Performance Comparison Of BERT Metrics and Classical Machine Learning Models (SVM,Naive Bayes) for Sentiment Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AEMRH5JJA" target="_blank" >RIV/00216208:11320/26:EMRH5JJA - isvavai.cz</a>

Result on the web

<a href="https://jurnal.polbeng.ac.id/index.php/ISI/article/view/505" target="_blank" >https://jurnal.polbeng.ac.id/index.php/ISI/article/view/505</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.35314/wmh3rg23" target="_blank" >10.35314/wmh3rg23</a>

Result language

angličtina

Original language name

Performance Comparison Of BERT Metrics and Classical Machine Learning Models (SVM,Naive Bayes) for Sentiment Analysis

Original language description

Sentiment analysis is one of the important methods in understanding public opinion from large amounts of text, such as product reviews or user comments. Many studies have shown that the BERT (BiDirectional Encoder Representations from Transformers) model has advantages over classical machine learning models such as Support Vector Machine (SVM) and Naïve Bayes. However, there are still few studies that systematically compare the performance of the two on datasets from various topics and languages, especially those with imbalanced label distributions. This study compares four BERT variants (bert-base-uncased, distilbert-base-uncased, indobert-base-uncased, and distilbert-base-indonesian) with two classical models using three datasets of IMDb 50K (English), Amazon Food Reviews (English), and Gojek App Review (Indonesian). The classical model uses the TF-IDF vectorisation method, while the BERT model is optimised through a further training process (fine-tuning) with a layer freezing technique. The evaluation is carried out using accuracy, precision, recall, and F1-score. The results show that the BERT model excels on English data, while on imbalanced Indonesian data, SVM and Naïve Bayes produce higher F1-score results. These findings indicate that the selection of the right model must be adjusted to the characteristics of the data used.

Czech name

—

Czech description

—

Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

—

Publication year

2025

Confidentiality

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

Name of the periodical

INOVTEK Polbeng - Seri Informatika

ISSN

2527-9866

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

741-752

UT code for WoS article

—

EID of the result in the Scopus database

—