Implementation of BiLSTM and IndoBERT for Sentiment Analysis of TikTok Reviews

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A4KVJ4WXF" target="_blank" >RIV/00216208:11320/26:4KVJ4WXF - isvavai.cz</a>

Result on the web

<a href="https://jurnal.stkippgritulungagung.ac.id/index.php/jipi/article/view/5815" target="_blank" >https://jurnal.stkippgritulungagung.ac.id/index.php/jipi/article/view/5815</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.29100/jipi.v10i1.5815" target="_blank" >10.29100/jipi.v10i1.5815</a>

Result language

angličtina

Original language name

Implementation of BiLSTM and IndoBERT for Sentiment Analysis of TikTok Reviews

Original language description

The significant increase in users on TikTok has led to a notable rise in the number of reviews in the form of opinions given to the application. The large number of opinions can be analyzed to identify the prevailing sentiment among the community towards the application. The sentiment analysis method employing machine learning is particularly well-suited to this problem due to its practicality and efficiency. The objective of this research is to develop a model that can be utilized as a sentiment analysis tool with a high degree of accuracy. In this research, the BiLSTM algorithm, combined with IndoBERT, a pre-trained model, is employed. The BiLSTM can comprehend the interrelationships between words within a sentence in a bidirectional manner. IndoBERT is pertinent to this research as it is a model that has been fine-tuned using Indonesian language datasets from various sources on the Internet. To support this research, a scenario was created by considering various aspects when adding methods as an optimization scheme until the optimal model was identified. The outcomes of experimentation demonstrate that sentiment analysis using the BiLSTM+IndoBERT method achieved the highest accuracy, reaching 81% in the classification report test and an average accuracy of 92.03% in cross-validation testing with a total of 10 folds.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)

ISSN

2540-8984

e-ISSN

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Volume of the periodical

10

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

96-106

UT code for WoS article

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EID of the result in the Scopus database

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