Deep Learning for Multi-Aspect Sentiment Analysis of TikTok App using the RNN-LSTM Method

  • Diki Wahyudi * Mail Telkom University, Bandung, Indonesia
  • Yuliant Sibaroni Telkom University, Bandung, Indonesia
  • (*) Corresponding Author
Keywords: Sentiment Analysis; Deep Learning; LSTM; Multi-aspect; Word Embedding


Applications built expressly for consumers to communicate online are known as social media apps. Social media applications are utilized for enjoyment as well as for interacting. For Android users, applications may be found in the Google Play Store, while for iOS users, they can be found in the Apple App Store. The site offers a collection that is a big resource-rich in thoughts, opinions, and feelings, notably on Google Playstore. Each user's review has an aspect value. Due to a large number of reviews, sentiment analysis is tough. The author proposes to do an Aspect-Based Sentiment Analysis (ABSA) utilizing TikTok app reviews on the Google Play Store in this paper. Currently, there are 65.2 million active users of the Tik Tok program, including 8.5 million users from Indonesia, there are still a few studies that use the TikTok application dataset. In this study, sentiment classification is carried out on each aspect that has been determined, namely, aspects of features, business, and content, the method used is deep learning Recurrent Neural Network with the Long Short-Term Memory (RNN – LSTM) model and the addition of word embedding BERT. The results showed that the classification of sentiment in the business aspect showed the highest score, namely 0.94, the sentiment classification in the aspect received an accuracy of 0.91 while the feature aspect got the lowest accuracy, which was 0.85.


Download data is not yet available.


M. Arkansyah, D. Prasetyo, and N. Ratna Amina, “Utilization of Tik Tok Social Media as A Media for Promotion of Hidden Paradise Tourism in Indonesia.” [Online]. Available:

B. Xing et al., “Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis,” May 2019, [Online]. Available:

S. Cahyaningtyas, D. Hatta Fudholi, and A. Fathan Hidayatullah, “Deep Learning for Aspect-Based Sentiment Analysis on Indonesian Hotels Reviews,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Aug. 2021, doi: 10.22219/kinetik.v6i3.1300.

D. A. K. Khotimah and R. Sarno, “Sentiment analysis of hotel aspect using probabilistic latent semantic analysis, word embedding and LSTM,” International Journal of Intelligent Engineering and Systems, vol. 12, no. 4, pp. 275–290, 2019, doi: 10.22266/ijies2019.0831.26.

E. I. Setiawan, F. Ferry, J. Santoso, S. Sumpeno, K. Fujisawa, and M. H. Purnomo, “Bidirectional GRU for targeted aspect-based sentiment analysis based on character-enhanced token-embedding and multi-level attention,” International Journal of Intelligent Engineering and Systems, vol. 13, no. 5, pp. 392–407, Oct. 2020, doi: 10.22266/ijies2020.1031.35.

M. Sivakumar and S. R. Uyyala, “Aspect-based sentiment analysis of mobile phone reviews using LSTM and fuzzy logic,” International Journal of Data Science and Analytics, vol. 12, no. 4, pp. 355–367, Oct. 2021, doi: 10.1007/s41060-021-00277-x.

J. Wang et al., “Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks,” 2018.

H. H. Do, P. W. C. Prasad, A. Maag, and A. Alsadoon, “Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review,” Expert Systems with Applications, vol. 118. Elsevier Ltd, pp. 272–299, Mar. 15, 2019. doi: 10.1016/j.eswa.2018.10.003.

H. Jangid, S. Singhal, R. R. Shah, and R. Zimmermann, “Aspect-Based Financial Sentiment Analysis using Deep Learning,” 2018, pp. 1961–1966. doi: 10.1145/3184558.3191827.

S. Li and Q. Wang, “A hybrid approach to recognize generic sections in scholarly documents,” International Journal on Document Analysis and Recognition, vol. 24, no. 4, pp. 339–348, Dec. 2021, doi: 10.1007/s10032-021-00381-5.

F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” Nov. 2020, [Online]. Available:

I. Om Prabha and G. U. Srikanth, “Survey of Sentiment Analysis Using Deep Learning Techniques.”

W. Wang, S. J. Pan, D. Dahlmeier, and X. Xiao, “Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis,” Mar. 2016, [Online]. Available:

L. Zhao and A. Zhao, “Sentiment analysis based requirement evolution prediction,” Future Internet, vol. 11, no. 2, 2019, doi: 10.3390/fi11020052.

Y. Wang, M. Huang, L. Zhao, and X. Zhu, “Attention-based LSTM for Aspect-level Sentiment Classification.”

Y. Wang, M. Huang, A. Sun, and X. Zhu, “Aspect-level sentiment analysis using AS-capsules,” in The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, May 2019, pp. 2033–2044. doi: 10.1145/3308558.3313750.

L. Bao, P. Lambert, and T. Badia, “Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis.”

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Deep Learning for Multi-Aspect Sentiment Analysis of TikTok App using the RNN-LSTM Method

Dimensions Badge
Article History
Submitted: 2022-06-10
Published: 2022-06-30
Abstract View: 1127 times
PDF Download: 849 times
How to Cite
Wahyudi, D., & Sibaroni, Y. (2022). Deep Learning for Multi-Aspect Sentiment Analysis of TikTok App using the RNN-LSTM Method. Building of Informatics, Technology and Science (BITS), 4(1), 169−177.

Most read articles by the same author(s)

1 2 > >>