 2021, doi: 10.20895/inista.v4i1.387.

D. D. Nur Cahyo et al., “Sentiment Analysis for IMDb Movie Review Using Support Vector Machine (SVM) Method,” Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 8, no. 2, pp. 90–95, Mar. 2023, doi: 10.25139/inform.v8i2.5700.

Muhammad Ilham Fadila, Hanafi, and Anggit Dwi Hartanto, "Sentiment Analysis of the Indonesian National Team in the 2020 AFF Cup Using Naïve Bayes and K-Nearest Neighbor Algorithms," Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 8, no. 1, pp. 1–6, Jan. 2023, doi: 10.25139/inform.v8i1.5222.

H. Utami, “Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network,” Indonesian Journal of Applied Statistics, vol. 5, no. 1, p. 31, May 2022, doi: 10.13057/ijas.v5i1.56825.

W. Widayat, “Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 3, p. 1018, Jul. 2021, doi: 10.30865/mib.v5i3.3111.

M. Ihsan, Benny Sukma Negara, and Surya Agustian, “LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter,” Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, vol. 13, no. 1, pp. 79–89, May 2022, doi: 10.31849/digitalzone.v13i1.9950.

J. Nurvania and K. Muslim Lhaksamana, “Analisis Sentimen Pada Ulasan di TripAdvisor Menggunakan Metode Long Short-Term Memory (LSTM).”

M. NEJJARI and A. MEZIANE, "SAHAR-LSTM: An enhanced Model for Sentiment Analysis of Hotels'Arabic Reviews based on LSTM," in 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), IEEE, Nov. 2020, pp. 1–7. doi: 10.1109/CloudTech49835.2020.9365921.

How to Cite
Tri Yunanda, C. M., Hanafi, M., & Pradnya Dhuhita, W. M. (2023). Sentiment Analysis on TikTok Shop Reviews Using Long Short-Term Memory Method to Find Business Opportunity. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 9(1), 1-7.