Sentiment Analysis on Tiktok Application Reviews Using Natural Language Processing Approach

Abdul Majid(1), Dian Nugraha(2), Faisal Dharma Adhinata(3*),

(1) Universitas Global Jakarta
(2) Universitas Global Jakarta
(3) Institut Teknologi Telkom Purwokerto
(*) Corresponding Author



Technology today is very developed, there are so many media that can be used to communicate, these media are very easy to use by connecting to the internet network. Research on the sentiment of this analysis can still be relatively small and new. The rapid development of technology today makes it very easy for humans to communicate with one of the modern technologies, namely smartphones. The initial stage of this research begins with the review to be analyzed, then continues with the collection of review data.  Conducted on reviews that have been collected with and without an NLP approach resulting in 2 datasets, with an NLP approach and datasets without an NLP approach. The first step is to identify the problem with the research object. It then looked for related literature studies from both journals and review proceedings used as many as 1000 reviews, which have been labeled by 5 correspondents and resulted in positive reviews and negative reviews. The review is used as a dataset, then pre-processed with an NLP approach.  Classification using the NLP approach got an accuracy of 76.92%, a precision of 80.00% and a recall of 74.07%, while without NLP it only got an accuracy of 69.23%, a precision of 80.00% and a recall of 64.52% At the    preprocessing stage, the stemming feature, and stopword removal features were applied to each review. Word normalizer to handle variations in writing words that have the same meaning to be counted as a single term Furthermore, a stopword removal process is carried out to remove the stopword from the review.


NLP approach; pre-processing; Scrapping

Full Text:



E. Adiwaluyo, "Research: Shopee Becomes the Number One e-Commerce Platform in Indonesia," Marketeers, December 2, 2019. [Online].Available:

Mounika, A. and Saraswathi, D. S. (2019) Classification Of Book Reviews Based On Statement Analysis: A Survey. India

Ratri, H. D. (2018) 'The Relationship between Social Media Use and Adolescent Self-Esteem Levels in SMA Negeri 2 Jember'.

Liu, B. ( 2012) 'Sentiment analysis and opinion mining', Synthesis lectures on human language technologies, 5(1), pp. 1–167.

Hs, F. G. S. H. H. (2012) 'Analysis of the application of 4G LTE network technology in Indonesia', UNIKOM scientific magazine.

Vinodhini, G. and Chandrasekaran, R. M. (2012) 'Sentiment analysis and opinion mining: a survey', International Journal, 2(6), pp. 282–292.

O. Bharti, and M. Malhotra, "Sentiment analysis," International Journal of Computer Science and Mobile Computing, vol. 5, pp. 625-633, 2016.

N. H. Surve, "Sentiment analysis using natural language processing (NLP)," International Research Journal of Engineering and Technology, vol. 6, pp. 1240-1244, 2019.

R. Arief, and K. Imanuel, "Sentiment analysis of viral topics of dancer villages on social media twitter with lexicon based method," Journal of Mathematical Sciences, vol. 21, pp. 242-250, 2019.

A. Jurek, M. D. Mulvenna, and Y. Bi, "Improved lexicon-based sentiment analysis for social media analytics," Security Informatics, vol. 4, pp. 1-13, 2015.

M. Mhatre, D. Phondekar, P. Kadam, A. Chawathe, and K. Ghag, "Dimensionality reduction for sentiment analysis using pre-processing techniques," in 2017 International Conference on Computing Methodologies and Communication (ICCMC), 2017, pp. 16–21, doi: 10.1109/ICCMC.2017.8282676.

L. A. Mullen, K. Benoit, O. Keyes, D. Selivanov, and J. Arnold, "Fast, Consistent Tokenization of Natural Language Text," J. Open Source Softw., vol. 3, no. 23, p. 655, 2018, doi: 10.21105/joss.00655.

B. P. Pande and H. S. Dhami, "Application of Natural Language Processing Tools in Stemming," Int. J. Comput. Appl., vol. 27, no. 6, pp. 14–19, 2011, doi: 10.5120/3302-4530.

A. K. Putra, R. D. Nyoto, and P. H. Sasty, "Design and Build a Web-Based Private Tutoring Service Provider Marketplace Application in Pontianak City," J. Sist. and Teknol. Inf., vol. 5, no. 1, pp. 22–25, 2017

A. Prakash and U. Kumar, "International Journal of Computer Sciences and Engineering Open Access Authentication Protocols and Techniques : A Survey," no. March,2019,doi:10.26438/ijcse/v6i6.10141020.

R. Melita, V. Amrizal, H. Suseno, and T. Dirjam, "Application of the Term Frequency Inverse Document Frequency (Tf-Idf) Method and Cosine Similarity to the Information Retrieval System to Know Web-Based Hadith Sharah (Case Study: Hadith Shahih Bukhari-Muslim)," J. Tek. Inform., vol. 11, no. 2, pp. 149–164,Nov.2018,doi:10.15408/jti.v11i2.8623.

K. Norman, Z. Li, Y. T. Oh, G. Golwala, S. Sundaram, and J. Allebach, "Application of natural language processing to an online fashion marketplace," IS T Int. Symp. Electron.ImagingSci.Technol.,pp.1–5, 2018,doi:10.2352/ISSN.2470-1173.2018.10.IMAWM-444.

A. Tharwat, "Classification Assessment Methods," Appl. Comput. Informatics, no. January,2018, doi: 10.1016/j.aci.2018.08.003.

J. Asian, H. E. Williams, and S. M. M. Tahaghoghi, "Stemming Indonesian," Conf. Res. Pract. Inf. Technol. Ser., vol. 38, no. January, pp. 307–314, 2005, doi: 10.1145/1316457.1316459

V. Jahjah, R. Khoury, and L. Lamontagne, Word Normalization Using Phonetic Signatures. 2016.

Article Metrics

Abstract view : 636 times | PDF view : 136 times


  • There are currently no refbacks.

Indexed by:


ROAD: the Directory of Open Access scholarly Resources



Creative Commons License

Journal of Embedded Systems, Security and Intelligent Systems (JESSI) is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License